Patent Publication Number: US-2021173843-A1

Title: Systems, Methods, and Media for Identification, Disambiguation, Verification and for Communicating Knowledge

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     The present application claims the priority benefit of U.S. Provisional Patent Application Ser. No. 62/944,641 filed on Dec. 6, 2019 titled, “Systems, Methods, and Media for Identification, Disambiguation, and Verification,” and the present application claims the priority benefit of U.S. Provisional patent Application Ser. No. 63/031,419 filed on May 28, 2020 titled, Systems, Methods and Media for Communicating Knowledge,” all of which are hereby incorporated by reference in their entireties 
    
    
     SUMMARY 
     The present disclosure specifies a Context Identifier which enables users to define and utilize an identifier for a data set that is external to the data storage system. In another embodiment, a Context Identifier can be used for user identification, disambiguation, and verification. 
     The present disclosure specifies a Emoji Mark which enables users to visually, audibly, or tactilely identify and disambiguate a fact, subject, object, individual, entity, or event. In another embodiment, a Emoji Mark can be themed, branded, and used to convey affiliation, sentiment, and language. 
     The present disclosure provides a specification which makes it possible for all users to use their real first and last name as their username. It enables disambiguation for users who have the same first and last name and provides a unique and easily recognizable visual, audio, or tactile identification for a specific user. The present disclosure provides a specification which makes it possible for users to search for one another by Emoji Mark. 
     The present disclosure provides an Emoji Mark specification derived from the 4Emoji Mark with the improvement of utilizing a dynamic set of emoji where capacity is automatically increased or decreased as required. The exemplary enablement can be utilized with less or more permutations and combinations as required. 
     Examples of Emoji Mark utilization include part numbers, serial numbers, model numbers, and SKUs. More examples of Emoji Mark utilization include Emoji Mark for stickers or labels for business cards, windows, products, etc. The disclosure of optical emoji recognition enables Emoji Mark to provide information about the subject of Emoji Mark as well as opportunities for engagement. 
     In another embodiment of the Emoji Mark, the present disclosure specifies utilizing Emoji Mark for the local-part of an email address in whole or in part, enabling users to use their real name while being disambiguated from other users with the same name for a specific email domain. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows an exemplary Directory Database. 
         FIG. 2  shows an exemplary Context Identifier Algorithm. 
         FIG. 3  shows an exemplary Emoji &amp; 4Emoji Mark. 
         FIG. 4  shows an exemplary Create 4Emoji Mark Algorithm. 
         FIG. 5  shows an exemplary 4Emoji Signature Context Enumeration. 
         FIG. 6  shows an exemplary 4Emoji Signature Question Set Enumeration. 
         FIG. 7  shows an exemplary 4Emoji Signature Question Set Alt Enumeration. 
         FIG. 8  shows an exemplary 4Emoji Signature. 
         FIG. 9  shows an exemplary 4Emoji Signature Question Type. 
         FIG. 10  shows an exemplary 4Emoji Signature Question. 
         FIG. 11  shows an exemplary 4Emoji Signature Question Set. 
         FIG. 12  shows an exemplary 4Emoji Signature Algorithm Context. 
         FIG. 13  shows an exemplary 4Emoji Signature Algorithm Manager. 
         FIG. 14  shows an exemplary Create 4Emoji Signature Context Value Algorithm. 
         FIG. 15  shows an exemplary Create 4Emoji Signature Question Set Value Algorithm. 
         FIG. 16  shows an exemplary Create 4Emoji Signature Algorithm. 
         FIG. 17  shows an exemplary Create 4Emoji Signature Proof Algorithm. 
         FIG. 18  shows an exemplary 4Emoji Signature Context Value Proof Algorithm. 
         FIG. 19  shows an exemplary 4Emoji Signature Question Set Value Proof Algorithm. 
         FIG. 20  shows an exemplary 4Emoji Signature Verification Algorithm. 
         FIG. 21  shows an exemplary 4Emoji Signature Proof Verification Algorithm. 
         FIG. 22  shows an exemplary 4Emoji Signature Credit Report Example. 
         FIG. 23  shows an exemplary 4Emoji Signature Purchase Example. 
         FIG. 24  shows an exemplary Create 4Emoji Mark Username Algorithm. 
         FIG. 25  shows an exemplary 4Emoji Mark Username Search Algorithm. 
         FIG. 26  shows an exemplary Lookup Apps and Systems. 
         FIG. 27  shows exemplary Context Item &amp; Items. 
         FIG. 28  shows an exemplary Context Expression. 
         FIG. 29  shows an exemplary Context Expression Module Manager. 
         FIG. 30  shows exemplary Context Expression Elements. 
         FIG. 31  shows exemplary Context Expressions. 
         FIG. 32  shows exemplary Context Expressions. 
         FIG. 33  shows exemplary Context Expressions. 
         FIG. 34  shows an exemplary Pathway Enumeration. 
         FIG. 35  shows an exemplary Content Enumeration. 
         FIG. 36  shows an exemplary Content. 
         FIG. 37  shows an exemplary Taxonomy Enumeration. 
         FIG. 38  shows an exemplary Pathway Base. 
         FIG. 39  shows an exemplary Pathway. 
         FIG. 40  shows an exemplary Pathway Module Manager. 
         FIG. 41  shows an exemplary Pathway Index Algorithm. 
         FIG. 42  shows an exemplary Pathway Character Incremental Index Algorithm. 
         FIG. 43  shows an exemplary Pathway Word Incremental Index Algorithm. 
         FIG. 44  shows an exemplary Pathway Word-Joined Incremental Index Algorithm. 
         FIG. 45  shows an exemplary Pathway Word-Joined Character Incremental Index Algorithm. 
         FIG. 46  shows an exemplary Lookup Taxonomy. 
         FIG. 47  shows an exemplary Lookup Header. 
         FIG. 48  shows an exemplary Lookup. 
         FIG. 49  shows an exemplary Lookup Module Manager. 
         FIG. 50  shows an exemplary Lookups. 
         FIG. 51  shows exemplary Lookup Systems and Modules. 
         FIG. 52  shows an exemplary Lookup Domain Name. 
         FIG. 53  shows an exemplary Create Lookup Domain Name Algorithm. 
         FIG. 54  shows an exemplary Lookup Domain Name Registration. 
         FIG. 55  shows an exemplary Browser Module Manager. 
         FIG. 56  shows an exemplary Lookup Browser. 
         FIG. 57  shows an exemplary Business Listing Placard. 
         FIG. 58  shows an exemplary Business Listing Summary Placard. 
         FIG. 59  shows exemplary Business Placards. 
         FIG. 60  shows exemplary Business Placards. 
         FIG. 61  shows an exemplary Business Lookups. 
         FIG. 62  shows an exemplary Account, Identity, and Lookup Systems. 
     
    
    
     DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS 
       FIG. 1  illustrates Directory Database  100  which is a relational database design. Directory Database  100  is comprised of Person Table  102  and Address Table  120 . Person Table  102  contains PersonId (PK)  110 , FirstName (IX_FirstName)  112 , and LastName (IX_LastName)  114 . Address Table  120  contains AddressId (PK)  122 , PersonId (FK)  124 , AddressLine1  128 , AddressLine2  130 , City (IX_City)  132 , State (IX_State)  134 , Zip  136 , and Year  138 . PersonId (PK)  110  is connected to PersonId (FK)  124  via Line Segment  126 . Those skilled in the art will appreciated that the abbreviation PK represents a primary key, the abbreviation IX represents an index, and the abbreviation FK represents a foreign key. 
     Element  140  is comprised of a SELECT standard query language (SQL) statement: SELECT p.FirstName, p.LastName, a.City, a.State FROM Person p JOIN Address a ON a.PersonId=p.PersonId WHERE p.FirstName=‘William’ AND p.LastName=‘Anderson’ AND a.City=‘Boise’ AND a.State=‘ID’ AND a.Year=‘2019’. 
     Directory Database  100  illustrates a common approach to data storage and query which has been established for decades. It also illustrates, in some ways, the skillsets needed to Identify the data attributes in tables or across tables so that queries can be performed, usually by a database professional such as a database administrator (DBA) or a software engineer. 
     Typically, a DBA or software engineer would go through a specific domain of information stored in tables and identify attributes so that queries can be performed to fulfill specific use cases. So, while this structure may be used by various businesses, each business may have very specific use cases for their specific business needs and therefore may identify differing business-specific attributes in the data. As mentioned above, this would require someone of advanced technical skill to make an index that would optimize a search or searches for any given desired result. 
     Typically, the foreign key and the primary key are used to locate the data for queries, but upon removing the querying aspect to find specific data attributes that match specific data-attribute constraints, an index and foreign-key relationship would still be essential in a relational database to successfully locate specific records. 
     The same uses of primary keys, foreign keys, and index keys are not only applied by relational database management systems, but those skilled in the art will appreciate that these same keys and indexes are often utilized in document storage, object storage, and other data storage systems. 
     The present disclosure establishes an algorithm to enable the abstraction of indexes from data storage systems. It provides the functionality to give subject matter experts the ability to define indexes and apply them to the underlying data without having a specialized skill or access to administrative and development tools required to work with data storage systems. 
       FIG. 2  illustrates Context Identifier Algorithm  200 . The algorithm starts with Initial  202  which is connected to Input Key-Value Pairs (KVP)  204 . As an example, the algorithm could take a dictionary of one or more key-value pairs of strings, each consisting of a string key and a string value. Input Key-Value Pairs (KVP)  204  is connected to Initialize Variable, [i=0]  206 . Initialize Variable, [i=0]  206  is connected to Decision  208 . 
     Decision  208  is connected to the note [i&lt;KVP Count]  210 , which illustrates the looping criteria. While i&lt;KVP Count, control proceeds to Case Folding and Normalization  212 , then Increment Variable, [i++]  214 , then back to Decision  208  via Line Segment  216  until i=KVP Count. This illustrates that each key-value pair goes through Case Folding and Normalization  212 . When i=KVP Count, control proceeds to Sort Key-Value Pairs by Key  220  via Line Segment  218 . 
     Case Folding and Normalization  212  is connected to Culture and Case Settings  213 . Culture and Case Settings  213  illustrates that case folding and normalization can be configured for sorting and hashing requirements. Sort Key-Value Pairs by Key  220  is connected to Sort Settings  222 . Sort Settings  222  illustrates that sort order can be configured for sort order requirements of key-value pairs. Sort Key-Value Pairs by Key  220  is also connected to Calculate Merkle Root of KVP  224  which is connected to Convert Merkle Root to Hexadecimal  226 . Convert Merkle Root to Hexadecimal  226  produces Context Identifier  228 . Convert Merkle Root to Hexadecimal  226  is connected to Final  230 , illustrating the end of the algorithm. 
     In one embodiment, both the key and the value for each key-value pair undergoes Case Folding and Normalization  212 . In another embodiment, only the value string of each key-value pair undergoes Case Folding and Normalization  212 . In another embodiment, only the value string of each key-value pair undergoes Case Folding and Normalization  212 , the value string is then hashed, and the resulting hash is converted to hexadecimal and used to replace the key in the key-value pair. 
     In one embodiment, the default value of Culture and Case Settings  213  is culture invariant and case insensitive. In another embodiment, other cultures and case sensitivity could be set. Those skilled in the art will appreciate that Culture and Case Settings  213  will impact hashing and sorting. 
     In one embodiment, the default value of Sort Settings  222  is ascending. In another embodiment, descending or none could be set. Those skilled in the art will appreciate that sorting will impact the Merkle Tree calculation. 
     Context Identifier Algorithm  200  is of benefit because it prepares key-value pairs of data and performs a Merkle Tree calculation with consistent results. One intention of Context Identifier Algorithm  200  is to create an identifier from contextual information about a specific data set. To illustrate, consider the following example where the data set is a person&#39;s contact information. The person&#39;s first name, last name, city, and state are passed into the algorithm. Each of these contextual values has associated keys such as first name, last name, city, and state. Context Identifier Algorithm  200  will produce a consistent identifier to uniquely represent the contact information. 
     In one embodiment, Context Identifier Algorithm  200  enables users to create a Context Identifier  228  based on context of a data set (e.g. database row or JSON object) that is equivalent to an index found in a data storage system. Those skilled in the art will appreciate that Context Identifier  228  would be required to be included in the data set (e.g. column or property) and that the data storage system may apply an index for Context Identifier  228 . 
     This embodiment enables users to define and utilize Context Identifier  228  from their perspective (and individual use cases of the data set) without that knowledge being built into and maintained in the data storage system. Just as easily as users can create Context Identifier  228 , they can make meaningful changes to Context Identifier  228  that are implemented as soon as the data is committed. 
     An index based on Context Identifier  228  in a data storage system would return only data sets that match the contextual data that was used to derive its composition. This approach has the benefit of reducing or eliminating costly index creation and management for the data set in the data storage system. It also reduces or eliminates costly cross indexing between databases, tables, documents, nodes, and edges to support query joins and/or linking. 
     Moreover, those skilled in the art will appreciate that Context Identifier  228  enables users to support more use cases for a single implementation of a data set in a data storage system since there is marginal decrease in efficiency as more and more indexes are added to a single implementation (the basic premise being that more indexes result in more processing and storage resource overhead to support added indexes). In another embodiment, Context Identifier  228  can be used in systems, methods and media for user identification, disambiguation, and verification. 
       FIG. 3  illustrates Emoji and 4Emoji Mark  300 . Emoji  302  is comprised of Emoji Id  304 , Emoji Name  306 , Emoji Description  308 , Emoji Code  310 , Emoji Context Id  312 , Emoji Byte Array  314 , and Emoji Byte Array Hash  316 . 4Emoji Mark  340  is comprised of First Emoji  342 , Second Emoji  344 , Third Emoji  346 , Fourth Emoji  348 , Emoji Mark Id  350 , Emoji Mark Theme Id  352 , Emoji Mark Brand Id  354 , Emoji Mark Affiliation Id  356 , Emoji Mark Sentiment Id  358 , and Emoji Mark Language Id  360 . 
     Emoji Id  304  is the unique identifier for Emoji  302 . Emoji Name  306  is the name for Emoji  302 . Emoji Description  308  is the description for Emoji  302 . Emoji Code  310  is the code that associates a graphic, sound, or tactile pattern with Emoji  302 . Emoji Context Id  312  is the Context Identifier  228  in  FIG. 2  for Emoji  302 . Emoji Byte Array  314  may contain the Emoji graphic, sound or tactile pattern for Emoji  302 . Emoji Byte Array Hash  316  is the hash of Emoji Byte Array  314  for Emoji  302 . 
     First Emoji  342  is the first Emoji  302  for 4Emoji Mark  340 . Second Emoji  344  is the second Emoji  302  for 4Emoji Mark  340 . Third Emoji  346  is the third Emoji  302  for 4Emoji Mark  340 . Fourth Emoji  348  is the fourth Emoji  302  for 4Emoji Mark  340 . Emoji Mark Id  350  is the unique identifier for 4Emoji Mark  340 . It is comprised of Emoji Id  304  of each Emoji  302  in 4Emoji Mark  340  passed in as key-value pairs into Context Identifier Algorithm  200  in  FIG. 2 . 
     Emoji Mark Theme Id  352  is the identifier of the specific theme applied to 4Emoji Mark  340 . Emoji Mark Brand Id  354  is the identifier of the specific brand applied to 4Emoji Mark  340 . Emoji Mark Affiliation Id  356  is the identifier of the specific affiliation represented by 4Emoji Mark  340 . Emoji Mark Sentiment Id  358  is the identifier of the specific sentiment conveyed by 4Emoji Mark  340 . Emoji Mark Language Id  360  is the identifier of the specific language conveyed by 4Emoji Mark  340 . 
     Emoji  302  encapsulates the commonly accepted definition of emoji (a small digital image or icon used to express an idea or emotion) and could include but is not limited to symbols, icons, pictograms, logos, brands, gestures, phonetics, sounds, punctuation, typography, letters, and numbers. 
     4Emoji Mark  340  provides the basis of an identifier that is visual (or may be audio or tactile) and is readily understood as a pattern for attribution. Furthermore, as the present disclosure makes clear, 4Emoji Mark  340  provides the basis for disambiguation when used as an attribute of an item within a collection of similar items. 
     The present disclosure generates 4Emoji Mark  340  utilizing combination mathematics where “a k-combination of a set S is a subset of k distinct elements of S.” 1  Those skilled in the art will appreciate 4Emoji Mark  340  is a subset of k distinct elements of a set of Emoji  302 . As an example, there are 416,416,712,497,500 4Emoji Mark  340  combinations in a set of 10,000 unique Emoji  302 . In a set of 100,000 unique Emoji  302 , there are 4,166,416,671,249,975,000 4Emoji Mark  340  combinations.  1 Source: Combination—en.wikipedia.org/Combination 
     In another embodiment, 4Emoji Mark  340  generation utilizes permutation mathematics. As an example, there are 9,994,001,099,940,000 4Emoji Mark permutations in a set of 10,000 unique Emoji  302 . 
     In one embodiment, users can visually identify and disambiguate a fact, subject, object, individual, entity, or event using 4Emoji Mark  340 . In another embodiment, 4Emoji Mark  340  can be used to audibly identify and disambiguate a fact, subject, object, individual, entity, or event. In another embodiment, 4Emoji Mark  340  can be used to tactilely identify and disambiguate a fact, subject, object, individual, entity, or event. 
     In another embodiment, 4Emoji Mark  340  is themed (see Emoji Mark Theme Id  352 ). Users can apply themes such as Christmas, Fourth of July, Outdoors, et cetera to their 4Emoji Mark  340 . Themes may incorporate color and shape variations for each specific Emoji  302  in 4Emoji Mark  340 . 
     In another embodiment, 4Emoji Mark  340  is branded (see Emoji Mark Brand Id  354 ). Users can apply branded themes such as school or professional sports teams, corporate or non-profit imagery, et cetera. 
     In another embodiment, 4Emoji Mark  340  and Emoji  302  convey affiliation (see Emoji Mark Affiliation Id  356 ). Entities can approve users to affiliate with the entity and gain access to imagery embedded in 4Emoji Mark  340  and/or Emoji  302  that conveys the affiliation. 
     In another embodiment, 4Emoji Mark  340  conveys sentiment (see Emoji Mark Sentiment Id  358 ). 4Emjoi Mark  340  and Emoji  302  imagery contains sentiment which can be used as an attribution for a fact, subject, object, individual, entity, or event. Sentiment can be expressed by users to communicate and inform more complex emotional attributions, especially when presented with 4Emoji Mark  340 . Such attribution is correlated with an associated sentiment identifier that can be used to infer the expression of sentiment and ultimately be utilized for measuring, communicating and optimizing sales, marketing and advertisement. 
     In another embodiment, 4Emoji Mark  340  conveys language (see Emoji Mark Language Id  360 ). Emoji  302  and 4Emoji Mark  340  can be utilized to express words, phrases, and concepts, creating the basis for a language. 
       FIG. 4  illustrates Create 4Emoji Mark Algorithm  400 . The algorithm starts at Initial  402  which is connected to Create Emoji Key-Value Pairs (KVP)  404 . Create Emoji Key-Value Pairs (KVP)  404  is connected to note [KVP Capacity=4]  406  and Initialize Variable, [i=0]  408 . Initialize Variable, [i=0]  408  is connected to Decision  410 . Decision  410  is connected to note [i&lt;KVP Count]  412 , which illustrates the looping criteria. While i&lt;KVP Count, control proceeds to Generate Emoji Selection  414 , then Add Emoji Id and Hash to KVP  416 , then Increment Variable, [i++]  418 , then back to Decision  410  via Line Segment  420  until i=KVP Count. When I=KVP Count, control proceeds to Create 4Emoji Mark  424  via Line Segment  422 . 
     Create 4Emoji Mark  424  is connected to Create 4Emoji Mark Id  426 , which is connected to Context Identifier Algorithm  200  and links to Decision  428 . Decision  428  illustrates that the algorithm determines if the 4Emoji Mark Id is available or not. If the 4Emoji Mark Id is not available, control proceeds from Decision  428  to Initialize Variable, [i=0]  408  via Line Segment  430 . In addition to initializing the variable, the algorithm clears the KVP. If the 4Emoji Mark Id is available, control proceeds from Decision  428  to Save 4Emoji Mark and 4Emoji Mark Id  434  via Line Segment  432 . Save 4Emoji Mark and 4Emoji Mark Id  434  is connected to 4Emoji Mark  436 , 4Emoji Data Storage System  438 , and Final  440 , illustrating the end of the algorithm. 
     In one embodiment, Generate Emoji Selection  414  illustrates that an algorithm randomly generates and selects a single Emoji  302  in  FIG. 3 . In another embodiment, Generate Emoji Selection  414  illustrates a user interface is used to generate multiple emoji from which a user selects a single Emoji  302  in  FIG. 3 . 
     Create 4Emoji Mark  424  illustrates the creation of a 4Emoji Mark  340  in  FIG. 3 , specifically 4Emoji Mark  436 . Create 4Emoji Mark Id  426  illustrates the creation of 4Emoji Mark Id  350  in  FIG. 3 , utilizing Context Identifier Algorithm  200 . Save 4Emoji Mark and 4Emoji Mark Id  434  illustrates the saving of 4Emoji Mark  436  to 4Emoji Data Storage System  438 . 
     Those skilled in the art will appreciate 4Emoji Mark  340  and the associated 4Emoji Mark Id  350  being utilized in systems and algorithms for identification and disambiguation. 
       FIG. 5  illustrates 4Emoji Signature Context Enumeration  500  which is comprised of None=0  502 , Risk Level  504 , Version  512 , and Question Sets  518 . Risk Level  504  contains Level One=1  506 , Level Two=2  508 , and Level Three=4  510 . Version  512  contains Version One=8  514  and Version Two=16  516 . Question Sets  518  contains Question Set One=32  520 , Question Set Two=64  522 , and Question Set Three=128  524 . 4Emoji Signature Context Enumeration  500  is connected to 4Emoji Signature Context Value  526 . None=0  502  illustrates a flag value of zero, as an optimization, meaning that no flags are set. 
     Risk Level  504  illustrates that an instance of the enumeration can store three flags to indicate the level of risk. The flags at Risk Level  504  set the context of varying degrees of risk for the instance. For example, the lowest risk level being one and the highest risk level being seven out of the possible bitwise OR operations for Level One=1  506 , Level Two=2  508 , and Level Three=4  510 . 
     Version  512  illustrates the version of an instance of the enumeration, where the bitwise OR operation of Version One=8  514  and Version Two=16  516  represent possible version flags. 
     Question Sets  518  illustrates the question sets of an instance of the enumeration, where the bitwise OR operation of Question Set One=32  520 , Question Set Two=64  522  and Question Set Three=128  524  represent possible question set flags. 4Emoji Signature Context Value  526  represents the integer value of 4Emoji Signature Context Enumeration  500 . 
     Those skilled in the art will appreciate that 4Emoji Signature Context Enumeration  500  is an enumeration type 2  that defines bit flags and enables an instance of the enumeration type to store any combination of the values that are defined in the enumerator list as well as support the bitwise operations of AND, OR, NOT and XOR.  2  Source: Enumeration types (C# Programming Guide)—docs.microsoft.com/en-us/dotnet/csharp/programming-guide/enumeration-types 
     4Emoji Signature Context Enumeration  500  depicts the first 8 power of 2 3  values 0 to 128 in the enumerator list of an 8-bit integer-based enumeration. Those skilled in the art will appreciate that 16-bit, 32-bit, or 64-bit based enumerations would provide 16, 32 or 64 values in their respective enumerator list; therefore, more values could be defined for Risk Level  504 , Version  512 , and Question Sets  518 . As an example, consider a 16-bit based enumeration where Risk Level  504  and Version  512  are defined with the equivalent values of the current 8-bit enumeration illustrated in  FIG. 5 , whereby Question Sets  518  could be defined with power of 2 values 32 through 32,768 in the enumerator list of the 16-bit based enumeration to provide 11 total question sets.  3  Source: Power of 2 Table—www.vaughns-1-pagers.com/computer/powers-of-2.htm 
     Those skilled in the art will also appreciate the bitwise AND operations that can be utilized with an instance of the enumeration to determine what flags are set to access risk level, version, and question sets present in the enumeration list. 
     Risk Level  504  illustrates a risk level that is associated with 4Emoji Signature Context Enumeration  500 . In one embodiment, risk 4  is defined as a function of financial cost and probability of unauthorized transactions for which 4Emoji Signature Context Enumeration  500  is designed to mitigate through Question Sets  518 . In another embodiment, risk is defined as a function of financial cost and probability of unauthorized access to personal information and assets for which 4Emoji Signature Context Enumeration  500  is designed to mitigate through Question Sets  518 .  4  Source: Risk assessment—en.wikipedia.org/wiki/Risk_assessment 
       FIG. 6  illustrates 4Emoji Signature Question Set Enumeration  600  which is comprised of None=0  502 , Risk Level  504 , Version  512 , and Question Set  602 . Risk Level  504  contains Level One=1  506 , Level Two=2  508 , and Level Three=4  510 . Version  512  contains Version One=8  514  and Version Two=16  516 . Question Set  602  contains Question One=32  604 , Question Two=64  606 , and Question Three=128  608 . 4Emoji Signature Question Set Enumeration  600  is connected to 4Emoji Signature Question Set Value  610 . 
     Question Set  602  illustrates the questions of an instance of the enumeration, where the bitwise OR operation of Question One=32  604 , Question Two=64  606  and Question Three=128  608  represent possible question set flags. 4Emoji Signature Set Value  610  represents the integer value of 4Emoji Signature Question Set Enumeration  600 . 
     Those skilled in the art will appreciate that 4Emoji Signature Question Set Enumeration  600  is an enumeration type that defines bit flags and enables an instance of the enumeration type to store any combination of the values that are defined in the enumerator list as well as support the bitwise operations of AND, OR, NOT and XOR. 
     4Emoji Signature Question Set Enumeration  600  depicts the first 8 power of 2 values 0 to 128 in the enumerator list of an 8-bit integer-based enumeration. Those skilled in the art will appreciate that 16-bit, 32-bit, or 64-bit based enumerations would provide 16, 32 or 64 values in their respectively enumerator list; therefore, more values could be defined for Risk Level  504 , Version  512 , and Question Set  602 . As an example, consider a 16-bit based enumeration where Risk Level  504  and Version  512  are defined with the equivalent values of the current 8-bit enumeration illustrated in  FIG. 6 , whereby Question Set  602  could be defined with power of 2 values 32 through 32,768 in the enumerator list of the 16-bit based enumeration to provide 11 total questions in the question set. 
     Those skilled in the art will also appreciate the bitwise AND operations that can be utilized with an instance of the enumeration to determine what flags are set to access risk level, version, and questions present in the enumeration list. 
     Risk Level  504  illustrates a risk level that is associated with 4Emoji Signature Question Set Enumeration  600 . In one embodiment, risk is defined as a function of financial cost and probability of unauthorized transactions for which 4Emoji Signature Question Set Enumeration  600  is designed to mitigate through Question Set  602 . In another embodiment, risk is defined as a function of financial cost and probability of unauthorized access to personal information and assets for which 4Emoji Signature Question Set Enumeration  600  is designed to mitigate through Question Set  602 . 
       FIG. 7  illustrates 4Emoji Signature Question Set Alt Enumeration  700  which is comprised of None=0  502  and Question Set  602 . Question Set  602  contains Question One=1  702 , Question Two=2  704 , Question Three=4  706 , Question Four=8  708 , Question Five=16  710 , Question Six=32  712 , Question Seven=64  714 , Question Eight=128  716 . 4Emoji Signature Question Set Alt Enumeration  700  is connected to 4Emoji Signature Question Set Value  610 . 
     4Emoji Signature Question Set Alt Enumeration  700  is similar to 4Emoji Signature Question Set Enumeration  600 , except that it doesn&#39;t contain risk level or version flags. It illustrates eight questions for Question Set  602 . 
       FIG. 8  illustrates 4Emoji Signature  800  and 4Emoji Signature Proof  810 . 4Emoji Signature  800  is comprised of First Emoji  802 , which contains 4Emoji Signature Context Value  526 ; Second Emoji  804 , which contains 4Emoji Signature Question Set Value  610 ; Third Emoji  806 , which contains 4Emoji Signature Question Set Value  610 ; and Fourth Emoji  808 , which contains 4Emoji Signature Question Set Value  610 . 4Emoji Signature  800  contains four emoji. Each emoji has a corresponding integer that reflects four separate enumeration values. 
     Those skilled in the art will see that First Emoji  802  corresponds with 4Emoji Signature Context Value  526 ; Second Emoji  804  corresponds with 4Emoji Signature Question Set Value  610 ; Third Emoji  806  corresponds with 4Emoji Signature Question Set Value  610 ; and Fourth Emoji  808  corresponds with 4Emoji Signature Question Set Value  610 . 4Emoji Signature Proof  810  is derived from Context Identifier Algorithm  200  in  FIG. 2 . 
     In one embodiment, individuals and entities can use 4Emoji Signature  800  as a pinxit (e.g., unique signature element) to signify knowledge, approval, acceptance, or obligation. In another embodiment, 4Emoji Signature  800  and 4Emoji Signature Proof  810  can be utilized to replace usernames and passwords for authentication. 
       FIG. 9  illustrates 4Emoji Signature Question Type Enumeration  900  which is comprised of None=0  902 , True False=1904, Multiple Choice=2  906 , Fill In the Blanks=4  908 , Connect the Dots=8  910 , Connect the Lines=32  912 , Pick Your Favorite=64  914 , Text Selection=128  916 , Image Selection=256  918 , Audio Selection=512  920 , Video Selection=1024  922 , Browse and Selection=2048  924 , Number Of=4096  926 , and Build a Word=8192  928 . 
     4Emoji Signature Question Type Enumeration  900  is an enumeration list of question types that are attributed to questions. True or False=1  904  represents questions whose answers require selecting true or false. Multiple Choice=2  906  represents questions whose answers require selecting from multiple answers. Fill in the Blanks=4  908  represents questions whose answers require the completion of an incomplete phrase. Connect the Dots=8  910  represents questions whose answers require connecting selected dots within an array. Connect the Lines=32  912  represents questions whose answers require creating shapes by connecting lines within a grid with multiple rows of line segments separated by spaces. Pick Your Favorite=64  914  represents questions whose answers require picking one or more items out of a set of items favorited. 
     Text Selection=128  916  represents questions requiring selection of one or more letters or words from a sentence, paragraph, page, document, or book. In another embodiment, this question type may represent questions that provide a list of categories of documents, books, articles, and publications. Users select a category that lists available titles from which a user selects. Within the selected title, users select letters, words, or sentences. 
     Image Selection=256  918  represents questions requiring selection of one or more images. In another embodiment, this question type may represent questions that provide a list of categories for selection. Users choose a category, and that category provides a list of image sets, wherein a user selects an image set, then selects one or more images. In another embodiment, this question type may represent questions that provide a set of images wherein a user selects an image and then one or more objects within the image. 
     Audio Selection=512 represents questions requiring selection of one or more tracks from an audio file such as an album, a soundtrack, an audio book, or any such recording such that a user selects a section of the playback. In another embodiment, this question type may represent questions that provide one or more segments from an audio recording from which a user selects their answer, for example, John F. Kennedy&#39;s Inaugural Address, where he states, “Ask not what your country can do for you—ask what you can do for your country.” 
     Video Selection=1024  922  represents questions requiring selection of one or more chapters or episodes from a video file such as a television show, movie, video series, or any such video that a user selects a section of the playback. In another embodiment, this question type may represent questions that provide two or more segments from a video from which a user selects their answer, for example, in the CBS broadcast of the first Moon landing wherein Neal Armstrong begins the statement, “That&#39;s one small step for man. One giant leap for Mankind.” 
     Browse and Selection=2048  924  represents questions requiring the user to browse to a specific document, image, audio, or video resource on the Internet. Once the resource is loaded, the user selects text, image, audio, or video as their answer. 
     Number Of=4096  926  represents questions requiring a user to select a category of topics wherein the user is provided a list of questions that the user answers with the number of items or characteristics about a topic. Topics may include personally relevant questions, such as “How many grandchildren to you have?” In another embodiment, this question type may represent questions that provide a number of objects in an image from which a user selects their answer. 
     Build a Word=8192  928  represents questions requiring a user to use a grid whose composition is columns A through Z and rows A through Z to select letters to build a word. Users may begin building a word at any column. Each column-row letter is assigned a unique integer. 
     Those skilled in the art will see that question types do not only embody the common definition of a question to elicit information; they may also include puzzles and activities. The present disclosure is not limited to the question types listed here. 
     4Emoji Signature Question Type Enumeration  900  represents the inclusion of a wealth of content in text, image, video, or audio form as well as unique ways of selecting and/or inputting content to answer questions. It illustrates a capacity to inject randomness and individual-attributed knowledge and preferences to answers that make it highly improbable for computational and algorithmic attempts to impersonate a user&#39;s selection of questions and their answers. 
     One of the most difficult problems in designing systems for user verification has been the inclusion of randomness and depth in the realm of possible characteristics that may be used to attribute to a user for future verification. Furthermore, the storage of static characteristics is an easily exploitable feature in most user verification systems. The present disclosure provides for dynamic characteristic attribution determined by the user that is provided in real time without having been stored by the system. Simply said, most systems for user verification store data about the user; these static characteristics are narrow in scope (e.g., PINs) and are easily exploitable. The present disclosure illustrates how to define and abstract dynamic characteristics outside of the user verification system. 
     By enabling dynamic questions determined by the user, with those choices by the user not being stored by the system, the present disclosure injects user-injected randomness about what is being asked of the user to be verified. This user-injected randomness is illustrated by the user&#39;s vast choices in question set, question selection, and question types. In some embodiments, users are provided with hundreds, thousands, or tens-of-thousands of categories and topics of question sets, wherein each question set in turn lists hundreds, thousands, or tens-of-thousands of questions. These embodiments illustrate the magnitude of the user&#39;s choices in selecting questions. Additionally, the randomness and depth in the realm of possible characteristics is magnified by the user&#39;s answers. 
       FIG. 10  illustrates 4Emoji Signature Question  1000  which is comprised of Question Id  1002 , Question Risk Level  1004 , Question Version  1006 , Question Type  1008 , Question  1010 , Is Dynamic Question  1012 , Question Instructions  1014 , and Question Description  1016 . 4Emoji Signature Question  1000  is connected to Question Storage  1020 . Question Id  1002  illustrates a unique identifier for 4Emoji Signature Question  1000 . Question Risk Level  1004  illustrates Risk Level  504  in  FIG. 5  associated with 4Emoji Signature Question  1000 . Question Version  1006  illustrates the version for 4Emoji Signature Question  1000 . Question Type  1008  illustrates 4Emoji Signature Question Type Enumeration  900  in  FIG. 9  specifying the type of question for 4Emoji Signature Question  1000 . Question  1010  illustrates the text, image, video, audio, puzzle, or activity for 4Emoji Signature Question  1000 . It contains the question and may also include metadata. Is Dynamic Question  1012  illustrates a Boolean indicating whether or not 4Emoji Signature Question  1000  is dynamic. Question Instructions  1014  illustrates the text, image, video, or audio to convey the instructions of 4Emoji Signature Question  1000 . Question Description  1016  illustrates a description of 4Emoji Signature Question  1000 . Question Storage  1020  illustrates a data storage system such as a database. Those skilled in the art will see that 4Emoji Signature Question  1000  is a type whose instance is stored in Question Storage  1020 . 
       FIG. 11  illustrates 4Emoji Signature Question Set  1100  which is comprised of Question Set Id  1102 , Question Set Risk Level  1104 , Question Set Version  1106 , Number of Questions Required  1108 , Question Dictionary  1110 , Is Dynamic Question Set  1114 , Question Set Instructions  1116 , Question Set Description  1118 , and Dynamic Question Sets Dictionary  1120 . Question Dictionary  1110  contains Question Number  1112  and Question Id  1002 . 
     4Emoji Signature Question Set  1100  is connected to Dynamic Question Sets  1140  via Line Segment  1142 . Dynamic Question Sets  1140  is recursively connected to itself via Line Segment  1144 , illustrating the recursive relationship of question sets, namely question sets can contain other question sets. Dynamic Question Sets  1140  is connected to Question Storage  1020  via Line Segment  1146 . 4Emoji Signature Question Set  1100  is also connected to Question Storage  1020  via Line Segment  1148 . Question Set Id  1102  illustrates a unique identifier for 4Emoji Signature Question Set  1100 . Question Set Risk Level  1104  illustrates Risk Level  504  in  FIG. 5  associated with 4Emoji Signature Question Set  1100 . Question Set Version  1106  illustrates the version of 4Emoji Signature Question Set  1100 . Number of Questions Required  1108  illustrates the number of questions or question sets required for 4Emoji Signature Question Set  1100 . Question Dictionary  1110  illustrates the questions associated with 4Emoji Signature Question Set  1100 . Each association includes Question Id  1002  and Question Number  1112 , which enables assignment of an order number for each question in Question Dictionary  1110 . 
     Is Dynamic Question Set  1114  illustrates a Boolean indicating whether or not 4Emoji Signature Question Set  1100  is dynamic. If true, Question Dictionary  1110  does not have associated questions; instead Dynamic Question Sets Dictionary  1120  has associated question sets. If false, Question Dictionary  1110  has associated questions; Dynamic Question Sets Dictionary  1120  does not have associated question sets. Dynamic Question Sets  1140  illustrates the recursive associations of question sets in Dynamic Question Sets Dictionary  1120 . Question Set Instructions  1116  illustrates the specific instructions for how to use 4Emoji Signature Question Set  1100 . Question Set Description  1118  illustrates the specific description of the nature of 4Emoji Signature Question Set  1100 . Those skilled in the art will see that 4Emoji Signature Question Set  1100  is a type whose instance is stored in Question Storage  1020 . 
       FIG. 12  illustrates 4Emoji Signature Algorithm Context  1200  which is comprised of Signature Algorithm Context Id  1202 , Signature Algorithm Context Risk Level  1204 , Signature Algorithm Context Version  1206 , Signature Algorithm Id  1208 , Signature Algorithm Version  1210 , Number of Question Sets Required  1212 , Question Set Dictionary  1214 , Signature Algorithm Emoji Data  1218 , Signature Algorithm Training Data  1220 , and Signature Algorithm Settings  1222 . Question Set Dictionary  1214  contains Question Set Number  1216  and Question Set Id  1102 . 4Emoji Signature Algorithm Context  1200  is connected to Signature Algorithm Context Storage  1224  via Line Segment  1226  and Question Storage  1020  via Line Segment  1228 . Signature Algorithm Context Id  1202  illustrates a unique identifier for 4Emoji Signature Algorithm Context  1200 . Signature Algorithm Context Risk Level  1204  illustrates a risk level for 4Emoji Signature Algorithm Context  1200 . Signature Algorithm Context Version  1206  illustrates the version assigned to 4Emoji Signature Algorithm Context  1200 . Signature Algorithm Id  1208  illustrates a signature algorithm identifier for 4Emoji Signature Algorithm Context  1200 . Signature Algorithm Version  1210  illustrates a signature algorithm version for 4Emoji Signature Algorithm Context  1200 . 
     Number of Question Sets Required  1212  illustrates the number of question sets required for 4Emoji Signature Algorithm Context  1200 . Question Set Dictionary  1214  illustrates the question sets associated with 4Emoji Signature Algorithm Context  1200 . Each association includes Question Set Id  1102  and Question Set Number  1216 , which enables assignment of an order number for each question set in Question Set Dictionary  1214 . Signature Algorithm Emoji Data  1218  illustrates Emoji  302  in  FIG. 3  associated with 4Emoji Signature Algorithm Context  1200 . Signature Algorithm Training Data  1220  illustrates training data for machine learning used to train a signature algorithm associated with 4Emoji Signature Algorithm Context  1200 . Signature Algorithm Settings  1222  illustrates settings for a signature algorithm associated with 4Emoji Signature Algorithm Context  1200 . Signature Algorithm Context Storage  1224  illustrates a data storage system such as a database. Those skilled in the art will see that 4Emoji Signature Algorithm Context  1200  is a type whose instance is stored in Signature Algorithm Context Storage  1224 . The question sets associated with Question Set Dictionary  1214  are stored in Question Storage  1020 . 4Emoji Signature Algorithm Context  1200  provides the data and settings for a signature algorithm. 
       FIG. 13  illustrates 4Emoji Signature Algorithm Manager  1300  which is comprised of Signature Algorithm Manager Module  1302 , Signature Algorithm Question Module  1304 , Signature Algorithm Question Set Module  1306 , Signature Algorithm Dynamic Question Module  1308 , Signature Algorithm Dynamic Question Set Module  1310 , Signature Algorithm Machine Learning Module  1312 , Signature Algorithm Settings Module  1314 , Signature Algorithm Context Module  1316 , Signature Algorithm Blockchain Module  1318 , and Signature Algorithm Emoji Module  1320 . 4Emoji Signature Algorithm Manager  1300  is connected to Signature Algorithm Context Storage  1224  via Line Segment  1326 , Signature Algorithm Manager Storage  1322  via Line Segment  1324 , and Question Storage  1020  via Line Segment  1328 . Signature Algorithm Manager Module  1302  illustrates all the functionality necessary to manage and design the context of the modules and the signature algorithm; see Context Identifier Algorithm  200  in FIGS. 2 and 4Emoji Signature Context Enumeration  500  in  FIG. 5 . It also illustrates the functionality to create, read, update, and delete signature algorithm management and design data, as well as all the business logic for the signature algorithm management and design. 
     Signature Algorithm Question Module  1304  illustrates all the functionality necessary to manage and design questions; see 4Emoji Signature Question  1000  in  FIG. 10  and 4Emoji Signature Question Type Enumeration  900  in  FIG. 9 . It also illustrates the functionality to create, read, update and delete question data, as well as all the business logic for question management and design. 
     Signature Algorithm Question Set Module  1306  illustrates all the functionality necessary to manage and design question sets; see 4Emoji Signature Question Set  1100  in  FIG. 11 , 4Emoji Signature Question Set Enumeration  600  in FIGS. 6, and 4Emoji Signature Question Set Alt Enumeration  700  in  FIG. 7 . It also illustrates the functionality to create, read, update and delete question set data, as well as all the business logic for question set management and design. 
     Signature Algorithm Dynamic Question Module  1308  illustrates all the functionality necessary to manage and design dynamic questions; see 4Emoji Signature Question  1000  in FIGS. 10 and 4Emoji Signature Question Type Enumeration  900  in  FIG. 9 . It also illustrates the functionality to create, read, update and delete dynamic question data, as well as all the business logic for dynamic question management and design. 
     Signature Algorithm Dynamic Question Set Module  1310  illustrates all the functionality necessary to manage and design dynamic question sets; see 4Emoji Signature Question Sets  1100  in  FIG. 11 , 4Emoji Signature Question Set Enumeration  600  in FIGS. 6, and 4Emoji Signature Question Set Alt Enumeration  700  in  FIG. 7 . It also illustrates the functionality to create, read, update and delete dynamic question set data, as well as all the business logic for dynamic question set management and design. 
     Signature Algorithm Machine Learning Module  1312  illustrates all the functionality necessary to manage and design the training data for machine learning. It also illustrates the functionality to create, read, update and delete the training data for machine learning, as well as all the business logic for machine learning management and design. 
     Signature Algorithm Settings Module  1314  illustrates all the functionality necessary to manage module and algorithm settings. It also illustrates the functionality to create, read, update and delete algorithm settings data, as well as all the business logic for module and algorithm settings management. 
     Signature Algorithm Context Module  1316  illustrates all the functionality necessary to manage module and algorithm context; see 4Emoji Signature Algorithm Context  1200  in  FIG. 12 . It also illustrates the functionality to create, read, update and delete algorithm context data, as well as all the business logic for module and algorithm context management. 
     Signature Algorithm Blockchain Module  1318  illustrates all the functionality necessary to manage and design the blockchain for the algorithm. It also illustrates the functionality to create, read, update and delete blockchain data, as well as all the business logic for blockchain management and design. 
     Signature Algorithm Emoji Module  1320  illustrates all the functionality necessary to manage and design the emoji for the algorithm; see 4Emoji Signature  800  in  FIG. 8 . It also illustrates the functionality to create, read, update and delete emoji data, as well as all the business logic for emoji management and design. 
     Signature Algorithm Manager Storage  1322  illustrates a data storage system such as a database. Those skilled in the art will see that 4Emoji Signature Algorithm Manager  1300  is a type whose instance is stored in Signature Algorithm Manager Storage  1322 . Signature Algorithm Context Module  1316  is stored in Signature Algorithm Context Storage  1224 . The questions, question sets, dynamic questions, and dynamic question sets associated with Signature Algorithm Question Module  1304 , Signature Algorithm Question Set Module  1306 , Signature Algorithm Dynamic Question Module  1308 , and Signature Algorithm Dynamic Question Set Module  1310  are stored in Question Storage  1020 . 
     4Emoji Signature Algorithm Manager  1300  is used to design, develop, test and deploy the 4Emoji signature algorithm and data. Any 4Emoji Signature Algorithm Manager  1300  module may use the functionality of other modules. Those skilled in the art will appreciate as an example, the use of 4Emoji Signature Algorithm Manager  1300  to design and manage signature algorithm and data for kiosks, web, desktop, and mobile applications. 
       FIG. 14  illustrates Create 4Emoji Signature Context Value Algorithm  1400 . The algorithm starts at Initial  1402  which is connected to Create 4Emoji Signature Context Enumeration  1404 . Create 4Emoji Signature Context Enumeration  1404  is connected to 4Emoji Signature Algorithm Context  1200 , 4Emoji Signature Context Enumeration  1450 , and Bitwise OR Risk Level and Version  1406 . Bitwise OR Risk Level and Version  1406  is connected to 4Emoji Signature Context Enumeration  1450  and Initialize Variable, [i=0]  1408 . Initialize Variable, [i=0]  1408  is connected to Select Question Set  1410 . Select Question Set  1410  is connected to Selected Question Set Dictionary  1452  and Bitwise OR Question Sets  1412 . Bitwise OR Question Sets  1412  is connected to 4Emoji Signature Context Enumeration  1450  and Increment Variable, [i++]  1414 . Increment Variable, [i++]  1414  is connected to Decision  1416 . 
     Decision  1416  is connected to note [i&gt;=Number of Question Sets Required]  1418 , which illustrates the looping criteria. While i&lt;Number of Question Sets Required, control proceeds to Select Question Set  1410  via Line Segment  1420 . When i&gt;=Number of Question Sets Required, control proceeds to Match Emoji  1424  via Line Segment  1422 . Match Emoji  1424  is connected to 4Emoji Signature Context Value  1454  and Set First Emoji  1426 . Set First Emoji  1426  is connected to 4Emoji Signature  800  as well as Final  1428 , illustrating the end of the algorithm. 
     Create 4Emoji Signature Context Value Algorithm  1400  takes in 4Emoji Signature Algorithm Context  1200  and requires 4Emoji Signature  800 . Create 4Emoji Signature Context Value Algorithm  1400  produces 4Emoji Signature Context Enumeration  1450  (see 4Emoji Signature Context Enumeration  500  in  FIG. 5 ), Selected Question Set Dictionary  1452 , and 4Emoji Signature Context Value  1454  (see 4Emoji Signature Context Value  526  in  FIG. 5 ). Bitwise OR Question Sets  1412  refers to Question Sets  518  in  FIG. 5 . [i&gt;=Number of Question Sets Required]  1418  refers to Number of Question Sets Required  1212  in  FIG. 12 . Match Emoji  1424  illustrates that 4Emoji Signature Context Value  1454  integer is matched with the corresponding First Emoji  802  in  FIG. 8 . First Emoji  802  thereby visually, audibly, or tactilely represents the risk level, version, and selected question sets defined by the user in Create 4Emoji Signature Context Value Algorithm  1400 . This is further illustrated by Set First Emoji  1426 ; see First Emoji  802  in  FIG. 8  and Emoji Signature Context Value  526  in  FIG. 5  as illustrated in 4Emoji Signature  800  in  FIG. 8 . 
     Risk Level  504  in  FIG. 5  determines the Number of Question Sets Required  1212  in  FIG. 12 . In another embodiment, Risk Level  504  may determine if Is Dynamic Question Set  1114  is true. Risk Level  504  also determines question sets, number, and degree of difficulty required to mitigate the associated risk implied by the risk level for a specific use case. The greater the complexity of question sets, the more difficult the questions are to answer and the longer it takes a user to navigate and answer associated questions. This is particularly true in the case of dynamic question sets where multiple levels of question sets (see Dynamic Question Sets Dictionary  1120  and Is Dynamic Question Set  1114  in  FIG. 11 ) require a user to select and recall the path to the questions presented by the last question set, as only the first question set is stored in 4Emjoi Signature Context Enumeration  1450 . The dynamic question sets&#39; path creates greater randomness in the system. 
     Those skilled in the art will see that Create 4Emoji Signature Context Value Algorithm  1400  is used by a user to select question sets. The question sets, risk level, and version produce 4Emoji Signature Context Value  1454  as an integer which then is matched to produce First Emoji  802  in 4Emoji Signature  800 . 
       FIG. 15  illustrates Create 4Emoji Question Set Value Algorithm  1500 . The algorithm starts at Initial  1502  which is connected to Create 4Emoji Signature Question Set Enumeration  1504 . Create 4Emoji Signature Question Set Enumeration  1504  is connected to 4Emoji Signature Question Set Enumeration  1550 , 4Emoji Signature Algorithm Context  1200 , and Bitwise OR Risk Level and Version  1506 . Bitwise OR Risk Level and Version  1506  is connected to 4Emoji Signature Question Set Enumeration  1550  and Load Questions  1508 . Load Questions  1508  is connected to Question Dictionary  1110  and Select Question  1510 . Select Question  1510  is connected to Answer Question  1512 . Answer Question  1512  is connected to Answer Dictionary  1552  and Bitwise OR Question  1514 . Bitwise OR Question  1514  is connected to 4Emoji Signature Question Set Enumeration  1550  and Increment Variable, [i++]  1516 . Increment Variable, [i++]  1516  is connected to Decision  1518 . 
     Decision  1518  is connected to note [i&gt;=Number of Question Required]  1520 , which illustrates the looping criteria. While i&lt;Number of Question Required, control proceeds to Select Question  1510  via Line Segment  1522 . When i&gt;=Number of Question Required, control proceeds to Match Emoji  1526  via Line Segment  1524 . Match Emoji  1526  is connected to 4Emoji Signature Question Set Value  1554  and Set Emoji  1528 . Set Emoji  1528  is connected to 4Emoji Signature  800  and Final  1532 , illustrating the end of the algorithm. 
     Set Emoji  1528  is also connected to note Second Emoji, Third Emoji and Fourth Emoji  1530 , which illustrates that in addition to setting Second Emoji  804 , Create 4Emoji Signature Question Set Value Algorithm  1500  and resulting Set Emoji  1528  are also used to set Third Emoji  806  and Fourth Emoji  808 . 
     Create 4Emoji Signature Question Set Value Algorithm  1500  takes in 4Emoji Signature Algorithm Context  1200  and requires 4Emoji Signature  800 . Create 4Emoji Signature Question Set Value Algorithm  1500  produces 4Emoji Signature Question Set Enumeration  1550  (see 4Emoji Signature Question Set Enumeration  600  in  FIG. 6 ), Answer Dictionary  1552 , and 4Emoji Signature Question Set Value  1554  (see 4Emoji Signature Question Set Value  610  in  FIG. 6 ). Bitwise OR Question  1514  refers to Question Set  602  in  FIG. 6 . [i&gt;=Number of Questions Required]  1520  refers to Number of Questions Required  1108  in  FIG. 11 . Match Emoji  1526  illustrates that 4Emoji Signature Question Set Value  1554  integer is matched with a corresponding Second Emoji  804 , Third Emoji  806 , or Fourth Emoji  808  in  FIG. 8 . These emoji thereby visually, audibly, or tactilely represent the risk level, version, and selected questions defined by the user in Create 4Emoji Signature Question Set Value Algorithm  1500 . This is further illustrated by Set Emoji  1528 ; see Second Emoji  804 , Third Emoji  806 , Fourth Emoji  808  and 4Emoji Signature Question Set Value  610  as illustrated in 4Emoji Signature  800  in  FIG. 8 . 
     Risk Level  504  in  FIG. 5  determines the Number of Questions Required  1108  in  FIG. 11 . In another embodiment, Risk Level  504  may determine if Is Dynamic Question  1012  is true. Risk Level  504  also determines question, number, and degree of difficulty required to mitigate the associated risk implied by the risk level for a specific use case. The greater the complexity of the question, the more difficult the question is to answer and the longer it takes a user to answer the question. This is particularly true in the case of a dynamic question where a user is expected to perform multiple time-consuming tasks (see 4Emoji Signature Question Type Enumeration  900  in  FIG. 9 ). 
     Dynamic questions (see Is Dynamic Question  1012  on  FIG. 10 ) include Text Selection=128  916 , Image Selection=256  918 , Audio Selection=512  920 , Video Selection=1024  922  as well as the notable Browse and Selection=2048  924 . These dynamic questions create greater randomness in the system, especially Browse and Selection=2048  924  where a user inputs a URL to a resource that only the user knows to perform the additional steps of selection. 
     Those skilled in the art will see that Create 4Emoji Signature Question Set Value Algorithm  1500  is used by a user to select questions. The questions, risk level, and version produce 4Emoji Signature Question Set Value  1554  as an integer which then is matched to produce Second Emoji  804 , Third Emoji  806 , and Fourth Emoji  808  of 4Emoji Signature  800 . 
       FIG. 16  illustrates Create 4Emoji Signature Algorithm  1600 . The algorithm starts at Initial  1602  which is connected to Load Signature Algorithm Context  1604 . Load Signature Algorithm Context  1604  requires 4Emoji Signature Algorithm Context  1200  and is connected to Create 4Emoji Signature  1606 . Create 4Emoji Signature  1606  is connected to 4Emoji Signature  1650  and Input Signature Algorithm Risk Level  1608 . Input Signature Algorithm Risk Level  1608  is connected to Input Signature Algorithm Version  1610 . Input Signature Algorithm Version  1610  is connected to Input Signature Algorithm Question Sets  1612 . Input Signature Algorithm Question Sets  1612  requires Question Set Dictionary  1214  and is connected to Create 4Emoji Signature Context Value Algorithm  1400 . Create 4Emoji Signature Context Value Algorithm  1400  is connected to Initialize Variable, [i=0]  1614 . Initialize Variable, [i=0]  1614  is connected to Decision  1616 . 
     Decision  1616  is connected to note [i&lt;Selected Question Set Dictionary]  1618 , which illustrates the looping criteria. While i&lt;Selected Question Set Dictionary, control proceeds to Input Signature Algorithm Questions  1622  via Line Segment  1620 . Input Signature Algorithm Questions  1622  requires Question Dictionary  1110  and is connected to Create 4Emoji Signature Question Set Value Algorithm  1500 . Create 4Emoji Signature Question Set Value Algorithm  1500  is connected to Decision  1616  via Line Segment  1624 . When i=Selected Question Set Dictionary, control proceeds to Save 4Emoji Signature  1628  via Line Segment  1626 . Save 4Emoji Signature  1628  is connected to 4Emoji Data Storage System  438  and Final  1630 , illustrating the end of the algorithm. 
     Create 4Emoji Signature Algorithm  1600  is used to create a user&#39;s 4Emoji Signature  1650  (see 4Emoji Signature  800  in  FIG. 8 ). Of note, Input Signature Algorithm Question Sets  1612  loads question sets by the order in Question Set Number  1216  in  FIG. 12 , and Input Signature Algorithm Questions  1622  loads questions by the order in Question Number  1112  in  FIG. 11 . 
     Save 4Emoji Signature  1628  illustrates the saving of 4Emoji Signature  1650  to 4Emoji Data Storage System  438 . Those skilled in the art will appreciate that the present disclosure saves 4Emoji Signature  1650  for future verification. 
       FIG. 17  illustrates Create 4Emoji Signature Proof Algorithm  1700 . The algorithm starts at Initial  1702  which is connected to Create 4Emoji Signature Proof Key-Value Pairs (KVP)  1704 . Create 4Emoji Signature Proof Key-Value Pairs (KVP)  1704  is connected to note [KVP Capacity=Answer Dictionary+1]  1706  as well as Hash 4Emoji Signature Context Value  1708 . Hash 4Emoji Signature Context Value  1708  requires 4Emoji Signature Context Value  1454  and is connected to Add 4Emoji Signature Context Value Hash to KVP  1710 . Add 4Emoji Signature Context Value Hash to KVP  1710  is connected to Add Answer Dictionary to KVP  1712 . Add Answer Dictionary to KVP  1712  requires Answer Dictionary  1552  and is connected to Create 4Emoji Signature Proof  1714 . Create 4Emoji Signature Proof  1714  is connected to Context Identifier Algorithm  200  as well as Save 4Emoji Signature Proof  1716 . Save 4Emoji Signature Proof  1716  generates 4Emoji Signature Proof  1750  and is connected to 4Emoji Data Storage System  438  and Final  1718 , illustrating the end of the algorithm. 
     Create 4Emoji Signature Proof Algorithm  1700  is used to create an instance of a user&#39;s 4Emoji Signature Proof  810  in  FIG. 8  as illustrated by Create 4Emoji Signature Proof  1714  in  FIG. 17  which utilizes Context Identifier Algorithm  200  in  FIG. 2  and the resulting 4Emoji Signature Proof  1750 . 
     Save 4Emoji Signature Proof  1716  illustrates the saving of 4Emoji Signature Proof  1750  to 4Emoji Data Storage System  438 . Those skilled in the art will appreciate that the present disclosure saves 4Emoji Signature Proof  1750  for future verification. 
     In one embodiment, individuals and entities can use 4Emoji Signature  800  as a pinxit to signify knowledge, approval, acceptance, or obligation, and then use 4Emoji Signature Proof  810  to verify their identity. As an example, consider the scenario where an individual or entity presents a signed check to a bank teller for deposit and is asked to present proof of identification to verify that they are the signee. With the present disclosure, the individual or entity uses their 4Emoji Signature  800  as the signature and their 4Emoji Signature Proof  810  as their identification, thereby eliminating the need for traditional signatures and identification. Furthermore, the current technologies for digital signatures are replaced with something that is far more tangible, easily accessible, low cost to replace, and arguably more secure. In some ways, the present disclosure returns transactions to the former state which required individuals and entities to participate in the signing and verification process, granting them more direct power and control. 
     The present disclosure could replace traditional credit cards and digital devices to perform financial transactions. A touch screen terminal in combination with 4Emoji Signature  800 , 4Emoji Signature Proof  810  and associated algorithms could be used to facilitate all types of financial transactions with varying degrees of risk, e.g. purchasing $50 worth of gasoline (low risk), purchasing a $1000 smart phone (medium risk), and purchasing a $50,000 car (high risk). In each instance, the risk would be mitigated by the present disclosure. 
     The present disclosure is relatively less expensive than credit card and digital device payment infrastructure and offers other benefits such as an ability for individuals and entities to easily and quickly reset their 4Emoji Signature  800  and 4Emoji Signature Proof  810 . No more lost or stolen credit cards. No more restricted access to funds or credit while waiting for new debit and credit cards to arrive in the mail. No more worrying about identity theft since users can now inject power and control over their identity that heretofore has not been possible. Using the present disclosure users can control access to their credit reports by requiring that access be granted only through their 4Emoji Signature  800  and 4Emoji Signature Proof  810 . 
       FIG. 18  illustrates 4Emoji Signature Context Value Proof Algorithm  1800 . The algorithm starts at Initial  1802  which is connected to Input 4Emoji Signature Context Enumeration  1804 . Input 4Emoji Signature Context Enumeration  1804  requires 4Emoji Signature Context Enumeration  500  and is connected to Create 4Emoji Signature Context Enumeration Proof  1806 . Create 4Emoji Signature Context Enumeration Proof  1806  is connected to 4Emoji Signature Context Enumeration Proof  1850  and Bitwise OR Risk Level and Version  1808 . Bitwise OR Risk Level and Version  1808  is connected to 4Emoji Signature Context Enumeration Proof  1850  and Initialize Variable, [i=0]  1810 . Initialize Variable, [i=0]  1810  is connected to Select Question Set  1812 . Select Question Set  1812  is connected to Selected Question Set Dictionary  1852  and Bitwise OR Question Sets  1814 . Bitwise OR Question Sets  1814  is connected to 4Emoji Signature Context Enumeration Proof  1850  and Increment Variable, [i++]  1816 . Increment Variable, [i++]  1816  is connected to Decision  1818 . 
     Decision  1818  is connected to note [i&lt;Question Sets Count]  1820 , which illustrates the looping criteria. While i&lt;Question Sets Count, control proceeds to Select Question Set  1812  via Line Segment  1822 . When i=Question Sets Count, control proceeds to Save 4Emoji Signature Context Value Proof  1826  via Line Segment  1824 . Save 4Emoji Signature Context Value Proof  1826  is connected to 4Emoji Signature Context Value Proof  1854  and Final  1828 , illustrating the end of the algorithm. 4Emoji Signature Context Value Proof Algorithm  1800  takes in 4Emoji Signature Algorithm Enumeration  500 . 4Emoji Signature Context Value Proof Algorithm  1800  produces 4Emoji Signature Context Enumeration Proof  1850 , Selected Question Set Dictionary  1852 , and 4Emoji Signature Context Value Proof  1854 . Bitwise OR Question Sets  1814  refers to Question Sets  518  in  FIG. 5 . [i&lt;Question Sets Count]  1820  refers to Number of Question Sets Required  1212  in  FIG. 12 . 
     Those skilled in the art will see that 4Emoji Signature Context Value Proof Algorithm  1800  is used by a user to select question sets. Those question sets, risk level, and version produce a 4Emoji Signature Context Value Proof  1854  as an integer. 
       FIG. 19  illustrates 4Emoji Signature Question Set Value Proof Algorithm  1900 . The algorithm starts at Initial  1902  which is connected to Input 4Emoji Signature Question Set Enumeration  1904 . Input 4Emoji Signature Question Set Enumeration  1904  requires 4Emoji Signature Question Set Enumeration  600  and is connected to Create 4Emoji Signature Question Set Enumeration Proof  1906 . Create 4Emoji Signature Question Set Enumeration Proof  1906  is connected to 4Emoji Question Set Enumeration Proof  1950  and Bitwise OR Risk Level and Version  1908 . Bitwise OR Risk Level and Version  1908  is connected to 4Emoji Question Set Enumeration Proof  1950  and Initialize Variable, [i=0]  1910 . Initialize Variable, [i=0]  1910  is connected to Select Question  1912 . Select Question  1912  is connected to Selected Question Dictionary  1952  and Bitwise OR Question  1914 . Bitwise OR Question  1914  is connected to 4Emoji Question Set Enumeration Proof  1950  and Answer Question  1916 . Answer Question  1916  is connected to Answer Dictionary  1954  and Increment Variable, [i++]  1918 . Increment Variable, [i++]  1918  is connected to Decision  1920 . 
     Decision  1920  is connected to note [i&lt;Question Count]  1922 , which illustrates the looping criteria. While i&lt;Question Count, control proceeds to Select Question  1912  via Line Segment  1924 . When i=Question Count, control proceeds to Save 4Emoji Signature Question Set Value Proof  1928  via Line Segment  1926 . Save 4Emoji Signature Question Set Value Proof  1928  is connected to 4Emoji Signature Question Set Value Proof  1956  and Final  1930 , illustrating the end of the algorithm. 
     4Emoji Signature Question Set Value Proof Algorithm  1900  takes in 4Emoji Signature Question Set Enumeration  600 . 4Emoji Signature Question Set Value Algorithm  1900  produces 4Emoji Signature Question Set Enumeration Proof  1950 , Selected Question Dictionary  1952 , and 4Emoji Signature Question Set Value Proof  1956 . Bitwise OR Question  1914  refers to Question Set  602  in  FIG. 6 . [i&lt;Question Count]  1922  refers to Number of Questions Required  1108  in  FIG. 11 . 
     Those skilled in the art will see that 4Emoji Signature Question Set Value Proof Algorithm  1900  is used by a user to select questions. These questions, risk level, and version produce a 4Emoji Signature Question Set Value Proof  1928  as an integer. 
       FIG. 20  illustrates 4Emoji Signature Verification Algorithm  2000 . The algorithm starts at Initial  2002  which is connected to Load Signature Algorithm Context  2004 . Load Signature Algorithm Context  2004  requires 4Emoji Signature Algorithm Context  1200  and is connected to Input 4Emoji Signature  2006 . Input 4Emoji Signature  2006  requires 4Emoji Signature  800  and is connected to Set Signature Context Risk Level  2008 . Set Signature Context Risk Level  2008  is connected to 4Emoji Signature Context Enumeration  2050  and Set Signature Context Version  2010 . Set Signature Context Version  2010  is connected to 4Emoji Signature Context Enumeration  2050  and Set Signature Context Question Sets  2012 . Set Signature Context Question Sets  2012  is connected to 4Emoji Signature Context Enumeration  2050 , Question Set Dictionary  1214 , and 4Emoji Signature Context Value Proof Algorithm  1800 . 4Emoji Signature Context Value Proof Algorithm  1800  is connected to 4Emoji Signature Context Proof  1854  and Decision  2014 . 
     Decision  2014  is connected to Final  2018  via Line Segment  2016  and Decision  2022  via Line Segment  2020 . Decision  2022  is connected to note [For Each 4Emoji Signature Question Set Enumeration]  2024  and 4Emoji Signature Question Set Value Proof Algorithm  1900 . 4Emoji Signature Question Set Value Proof Algorithm  1900  is connected to 4Emoji Signature Question Set Value Proof  1956  and Decision  2026 . Decision  2026  is connected to Decision  2022  via Line Segment  2028 , Final  2032  via Line Segment  2030 , and Verified Final  2036  via Line Segment  2034 . 
     4Emoji Signature Verification Algorithm  2000  is used to verify a user&#39;s 4Emoji Signature. The algorithm takes 4Emoji Signature  800  to verify 4Emoji Signature Context Value  526  in  FIG. 5  for First Emoji  802  and 4Emoji Signature Question Set Value  610  in  FIG. 6  for Second Emoji  804 , Third Emoji  806 , and Fourth Emoji  808  in  FIG. 8 . 4Emoji Signature Context Proof Algorithm  1800  is utilized to generate 4Emoji Signature Context Proof  1854 . 
     The expected 4Emoji Signature Context Value  526  is derived from 4Emoji Signature Context Enumeration  2050  (see 4Emoji Signature Context Enumeration  500  in  FIG. 5 ). Decision  2014  determines if the expected 4Emoji Signature Context Value  526  is equal to 4Emoji Signature Context Value Proof  1854 . If false, the 4Emoji Signature fails verification as illustrated by Final  2018 . 
     Each of the expected 4Emoji Question Set Values (Second Emoji  804 , Third Emoji  306 , and Fourth Emoji  808 ) is derived from 4Emoji Signature Context Enumeration  2050 . Decision  2022  determines if each expected 4Emoji Signature Question Set Value  610  is equal to 4Emoji Signature Question Set Value Proof  1956 . If any are false, the 4Emoji Signature fails verification as illustrated by Final  2032 . If all are true, the 4Emoji Signature passes verification as illustrated by Verified Final  2036 . 
     Those skilled in the art will appreciate that 4Emoji Signature Verification Algorithm  2000  verifies a 4Emoji Signature by determining if the user&#39;s choice of question sets and questions matches the signature. A bad actor may acquire a user&#39;s 4Emoji Signature, however 4Emoji Signature Verification Algorithm  2000  will verify the signature against the bad actor&#39;s input. 
     Traditional signatures can be easily forged. 4Emoji Signatures significantly reduce the probability of forgery. The versioning of the algorithm, context, question sets, and questions introduces an intended randomness. Changing versions frequently further reduces the probability of a 4Emoji Signature being forged. Those skilled in the art will appreciate the advantage of 4Emoji Signatures over digital signatures as well. 
       FIG. 21  illustrates 4Emoji Signature Proof Verification Algorithm  2100 . The algorithm starts at Initial  2102  which is connected to Create 4Emoji Signature Proof Verification Key-Value Pairs (KVP)  2104 . Create 4Emoji Signature Proof Verification Key-Value Pairs (KVP)  2104  is connected to note [KVP Capacity=Answer Dictionary+1]  2106  and Hash 4Emoji Signature Context Value Proof  2108 . Hash 4Emoji Signature Context Value Proof  2108  requires 4Emoji Signature Context Value Proof  1854  and is connected to Add 4Emoji Signature Context Value Proof Hash to KVP  2110 . Add 4Emoji Signature Context Value Proof Hash to KVP  2110  is connected to Add Answer Dictionary to KVP  2112 . Add Answer Dictionary to KVP  2112  requires Answer Dictionary  1954  and is connected to Create 4Emoji Signature Proof Verification  2114 . Create 4Emoji Signature Proof Verification  2114  is connected to Context Identifier Algorithm  200 , 4Emoji Signature Proof Verification  2150 , and Verify 4Emoji Signature Proof  2116 . Verify 4Emoji Signature Proof  2116  requires 4Emoji Signature Proof  1750  and is connected to 4Emoji Signature Proof Verification  2150 , 4Emoji Data Storage System  438 , and Decision  2118 . Decision  2118  is connected to Final  2122  via Line Segment  2120 . Decision  2118  is also connected to Verified Final  2126  via Line Segment  2124 . 
     4Emoji Signature Proof Verification Algorithm  2100  is used to verify a user&#39;s 4Emoji Signature Proof  1750 . Those skilled in the art will appreciate that the algorithm utilizes the saved outputs of 4Emoji Signature Context Value Proof Algorithm  1800  in FIGS. 18 and 4Emoji Signature Question Set Value Proof Algorithm  1900  in  FIG. 19  for a user&#39;s 4Emoji Signature  800  and their inputs in  FIG. 8 . Specifically, 4Emoji Signature Proof Verification Algorithm  2100  utilizes 4Emoji Signature Context Proof Value  1854 , Answer Dictionary  1954 , and Context Identifier Algorithm  200  to generate 4Emoji Signature Proof Verification  2150 . 
     The algorithm then retrieves 4Emoji Signature Proof  1750  from 4Emoji Data Storage System  438  to determine if the values of 4Emoji Signature Proof  1750  and 4Emoji Signature Proof Verification  2150  are equal. If the values are equal, then the user&#39;s 4Emoji Signature  800  is verified, as illustrated by Verified Final  2026 . If the values are not equal, then the user&#39;s 4Emoji Signature  800  is not verified, as illustrated by Final  2122 . 
     Those skilled in the art will appreciate that 4Emoji Signature Proof Verification Algorithm  2100  verifies that a stored 4Emoji Signature Proof  1750  (derived by the “originating” user who created the 4Emoji Signature) equals 4Emoji Signature Proof Verification  2150  (derived by an “active” user&#39;s answers). 
     An “active” user as a bad actor may acquire a user&#39;s 4Emoji Signature, however the present disclosure, in addition to 4Emoji Signature  800  via 4Emoji Signature Verification Algorithm  2000  in  FIG. 20 , requires the “active” user to choose questions sets and questions as well as provide answers to the chosen questions to generate 4Emoji Signature Proof Verification  2150 . Given the randomness of the version, question sets, questions, paths to questions through dynamic question sets, answers to questions, and answers and activities of dynamic questions, the resulting probability of bad actor&#39;s 4Emoji Signature Proof Verification  2150  matching a stored 4Emoji Signature Proof  1750  via 4Emoji Signature Proof Verification Algorithm  2100  is infinitesimal. 
     Those skilled in the art will appreciate the security advantages of 4Emoji Signature Verification and 4Emoji Signature Proof Verification over traditional usernames and passwords. Using 4Emoji Signature Verification enables verification of the username itself. This enablement, combined with 4Emoji Signature Proof Verification enablement, is revolutionary. 
     The present disclosure provides users with control and direction over their 4Emoji Signature and 4Emoji Signature Proof. Those skilled in the art will appreciate how engrained users&#39; credentials (e.g., usernames and passwords) are in information systems. Usernames are generally unique and immutable, and for decades, usernames have been combined in directories with their contact details. Changing usernames is often not possible, and if it is possible, the change is a costly function. 
     In recent years, whole industries have adopted email addresses as usernames, essentially weakening username and password protocols by making the username easily discernable for bad actors. While the introduction of Two Factor Authentication (2FA) has increased security of the creation and use of credentials, it is costly to implement and maintain, difficult for most users to understand, and not foolproof. Furthermore, using email addresses as usernames does not change the current paradigm of limited user control and direction of credentials and credentials being collapsed with contact information. 
     The present disclosure disrupts the current paradigm. Credentials are now an abstraction where usernames (i.e. 4Emoji Signature) are ephemeral and non-unique while providing users with control and direction over their passwords (i.e. 4Emoji Signature Proof). The resulting benefits are low cost, more security, easy and quick re-credentialing, and the possibility of bifurcating credentials from contact information. 
       FIG. 22  illustrates 4Emoji Signature Credit Report Example  2200 . 4Emoji Systems  2202  is connected to Notification Module  2204 , Merchant Verification Module  2206 , and Network(s)  2260 . User  2220  utilizes 4Emoji App  2222  which is connected to Network(s)  2260 . Bad Actor  2230  utilizes App  2232  which is connected to Network(s)  2260 . User  2240  utilizes Financial Systems  2242  which is connected to Network(s)  2260 . User  2250  utilizes Merchant Systems  2252  which is connected to Network(s)  2260 . Credit Reporting Systems  2270  is connected to Network(s)  2260 . 
     4Emoji Signature Credit Report Example  2200  also contains 4Emoji Signature Credit Report Example Data  2280  which is comprised of Credit Report Id  2282 , 4Emoji Credit Report Verification URL  2284 , Merchant Id  2286 , Transaction Data  2288 , 4Emoji Signature  2290 , 4Emoji Signature Proof  2292 , and 4Emoji Signature Verification Result  2294 . 4Emoji Systems  2202  provides 4Emoji Signature  2290  and 4Emoji Signature Proof  2292  verification by 4Emoji Signature Verification Result  2294 . 
     4Emoji Signature Credit Report Example  2200  illustrates Bad Actor  2230  trying to make a large, fraudulent purchase that requires the merchant (User  2250 ) to pull a credit report wherein 4Emoji Signature  2290  and 4Emoji Signature Proof  2292  thwart unauthorized use of User  2220 &#39;s credit report. 
     User  2220  utilizes 4Emoji Systems  2202  via 4Emoji App  2222  and Network(s)  2260  to generate a credit report customer statement. The statement contains Credit Report Id  2282  which is comprised of Context Identifier  228  in FIGS. 2 and 4Emoji Credit Report Verification URL  2284 . User  2220  then places that statement into their credit report(s). 
     Bad Actor  2230  uses App  2232  and Network(s)  2260  to purchase an automobile from User  2250  utilizing Merchant Systems  2252 . Bad Actor  2230  provides Transaction Data  2288  to Merchant Systems  2252 . Transaction Data  2288  contains personal identifiable information (PII) for User  2220 . 
     User  2250  utilizes Merchant Systems  2252  to pull User  2220 &#39;s credit report from Credit Reporting Systems  2270 . Unknown to Bad Actor  2230 , User  2250  follows the customer statement on User  2220 &#39;s credit report by browsing to 4Emoji Credit Report Verification URL  2284 . User  2250  views the web page on 4Emoji Systems  2202  and follows the instructions to enter in Credit Report Id  2282 , Merchant Id  2286 , and relevant Transaction Data  2288 . 
     Note that Transaction Data  2288  includes User  2220 &#39;s PII as well as data about the purchase. If User  2250  does not have Merchant Id  2286 , Transaction Data  2288  would also include information about User  2250  so that a Merchant Id can be generated for User  2250 . Using Merchant Verification Module  2206 , Merchant Id  2286  is issued and verified. 
     After merchant verification, 4Emoji Systems  2202  sends User  2220  a transaction notification via Notification Module  2204 . If User  2220 &#39;s communications have not been compromised, User  2220  can contact User  2250  as well as authorities to report the fraudulent activity of Bad Actor  2230 . 
     If Bad Actor  2230  has hacked User  2220 &#39;s email to intercept email notifications or has User  2220 &#39;s mobile device to intercept SMS notifications, Bad Actor  2230  is instructed to verify their identity via 4Emoji Signature  2290  and 4Emoji Signature Proof  2292 . Even if Bad Actor  2230  has User  2220 &#39;s 4Emoji Signature  2290 , they would fail 4Emoji Signature Proof  2292  verification via 4Emoji Signature Verification Result  2294  in 4Emoji Systems  2202 —especially since the risk level assigned to this type of purchase is so high. 
     Following verification failure, User  2220  and User  2250  are notified of the fraudulent activity by Bad Actor  2230 . Since 4Emoji Systems  2202  provides real-time interaction for notification and required identity verification, there is no opportunity for Bad Actor  2230  to take advantage of User  2220 &#39;s compromised communications. Moreover, since 4Emoji Systems  2202  captures all Transaction Data  2288 , the fraudulent activity is documented and can be utilized by authorities to hold Bad Actor  2230  accountable for their crime. 
     The present disclosure can be utilized in similar scenarios where identity needs to be verified, such as employment verification, address verification, government agency verification, etc.—essentially anywhere that a user&#39;s identity needs to be verified. 
     The present disclosure has the potential to end identity theft by enabling individuals and entities to actively participate in transactions requiring identity and identity verification in a way that&#39;s not available to them now. Furthermore, 4Emoji Signature Credit Report Example  2200  requires no changes to laws or existing credit reporting processing systems to implement. 
       FIG. 23  illustrates 4Emoji Signature Purchase Example  2300 . 4Emoji Systems  2302  is connected to Notification Module  2304 , Merchant Verification Module  2306 , and Network(s)  2360 . User  2320  utilizes 4Emoji App  2322  which is connected to Network(s)  2360 . Bad Actor  2330  utilizes App  2332  which is connected to Network(s)  2360 . User  2340  utilizes Financial Systems  2342  which is connected to Network(s)  2360 . User  2350  utilizes Merchant Systems  2352  which is connected to Network(s)  2360 . Credit Reporting Systems  2370  is connected to Network(s)  2360 . 
     4Emoji Signature Purchase Example  2300  also contains 4Emoji Signature Purchase Example Data  2380  which is comprised of Merchant Id  2382 , Transaction Data  2384 , 4Emoji Signature  2386 , 4Emoji Signature Proof  2388 , and 4Emoji Signature Verification Result  2390 . 4Emoji Systems  2302  provides 4Emoji Signature  2386  and 4Emoji Signature Proof  2388  verification by 4Emoji Signature Verification Result  2390 . 
     4Emoji Signature Purchase Example  2300  illustrates how the present disclosure can be utilized to make any size purchase without a credit card, debit card, or digital device. Consider User  2320  pulls into a gas station to purchase gasoline for their vehicle. User  2320  accesses Merchant Systems  2352  and selects “Pay with 4Emoji” payment option. Merchant Systems  2352  connects to 4Emoji Systems  2302  via Network(s)  2360  and prompts User  2320  to enter in 4Emoji Signature  2386 . 4Emoji Systems  2302  then requires User  2320  to generate 4Emoji Signature Proof  2388 . Once User  2320  has completed answering their questions, 4Emoji Systems  2302  determines 4Emoji Signature Verification Result  2390 . If false, then the transaction is denied. If true, the transaction is approved and the funds for the transaction are debited from User  2320 &#39;s financial institution. 
     If Bad Actor  2330  tried to use User  2320 &#39;s 4Emoji Signature  2386  to fill up their gas tank, it would be highly improbable that Bad Actor  2330  could generate the correct answers required for 4Emoji Signature Proof  2388  and a true 4Emoji Signature Verification Result  2390 . 
     The present disclosure mitigates unauthorized financial transactions by utilizing risk levels as part of the 4Emoji Signature Proof. Note that as purchase price increases, risk level increases, and question and question set randomness increases. 
       FIG. 24  illustrates Create 4Emoji Mark Username Algorithm  2400 . The algorithm starts at Initial  2402  which is connected to Create 4Emoji Mark Username  2404 . Create 4Emoji Mark Username  2404  is connected to Get User First Name and Last Name  2406 . Get User First Name and Last Name  2406  is connected to Directory Database  100  and Create 4Emoji Mark Algorithm  400 . Create 4Emoji Mark Algorithm  400  is connected to 4Emoji Mark  2450  and Create 4Emoji Mark Username  2408 . Create 4Emoji Mark Username  2408  is connected to Save 4Emoji Mark  2410 . Save 4Emoji Mark  2410  is connected to Directory Database  100  and Final  2412 , illustrating the end of the algorithm. Those skilled in the art will appreciate that Directory Database  100  can be extended to include 4Emoji Mark  2450  (see 4Emoji Mark  340  in  FIG. 3 ). 
     4Emoji Mark Username  2420  illustrates a user named William Anderson  2424  in combination with First Emoji  2426 , Second Emoji  2428 , Third Emoji  2430 , and Fourth Emoji  2432 . In another embodiment, 4Emoji Mark Username  2420  may be prepended with @  2422 . 
     The present disclosure makes it possible for all users to use their real first and last name as their username. System usernames are required to be unique. By appending 4Emoji Mark  2450  to a user&#39;s first name and last name, 4Emoji Mark Username  2420  becomes an immutable and unique username. 4Emoji Mark Username  2420  enables disambiguation for users who have the same first and last name and provides a unique and easily recognizable visual, audio, or tactile identification for a specific user. As such, viewers of usernames can now quickly identify and disambiguate users for attribution of content, credit, acknowledgement, et cetera. 
       FIG. 25  illustrates 4Emoji Mark Username Search Algorithm  2500 . The algorithm starts at Initial  2502  which is connected to Input 4Emoji  2504 . Input 4Emoji  2504  is connected to First Emoji  2506 , Second Emoji  2508 , Third Emoji  2510 , Fourth Emoji  2512  and Context Identifier Algorithm  200 . Context Identifier Algorithm  200  is connected to Context Identifier  2550  and Search Database for 4Emoji Mark Id  2514 . Search Database for 4Emoji Mark Id  2514  is connected to Directory Database  100  and Return 4Emoji Mark Username Data  2516 . Return 4Emoji Mark Username Data  2516  is connected to Final  2518 , illustrating the end of the algorithm. Those skilled in the art will appreciate that Directory Database  100  can be extended to include 4Emoji Mark  340  in  FIG. 3 . 
     4Emoji Mark Username Search  2520  contains Search  2522  with First Emoji  2506 , Second Emoji  2508 , Third Emoji  2510 , and Fourth Emoji  2512 . When Search  2522  is activated, if it has a result, 4Emoji Mark Username  2524  is returned. 4Emoji Mark Username  2524  illustrates a user named Angie Anderson  2528  in combination with First Emoji  2506 , Second Emoji  2508 , Third Emoji  2510 , and Fourth Emoji  2512 . In another embodiment, 4Emoji Mark Username  2524  may be prepended with @  2526 . 
     The present disclosure makes it possible for users to search for one another by 4Emoji. Said another way, 4Emoji are a concise, precise way to search for users in a directory database. 
       FIG. 26  illustrates Lookup Apps and Systems  2600  where User  2602 - 1  has Lookup App(s)  2604 - 1  which connects via Network(s)  2608  to Context Systems  2610 , Pathway Systems  2612 , Lookup Systems  2614 , Social Media Systems  2620 , Blockchain  2622 , and Resources  2624 . User  2602 - 2  has Lookup App(s)  2604 - 2 , and User  2602 -N has Lookup App(s)  2604 -N. The ellipse at  2606  between Lookup App(s)  2604 - 2  and Lookup App(s)  2604 -N illustrates that there are N number of lookup apps for every user on the network. Every lookup app is connected via Network(s)  2608  to Context Systems  2610 , Pathway Systems  2612 , Lookup Systems  2614 , Social Media Systems  2620 , Blockchain  2622 , and Resources  2624 . 
     Lookup Apps and Systems  2600  is a systems architecture diagram comprised of various networks, apps and systems. Resources  2624  illustrates systems and resources such as web servers, APIs, CDNs, etc. available through Networks(s)  2608 . User  2602 - 1 , User  2602 - 2 , and User  2602 -N illustrate users interfacing with their respective Lookup App(s)  2604 - 1 , Lookup App(s)  2604 - 2 , and Lookup App(s)  2604 -N. These apps interface with back office systems, namely Content Systems  2610 , Pathway Systems  2612 , and Lookup Systems  2614 . In one embodiment, the apps and back office systems interact over Network(s)  2608 . In another embodiment, the back-office systems are integrated in the app. Both embodiments interface with Social Media Systems  2620 , Blockchain  2622 , and Resources  2624  over Network(s)  2608 . 
       FIG. 27  illustrates Context Item  2700 , Context Item  2710 , Context Item Dictionary  2720 , Context Item Dictionary  2730 , and Context Expression Enumeration  2740 . Context Item  2700  contains Context Key  2702  and Context Value  2704 . Context Item  2710  contains Generated Context Key  2712  and Context Value  2704 . Context Item Dictionary 5    2720  contains Context Key  2702  and Context Value  2704 . Context Item Dictionary  2730  contains Generated Context Key  2712  and Context Value  2704 . Context Expression Enumeration  2740  contains None  2742 , User Template  2744 , Generated Template  2746 , Simple  2748 , Complex  2750 , and Prefix  2752 .  5  “Dictionary Class (System.Collections.Generic).” Technical Documentation, API, and Code Examples|Microsoft Docs, docs.microsoft.com/en-us/dotnet/api/system.collections.generic.dictionary-2. 
     In one embodiment, Context Key  2702  and Context Value  2704  illustrate a string 6  derived from one or more characters 78 . In another embodiment, Context Key  2702  and Context Value  2704  illustrate Emoji  302  in  FIG. 3 . Generated Context Key  2712  illustrates a generated string. Context Item  2700  and Context Item  2710  represent Key-Value Pairs. 9    6  “String Class (System).” Technical Documentation, API, and Code Examples|Microsoft Docs, docs.microsoft.com/en-us/dotnet/api/system.string. 7  “Glossary of Unicode Terms.” Unicode Consortium, unicode.org/glossary/#character. 8  “Character encoding.” Wikipedia, Wikimedia Foundation, 2019, en.wikipedia.org/wiki/Character_encoding. 9  “KeyValuePair Struct (System.Collections.Generic).” Microsoft Docs, docs.microsoft.com/en-us/dotnet/api/system.collections.generic.keyvaluepair-2?view=netcore-3.1. 
     Context Item Dictionary  2720  is a dictionary of Context Key  2702  and Context Value  2704  KVP. Context Item Dictionary  2730  is a dictionary of Generated Context Key  2712  and Context Value  2704  KVP. Context items in Context Item Dictionary  2720  and Context Item Dictionary  2730  represent Input Key-Value Pairs (KVP)  204  as input into Context Identifier Algorithm  200  in  FIG. 2 ; furthermore, when Emoji  302  in  FIG. 3  is utilized for Context Key  2702  and Context Value  2704 , Emoji Id  304 , Emoji Code  310  or Emoji Context Id  312  from  FIG. 3  may be used as values for KVP. Context Expression Enumeration  2740  illustrates an enumeration type 10  which could be extended and is not limited to the exemplified elements.  10  “Enumeration Types—C# Reference.” Technical Documentation, API, and Code Examples|Microsoft Docs, docs.microsoft.com/en-us/dotnet/csharp/language-reference/builtin-types/enum. 
       FIG. 28  illustrates Context Expression  2800 , Context Expression Collection 11    2840 , and Context Expression Collections  2850 . Context Expression  2800  contains Context Expression Id  2802 , Context Item Dictionary  2720 , Context Item Key Collection 12    2806 , Context Expression Key Text  2808 , Context Expression Text  2810 , Context Expression Type  2812 , Context Item Value Collection 13    2814 , Context Expression Value Text  2816 , and Context Expression Prefix Dictionary  2818 . Context Expression Collection  2840  contains Context Expression  2800 . Context Expression Collections  2850  contains Context Expression Collection  2840 .  11  “Collections (C#).” Technical Documentation, API, and Code Examples|Microsoft Docs, docs.microsoft.com/en-us/dotnet/csharp/programming-guide/concepts/collections. 12  “Dictionary.KeyCollection Class (System.Collections.Generic).” docs.microsoft.com/en-us/dotnet/api/system.collections.generic.dictionary-2.keycollection. 13  “Dictionary.ValueCollection Class (System.Collections.Generic).” docs.microsoft.com/en-us/dotnet/api/system.collections.generic.dictionary-2.valuecollection. 
     A “subject” is defined as “that which forms or is chosen as the matter of thought, consideration, or inquiry.” 14  A “context expression” is defined as an expression 15  that conveys 16  attributive 17  context 1819  for a subject. An “actionable context expression” is defined as an expression that conveys actionable 20  attributive context for a subject. When the phrase “context expression” is used, it refers to the context expression and actionable context expression definitions specified here in addition to Context Expression  2800 , its constituent parts, and any derivatives thereof.  14  “subject, n.” OED Online, Oxford University Press, March 2020, www.oed.com/viewdictionaryentry/Entry/192686. Accessed 23 Apr. 2020. 15  “expression, n.” OED Online, Oxford University Press, December 2019, www.oed.com/view/Entry/66747. Accessed 18 Jan. 2020. 16  “convey, v.1.” OED Online, Oxford University Press, March 2020, www.oed.com/viewdictionaryentry/Entry/40805. Accessed 16 Apr. 2020. 17  “attributive, adj. and n.” OED Online, Oxford University Press, March 2020, www.oed.com/view/Entry/12939. Accessed 16 Apr. 2020. 18  “context, n.” OED Online, Oxford University Press, December 2019, www.oed.com/view/Entry/40207. Accessed 18 Jan. 2020. 19  “Context.” Literary Devices, 29 Dec. 2016, literarydevices.net/context. Accessed 18 Jan. 2020. 20  “actionable, adj.” OED Online, Oxford University Press, March 2020, www.oed.com/view/Entry/1941. Accessed 16 Apr. 2020. 
     Context Expression Id  2802  illustrates an identifier of Context Expression  2800 . In one embodiment, Context Item Dictionary  2720  is a dictionary of Context Item  2700  in  FIG. 27  that make up the constituent elements or parts of a context expression. In another embodiment, Context Item Dictionary  2730  in  FIG. 27  is a dictionary of Context Item  2710  in  FIG. 27  that make up the constituent elements or parts of a context expression and is used in place of Context Item Dictionary  2720 . The phrase “context item dictionary” refers to both Context Item Dictionary  2720  and Context Item Dictionary  2730 . 
     Context Item Key Collection  2806  is a collection of keys in a context item dictionary. Context Expression Key Text  2808  is a string representation of the keys in Context Expression Key Collection  2806 . Context Expression Text  2810  is a string representation of the KVP in content item dictionary. Context Expression Type  2812  is the type of context expression (an instance of Context Expression Enumeration  2740  in  FIG. 27 ). Context Item Value Collection  2814  is a collection of the values in content item dictionary. Context Expression Value Text  2816  is a string representation of the values in Context Item Value Collection  2814 . Context Expression Prefix Dictionary  2818  is a dictionary that contains one or more Emoji  302  in  FIG. 3  or its derived types. The key for each KVP in Context Expression Prefix Dictionary  2818  is auto generated using integers; the keys starting with one and incremented by one for each KVP added to the dictionary. 
     Context Expression Collection  2840  is a collection type used to work with one or more Context Expression  2800 , and Context Expression Collections  2850  is a collection type used to work with one or more Context Expression Collection  2840 . 
       FIG. 29  illustrates Context Expression Module Manager  2900  which is composed of Context Expression Blockchain Module  2902 , Context Expression Build Module  2904 , Context Expression Concatenation Module  2906 , Context Expression Machine Learning Module  2908 , Context Expression Mapping Module  2910 , Context Expression Parsing Module  2912 , Context Expression Rules Module  2914 , Context Expression Settings Module  2916 , Context Expression Template Module  2918 , and Context Expression Validation Module  2920 . Context Expression Module Manager  2900  is connected to Context Identifier Algorithm  200 , Context Expression Storage  2920 , and Context Expression Module Manager Storage  2922 . 
     Context Expression Module Manager  2900  illustrates the functionality and logic to manage context expressions, modules, and algorithms. It illustrates modular functionality to build, validate, parse, concatenate, and map context expressions. It exemplifies the modular functionality to create, read, update, and delete data of context expressions. It also illustrates modular functionality for context expression integration with blockchain and machine learning. Finally, it exemplifies that modules also have the functionality to manage context expressions, modules, and algorithm settings. 
     Context Expression Blockchain Module  2902  illustrates functionality and logic for context expression integration with blockchain. It exemplifies functionality to create, read, update, and delete context expression blockchain data as well as the logic for context expression blockchain management. 
     Context Expression Build Module  2904  illustrates functionality and logic to build context expressions. It exemplifies functionality to create, read, update, and delete context expression data as well as the logic for context expression build management. It illustrates context expression build integration functionality for blockchain and machine learning. Context Expression Build Module  2904  also contains all the functionality and logic to build emoji (see, Emoji  302  in  FIG. 3 ) as a part of a context expression (see, Context Key  2702  and Context Value  2704  in  FIG. 27 ). It exemplifies functionality to create, read, update, and delete emoji data as part of a context expression as well as the logic for emoji build management as part of a context expression. 
     Context Expression Concatenation Module  2906  illustrates concatenation functionality and logic for context expressions. 
     Context Expression Machine Learning Module  2908  illustrates functionality and logic for context expression integration with machine learning. It exemplifies functionality to create, read, update, and delete context expression machine learning data as well as the logic for context expression machine learning management. 
     Context Expression Mapping Module  2910  illustrates mapping functionality and logic for working with context expressions. 
     Context Expression Parsing Module  2912  illustrates parsing functionality and logic for working with context expressions. Furthermore, Context Expression Parsing Module  2912  illustrates functionality for parsing query and programming language in context expressions. 
     Context Expression Rules Module  2914  illustrates functionality and logic for business rule, decision definition, and predictive modeling creation as well as utilization, notification, reporting, and management for context expressions. It exemplifies functionality to create, read, update, and delete business rule, decision definition, and predictive modeling data for context expressions. Furthermore, Context Expression Rules Module  2914  illustrates functionality for defining business rules for language inclusion and exclusion in context expressions. 
     Context Expression Settings Module  2916  exemplifies functionality and logic to create, read, update, and delete context expression settings data as well as the logic for context expression settings management. 
     Context Expression Template Module  2918  illustrates functionality and logic for template creation, utilization, notification, reporting, and management for context expressions. It exemplifies functionality to create, read, update, and delete template data for context expressions. 
     Context Expression Validation Module  2920  illustrates validation functionality and logic for working with context expressions. Furthermore, Context Expression Validation Module  2920  illustrates functionality for validating language use in context expressions. 
     Context Identifier Algorithm  200  illustrates the Context Identifier Algorithm  200  in  FIG. 2 . Context Identifier Algorithm Context Expression Storage  2920  and Context Expression Module Manager Storage  2922  illustrate data storage systems such as a database. Context Expression Module Manager  2900  and its constituent parts, as well as any derivatives thereof, may access and utilize Context Identifier Algorithm  200 , Context Expression Storage  2920  and Context Expression Module Manager Storage  2922 . 
       FIG. 30  illustrates Context Expression Elements  3000  which is composed of Word Divider  3002 , Delimiter  3004 , Context Emoji  3006 , Action Emoji  3008 , Emoji Mark  3010 , Taxonomy Emoji  3012 , Context Expression Spacer Tuple  3020 , Context Expression Spacer Tuple  3030 , Context Expression Key-Value Tuple  3040 , Context Expression Key Tuple  3050 , and Context Expression Value Tuple  3070 . 
     Taxonomy Emoji  3012  contains Classification Emoji  3014  and Subclassification Emoji  3016 . Context Expression Spacer Tuple  3020  contains Word Divider  3002 , Delimiter  3004 , and Word Divider  3002 . Context Expression Spacer Tuple  3030  contains Delimiter  3004  and Word Divider  3002 . Context Expression Key-Value Tuple  3040  contains Context Key  2702 , Context Expression Spacer Tuple  3020 , and Context Value  2704 . Context Expression Key Tuple  3050  contains Character Key  3052 , Glyph Key  3054 , Emoji Key  3056 , Symbol Key  3058 , Punctuation Key  3060 , and Context Key  2702 . Context Expression Value Tuple  3070  contains Character Value  3072 , Glyph Value  3074 , Emoji Value  3076 , Symbol Value  3078 , Punctuation Value  3080 , and Context Value  2704 . 
     Context Expression Elements  3000  illustrate the composition and combination of context expression elements. Word Divider  3002  illustrates a word divider 21  as an element that may be used in a context expression. In one embodiment, a word divider element may be denoted as a character such as, but not limited to, a space or interpunct. In another embodiment, a word divider may be denoted by an emoji (see, Emoji  302  in  FIG. 3 ).  21  “Word divider.” Wikipedia, Wikimedia Foundation, 2019, en.wikipedia.org/Word_divider. 
     Delimiter  3004  illustrates a delimiter 22  as an element that may be used in a context expression. In one embodiment, a delimiter element may be denoted as a character such as, but not limited to, a comma, colon, right angle bracket, vertical bar, or forward slash used “for specifying the boundary between separate, independent regions in plain text.” 23  In another embodiment, a delimiter may be denoted by an emoji (see, Emoji  302  in  FIG. 3 ).  22  “Delimiter.” Wikipedia, Wikimedia Foundation, 2019, en.wikipedia.org/Delimiter. 23  “Delimiter.” Wikipedia, Wikimedia Foundation, 2019, en.wikipedia.org/Delimiter. 
     Emoji shortcodes 24  represent an emoji in text. For example, consider an “example” emoji that is a derived type of Emoji  302  in  FIG. 3 . The shortcode for “example” emoji would be “:example:”. Each emoji that represents a count noun has both a singular and plural form. Hence, the count noun form of an “example” emoji is the “examples” emoji represented by the plural form shortcode of “:examples:”.  24  “Emoji Shortcodes.” Emojipedia—Home of Emoji Meanings, emojipedia.org/shortcodes. Accessed 29 Apr. 2020. 
     Context Emoji  3006  illustrates a context emoji, derived from Emoji  302  in  FIG. 3 , that is used to convey attributive context for a context expression. The following examples illustrate derived types of Context Emoji  3006  which include, but are not limited to, advertisement, art, article, book, business, classified, culture, data, dining, directory 25 , drinking, economy, editorial, education, energy, engineering, entertainment, entry, event, food, gardening, geography, government, happening, health, history, holiday, humanities, industry, international, job, knowledge, information, language, law, letter, life, listing, locality 26 , media, math, meeting, mention, military, movie, music, nature, news, national, notice, opinion, organization, people, philosophy, Pinx, pinxit, policy, politics, property, reference, religion, research, review, result, science, showtime, society, sport, setting, social, taxonomy 27 , technology, temporality 28 , ticket, travel, variety, weather, website, wellbeing, world. Each of the context emoji illustrated here may also be represented by a shortcode (e.g. “humanities” as “:humanities:” and “book” as “:book:”) and where applicable the plural shortcode (e.g., “books” as:books:”). Context emoji may convey a context that is broad or specific. For example, consider a context emoji that conveys business in general, versus one that conveys a type of business, such as landscapers, versus one that conveys a specific business, such as a specific restaurant.  25  “directory, n.” OED Online, Oxford University Press, March 2020, www.oed.com/viewdictionaryentry/Entry/53319. Accessed 21 Apr. 2020. 26  “locality, n.” OED Online, Oxford University Press, December 2019, www.oed.com/view/Entry/109556. Accessed 7 Feb. 2020. 27  “taxonomy, n.” OED Online, Oxford University Press, December 2019, www.oed.com/viewdictionaryentry/Entry/198305. Accessed 14 Feb. 2020. 28  “temporality, n.” OED Online, Oxford University Press, March 2020, www.oed.com/view/Entry/198948. Accessed 19 Apr. 2020. 
     Action Emoji  3008  illustrates an action emoji that may be used to convey actionable attributive context for a context expression. The following examples illustrate derived types of Action Emoji  3008  which include, but are not limited to, acknowledge, add, aloud, announce, accept, apply, associate, attribute, bid, block, book, bookmark, bridge, brighten, buy, call, cancel, change, check, cite, classify, close, configure, comment, compare, compile, copy, configure, count, create, cut, decline, delete, dim, disconnect, display, divide, do, draft, edit, end, execute, file, find, forward, get, give, go, group, highlight, hold, how, if, ignore, in, inform, insert, is, invite, last, link, locate, lookup, make, map, mark, markup, mask, mention, move, multiply, next, new, note, notify, off, on, open, options, order, out, page, page-down, page-up, pause, paste, ping, pinxit, pitstop, play, previous, prompt, print, purchase, query, rate, read, recall, recommend, redial, redo, reference, reject, remove, remind, rename, reply, restrict, review, route, run, save, schedule, screencap, scroll, sell, send, set, setup, share, shortcut, show, showtime, shuffle, shutdown, skip, sort, space, start, stop, subtract, sum, switch, tab, tag, take, takeout, tell, then, trace, track, translate, turn, turn-off, turn-on, undo, update, view, what, when, where, which, while, who, why, write, zoom. Each of the action emoji illustrated here may also be represented by a shortcode (e.g. “zoom” as “:zoom:”) or where applicable by a plural shortcode (e.g., “sells” as “:sells:”). 
     Context Emoji  3006  and Action Emoji  3008  may be used as a parent types to derive other types. As an example, an emoji may be derived from Context Emoji  3006  for each of the named constants in Expression Enumeration  2740  in  FIG. 27 . 
     An exemplary embodiment is a system that enables the creation and utilization of emoji for named constants in enumerated types. 
     Emoji Mark  3010  is derived from 4Emoji Mark  340  in  FIG. 3  with the improvement of utilizing a dynamic set of emoji (see, Emoji  302  in  FIG. 3 ) where the capacity is automatically increased or decreased as required. 
     An exemplary enablement is that Emoji Mark  3010  can be utilized with less or more permutations and combinations as required, making the various exemplary embodiments disclosed herein much more versatile. Emoji Mark  3010  could be utilized for part numbers, serial numbers, model numbers, and SKUs. Emoji Mark  3010  could be utilized to represent one or more of each Context Emoji  3006 , Action Emoji  3008 , and Taxonomy Emoji  3012 , as well as Classification Emoji  3014  and Subclassification Emoji  3016 . Emoji Mark  3010  could be utilized for stickers or labels for business cards, windows, products, etc. Optical emoji recognition could be used to locate Emoji Mark  3010  to provide information about the subject of Emoji Mark  3010  as well as opportunities for engagement. Each emoji recognized in Emoji Mark  3010  could be hashed to facilitate emoji recognition. Date and/or time could be added to Emoji Mark  3010  to ascribe a temporality context. This would assist in use cases where a temporality context would inform the use of the mark for industrial purposes (e.g. perishable food inventory). 
     Emoji Mark  3010  further extends the functionality of 4Emoji Mark  340  in  FIG. 3  by providing “emoji sentiment,” that is, the functionality providing users with the ability to convey sentiment as an attributive context for a context expression. Emoji Mark Sentiment Id  358  in  FIG. 3  is the identifier of the specific sentiment conveyed by 4Emoji Mark  340 . 
     In another embodiment, Emoji Mark  3010  can be used for email addresses. The present disclosure utilizes Unicode codepoints to represent the visual emoji in an email address. An exemplary enablement is that Emoji Mark  3010  could be appended to a user&#39;s name, allowing the user to use their real name while being disambiguated from other users with the same name and email domain. Emoji shortcodes and Unicode code points illustrate emoji in the local-part 29  of the following email addresses:  29  “Email Address.” Wikipedia, the Free Encyclopedia, Wikimedia Foundation, Inc, 12 Aug. 2003, en.wikipedia.org/wiki/Email_address#Local-part. Accessed 1 May 2020. 
     Emoji Mark  3010  Email Address Example 1: 
     “william.thomson.:grinning:.:cactus:.:rocket:.:milky_way:@some_domain.com” 
     Emoji Mark  3010  Email Address Example 2: 
     “william.thomson.U+1F600.U+1F335.U+1F680.U+1F30C@some_domain.com” 
     Emoji Mark  3010  Email Address Example 3: 
     “william.thomson#:grinning::cactus::rocket::milky_way:@some_domain.com” 
     Emoji Mark  3010  Email Address Example 4: 
     “william.thomson#U+1F600U+1F335U+1F680U+1F30C@some_domain.com” 
     Emoji Mark  3010  Email Address Example 5: 
     “U+1F600U+1F335U+1F680U+1F30C@some_domain.com” 
     Emoji Mark  3010  Email Address Example 1 illustrates a user&#39;s real first name and last separated by a period. The user&#39;s full name is followed by a period and then four emoji shortcodes, each separated by a period. Emoji Mark  3010  Email Address Example 2 replaces Emoji Mark  3010  Email Address Example 1 emoji shortcode with emoji Unicode codepoints. Emoji Mark  3010  Email Address Example 3 illustrates a user&#39;s real first name and last separated by a period. The user&#39;s full name is followed by a Number sign 30 , specifically ASCII 35, and then four emoji shortcodes without separation characters. Emoji Mark  3010  Email Address Example 4 replaces Emoji Mark  3010  Email Address Example 3 emoji shortcode with emoji Unicode codepoints. Emoji Mark  3010  Email Address Example 5 illustrates an embodiment where the local-part is an Emoji Mark  3010  for an email address.  30  Peter. “ASCII Code #, Number Sign.” The Complete Table of ASCII Characters, Codes, Symbols and Signs, American Standard Code for Information Interchange, theasciicode.com.ar/ascii-printable-characters/number-sign-ascii-code-35.html. 
     An exemplary enablement is a system with the ability to utilize Emoji Mark  3010  for the local-part of an email address in whole or in part. 
     While these examples are technically valid, many email platforms and clients do not adhere to technical standards. 31  Hence, Emoji Mark  3010  Email Address Example 3 may be the only viable option. 32    31  “Email Address.” Wikipedia, the Free Encyclopedia, Wikimedia Foundation, Inc, 12 Aug. 2003, en.wikipedia.org/wiki/Email_address#Local-part. Accessed 1 May 2020. 32  Topf, Jochen. “Characters in the Local Part of an Email Address.” Jochen Topf, www.jochentopf.com/email/chars.html. 
     Taxonomy Emoji  3012  is a derived type of Context Emoji  3006 . It contains Classification Emoji  3014  and Subclassification Emoji  3016  which are also derived types of Context Emoji  3006 . Taxonomy Emoji  3012  illustrates a taxonomy emoji (see, Emoji  302  in  FIG. 3 ) that may be used to convey taxonomy as an attributive context for a context expression. In one embodiment, Taxonomy Emoji  3012  may represent one or more Classification Emoji  3014 . In another embodiment, Taxonomy Emoji  3012  may represent one or more Classification Emoji  3014  and one or more Subclassification Emoji  3016 . In another embodiment, Taxonomy Emoji  3012  may represent either one or more Classification Emoji  3014  or one or more Subclassification Emoji  3016 . 
     Classification Emoji  3014  illustrates a classification 33  emoji (see, Emoji  302  in  FIG. 3 ) that may be used to convey classification as an attributive context for a context expression. Subclassification Emoji  3016  illustrates a subclassification 34  emoji (see, Emoji  302  in  FIG. 3 ) that may be used to convey subclassification as an attributive context for a context expression.  33  “classification, n.” OED Online, Oxford University Press, December 2019, www.oed.com/viewdictionaryentry/Entry/33896. Accessed 14 Feb. 2020. 34  “sub-, prefix.” OED Online, Oxford University Press, December 2019, www.oed.com/viewdictionaryentry/Entry/192418. Accessed 24 Feb. 2020. 
     Context Expression Spacer Tuple  3020 , Context Expression Spacer Tuple  3030 , Context Expression Key-Value Tuple  3040 , Context Expression Key Tuple  3050 , and Context Expression Value Tuple  3070  utilize tuples to illustrate “a data structure that contains a sequence of elements.” 35    35  “Tuple Class (System).” Microsoft Docs, Microsoft Corporation, docs.microsoft.com/en-us/dotnet/api/system.tuple?view=netframework-4.8. 
     Context Expression Spacer Tuple  3020  and Context Expression Spacer Tuple  3030  illustrates tuple combinations of Delimiter  3004  and Word Divider  3002 . Context Expression Key-Value Tuple  3040  illustrates a KVP in combination with Word Divider  3002 , Delimiter  3004 , and Word Divider  3002 . In another embodiment, Context Key  2702  in Context Expression Key-Value Tuple  3040  may be replaced with Generated Context Key  2712  in  FIG. 27 . 
     Context Expression Key Tuple  3050  illustrates a tuple comprised of Character 36  Key  3052 , Glyph Key  3054 , Emoji Key  3056 , Symbol Key  3058 , Punctuation 3738  Key  3060 , and Context Key  2702 . In one embodiment, Character Key  3052 , Glyph Key  3054 , Emoji Key  3056 , Symbol Key  3058 , and Punctuation Key  3060  are optional. In another embodiment, all or any of Character Key  3052 , Glyph Key  3054 , Emoji Key  3056 , Symbol Key  3058 , and Punctuation Key  3060  are required. In one embodiment, one of each of Character Key  3052 , Glyph Key  3054 , Emoji Key  3056 , Symbol Key  3058 , and Punctuation Key  3060  may be utilized. In another embodiment, more than one of Character Key  3052 , Glyph Key  3054 , Emoji Key  3056 , Symbol Key  3058 , and Punctuation Key  3060  may be utilized. In one embodiment, all or any of Character Key  3052 , Glyph Key  3054 , Emoji Key  3056 , Symbol Key  3058 , and Punctuation Key  3060  must precede Context Key  2702 . In another embodiment, all or any of Character Key  3052 , Glyph Key  3054 , Emoji Key  3056 , Symbol Key  3058 , and Punctuation Key  3060  must follow Context Key  2702 . In yet another embodiment, all or any of Character Key  3052 , Glyph Key  3054 , Emoji Key  3056 , Symbol Key  3058 , and Punctuation Key  3060  may precede or follow Context Key  2702 .  36  “Character (computing).” Wikipedia, Wikimedia Foundation, en.wikipedia.org/wiki/Character_(computing). 37  “punctuation, n.” OED Online, Oxford University Press, March 2020, www.oed.com/viewdictionaryentry/Entry/154629. Accessed 16 Apr. 2020. 38  “Punctuation|Definition, History, &amp; Facts.” Encyclopedia Britannica, www.britannica.com/topic/punctuation. Accessed 16 Apr. 2020. 
     Glyphs are defined in one sense as, “an elemental symbol within an agreed set of symbols, intended to represent a readable character for the purposes of writing. Glyphs are considered to be unique marks that collectively add up to the spelling of a word or contribute to a specific meaning of what is written, with that meaning dependent on cultural and social usage.” 39  In this sense, not all glyphs (e.g., ideograms, logograms, and pictograms) are available in Unicode.  39  “Glyph.” Wikipedia, Wikimedia Foundation, en.wikipedia.org/wiki/Glyph. 
     Symbols are defined in one sense as, “a written character or mark used to represent something; a letter, figure, or sign conventionally standing for some object, process, etc. e.g. the figures denoting the planets, signs of the zodiac, etc. in astronomy; the letters and other characters denoting elements, etc. in chemistry, quantities, operations, etc. in mathematics, the faces of a crystal in crystallography.” 40  Other examples of symbols include but are not limited to typographical symbols 41  and orthographic ligatures. 42  The Wikipedia page for Category:Symbols states that a “category is for symbols in the sense of marks, signs, or words that indicate, signify, or are understood as representing an idea, object, or relationship.” 43  It offers many other examples of symbols including seals of cities and towns or municipal heraldry or municipal coats of arms, armorial of the nations, professional symbols, ISO Standard for safety symbols, media control symbols, and mathematical symbols. In these senses, not all symbols are available in Unicode.  40  “symbol, n.1.” OED Online, Oxford University Press, March 2020, www.oed.com/view/Entry/196197. Accessed 16 Apr. 2020. 41  “List of typographical symbols.” Wikipedia, Wikimedia Foundation, en.wikipedia.org/wiki/List_of_typographical_symbols. 42  “Orthographic ligature.” Wikipedia, Wikimedia Foundation, en.wikipedia.org/wiki/Orthographic_ligature. 43  “Category:Symbols.” Wikipedia, Wikimedia Foundation, 2019, en.wikipedia.org/wiki/Category:Symbols. 
     At the time of this application, the Full Emoji List, v13.0 44 , has a mere 3292 45  emoji based on their subtotal count that, “is the total without the typical dups or components, so it is a better reflection of what people would typically see on emoji keyboards or palettes.” 46  As with glyphs and symbols, not all emoji are available in Unicode, especially when kaomoji—Japanese emoticons—are taken into consideration.  44  “Full Emoji List, V13.0.” Index, unicode.org/emoji/charts/full-emoji-list.html. 45  “Emoji Counts, V13.0.” Index, unicode.org/emoji/charts/emoji-counts.html. 46  “Unicode Emoji Chart Format.” Index, unicode.org/emoji/format.html#col-totals. 
     Context Expression Build Module  2904  in  FIG. 29 , in combination with Emoji  302  in  FIG. 3 , is used to create, store, manage, and utilize glyphs, symbols and emoji that are not part of Unicode and which are user generated, third party generated, crowd source generated, etc. 
     Context Expression Value Tuple  3070  illustrates a tuple comprised of Character Value  3072 , Glyph Value  3074 , Emoji Value  3076 , Symbol Value  3078 , Punctuation Value  3080 , and Context Value  2704 . In one embodiment, Character Value  3072 , Glyph Value  3074 , Emoji Value  3076 , Symbol Value  3078 , Punctuation Value  3080  are optional. In another embodiment, all or any of Character Value  3072 , Glyph Value  3074 , Emoji Value  3076 , Symbol Value  3078 , and Punctuation Value  3080  are required. In one embodiment, one of each of Character Value  3072 , Glyph Value  3074 , Emoji Value  3076 , Symbol Value  3078 , and Punctuation Value  3080  may be utilized. In another embodiment, more than one of each of Character Value  3072 , Glyph Value  3074 , Emoji Value  3076 , Symbol Value  3078 , and Punctuation Value  3080  may be utilized. In one embodiment, all or any of Character Value  3072 , Glyph Value  3074 , Emoji Value  3076 , Symbol Value  3078 , and Punctuation Value  3080  must precede Context Value  2704 . In another embodiment, all or any of Character Value  3072 , Glyph Value  3074 , Emoji Value  3076 , Symbol Value  3078 , and Punctuation Value  3080  must follow Context Value  2704 . In yet another embodiment, all or any of Character Value  3072 , Glyph Value  3074 , Emoji Value  3076 , Symbol Value  3078 , and Punctuation Value  3080  may precede or follow Context Value  2704 . 
     In one embodiment, Context Key  2702  in Context Expression Key Tuple  3050  and Context Value  2704  in Context Expression Value Tuple  3070  illustrate strings. In another embodiment, Context Key  2702  in Context Expression Key Tuple  3050  and Context Value  2704  in Context Expression Value Tuple  3070  illustrate an Emoji  302  in  FIG. 3 ; they do not illustrate the following emoji and any of their derivatives: Context Emoji  3006 , Action Emoji  3008 , Taxonomy Emoji  3012 , Classification Emoji  3014 , and Subclassification Emoji  3016 . 
     Emoji Key  3056  in Context Expression Key Tuple  3050  and Emoji Value  3076  in Context Expression Value Tuple  3070  illustrate the following emoji and their derivatives: Context Emoji  3006 , Action Emoji  3008 , Taxonomy Emoji  3012 , Classification Emoji  3014 , and Subclassification Emoji  3016 . 
     An exemplary enablement is a system to fill the gap where glyphs, symbols, and emoji are not part of Unicode. 
     Another exemplary enablement is a system to create and utilize context expressions, especially the conveyance of attributions: context, action, taxonomy, etc. 
     Another exemplary enablement is a system to combine Unicode with newly created emoji based on glyphs, symbols, and emoji. 
     Another exemplary enablement is a system to create and utilize context expression key tuples and context expression value tuples based on graphemes. 
     Another exemplary enablement is a system to capture human generated context and transform it into a machine codable format for computing. 
     An aspect of the present disclosure&#39;s novelty lay in its ability to capture human context in a unified way for storage, computation, and algorithmic utilization. 
       FIG. 31  illustrates Context Expressions  3100  which is composed of Context Expression Tuple  3110 , Context Expression Tuple  3120 , and Context Expression Tuple  3130 . Context Expression Tuple  3110  contains Context Key  2702 - 1 , Context Expression Spacer Tuple  3030 - 1 , Context Key  2702 - 2 , Context Expression Spacer Tuple  3030 - 2 , Context Key  2702 - 3 , Context Expression Spacer Tuple  3030 -N, and Context Key  2702 -N. The ellipse at  3112  illustrates that there are N number of context keys and context expression spacer tuples in Context Expression Tuple  3110 . Context Expression Tuple  3120  contains Context Value  2704 - 1 , Context Expression Spacer Tuple  3030 - 1 , Context Value  2704 - 2 , Context Expression Spacer Tuple  3030 - 2 , Context Value  2704 - 3 , Context Expression Spacer Tuple  3030 -N, and Context Value  2704 -N. The ellipse at  3122  illustrates that there are N number of context values and context expression spacer tuples in Context Expression Tuple  3120 . Context Expression Tuple  3130  contains Context Expression Key-Value Tuple  3040 - 1 , Context Expression Spacer Tuple  3020 - 1 , Context Expression Key-Value Tuple  3040 - 2 , Context Expression Spacer Tuple  3020 - 2 , Context Expression Key-Value Tuple  3040 - 3 , Context Expression Spacer Tuple  3020 -N, and Context Expression Key-Value Tuple  3040 -N. The ellipse at  3132  illustrates that there are N number of context expression key-value tuples and context expression spacer tuples in Context Expression Tuple  3130 . 
     In another embodiment of Context Expression Tuple  3110 , Context Key  2702  may be replaced with Generated Context Key  2712  in  FIG. 27 . In yet another embodiment of Context Expression Tuple  3110 , Context Expression Spacer Tuple  3030  may be replaced with Context Expression Spacer Tuple  3020  in  FIG. 30 . In another embodiment of Context Expression Tuple  3120 , Context Expression Spacer Tuple  3030  may be replaced with Context Expression Spacer Tuple  3020  in  FIG. 30 . In another embodiment of Context Expression Tuple  3130 , Context Expression Spacer Tuple  3020  may be replaced with Context Expression Spacer Tuple  3030  in  FIG. 30 . 
     Context Expression  3100  enables composition 47  and decomposition 48  of context expressions. “Composition” is defined as the due arrangement of context expression elements into context expressions. “Decomposition” is defined as the decomposing or separation of a context expression into its context expression elements. Context Expression Tuple  3110 , Context Expression Tuple  3120 , and Context Expression Tuple  3130  in combination with Context Expression Module Manager  2900 , Context Expression Storage  2920 , and Context Expression Module Manager Storage  2922  in  FIG. 29 , plus Context Identifier Algorithm  200  in  FIG. 2 , enable composition and decomposition.  47  “composition, n.” OED Online, Oxford University Press, March 2020, www.oed.com/viewdictionaryentry/Entry/37795. Accessed 27 Apr. 2020. 48  “decomposition, n.” OED Online, Oxford University Press, March 2020, www.oed.com/viewdictionaryentry/Entry/48355. Accessed 27 Apr. 2020. 
     In one embodiment, users provide a string to the system to compose context expressions utilizing Context Expression Tuple  3110 . In this embodiment, the system utilizes Context Expression Build Module  2904  in  FIG. 29  to create a Context Expression  2800  in  FIG. 28 , generate and add a unique identifier to Context Expression Id  2802  in  FIG. 28 , set the Context Expression Type  2812  in  FIG. 28  to User Template  2744  in  FIG. 27 , and add the string to Context Expression Text  2810  in  FIG. 28 . Context Expression Build Module  2904  then utilizes Context Expression Rules Module  2914  and Context Expression Validation Module  2920  in  FIG. 29  for context expression rule and definition validation. Context Expression Build Module  2904  then utilizes Context Expression Parsing Module  2912  and Context Expression Mapping Module  2910  in  FIG. 29  to parse and map Context Expression Text  2810 . 
     For each “key” in the set of Context Key  2702 - 1 , Context Key  2702 - 2 , Context Key  2702 - 3 , to Context Key  2702 -N, a new Context Item  2710  in  FIG. 27  is created. Context Expression Build Module  2904  in  FIG. 29  then generates an incremental integer which is assigned to Generated Context Key  2712  in Context Item  2710 , and the “key” is assigned to the Context Value  2704  in Context Item  2710 . Context Expression Build Module  2904  then adds Context Item  2710  to Context Item Dictionary  2730  in  FIG. 28 . 
     In another embodiment, users provide two or more strings to the system to compose context expressions in part utilizing Context Expression Tuple  3110 . In this embodiment, the system utilizes Context Expression Build Module  2904  in  FIG. 29  to create a Context Expression  2800  in  FIG. 28 , generate and add a unique identifier to Context Expression Id  2802  in  FIG. 28 , set the Context Expression Type  2812  in  FIG. 28  to User Template  2744  in  FIG. 27 . For each string provided, Context Expression Build Module  2904  then utilizes Context Expression Concatenation Module  2906  in  FIG. 29  to build a single string that it then adds to Context Expression Text  2810  in  FIG. 28 . Context Expression Build Module  2904  then utilizes Context Expression Rules Module  2914  and Context Expression Validation Module  2918  in  FIG. 29  for context expression rule and definition validation. Context Expression Build Module  2904  then utilizes Context Expression Parsing Module  2912  and Context Expression Mapping Module  2910  in  FIG. 29  to parse and map Context Expression Text  2810 . 
     For each “key” in the set of Context Key  2702 - 1 , Context Key  2702 - 2 , Context Key  2702 - 3 , to Context Key  2702 -N, a new Context Item  2710  in  FIG. 27  is created. Context Expression Build Module  2904  in  FIG. 29  then generates an incremental integer which is assigned to Generated Context Key  2712  in Context Item  2710 , and the “key” is assigned to the Context Value  2704  in Context Item  2710 . Context Expression Build Module  2904  then adds the Context Item  2710  to Context Item Dictionary  2730  in  FIG. 28 . 
     As a point of clarity and disambiguation for both embodiments above, each Context Key  2702  in Content Expression Tuple  3110  is stored as a Context Value  2704  in each Context Item  2710  in Context Item Dictionary  2730  in  FIG. 28 . This enables the system to store the position of each Context Key  2702  in Context Expression Tuple  3110 . 
     In one embodiment, users provide a string to the system to compose context expressions utilizing Context Expression Tuple  3120 . In this embodiment, the system utilizes Context Expression Build Module  2904  in  FIG. 29  to create a Context Expression  2800  in  FIG. 28 , generate and add a unique identifier to Context Expression Id  2802  in  FIG. 28 , set the Context Expression Type  2812  in  FIG. 28  to Simple  2748  in  FIG. 27 , and add the string to Context Expression Text  2810  in  FIG. 28 . Context Expression Build Module  2904  then utilizes Context Expression Rules Module  2914  and Context Expression Validation Module  2918  in  FIG. 29  for context expression rule and definition validation. Context Expression Build Module  2904  then utilizes Context Expression Parsing Module  2912  and Context Expression Mapping Module  2910  in  FIG. 29  to parse and map Context Expression Text  2810 . 
     For each “value” in the set of Context Value  2704 - 1 , Context Value  2704 - 2 , Context Value  2704 - 3 , to Context Value  2704 -N, a new Context Item  2710  in  FIG. 27  is created. Context Expression Build Module  2904  in  FIG. 29  then generates an incremental integer which is assigned to Generated Context Key  2712  in Context Item  2710 , and the “value” is assigned to Context Value  2704  in Context Item  2710 . Context Expression Build Module  2904  then adds the Context Item  2710  to Context Item Dictionary  2730  in  FIG. 28 . 
     In another embodiment, users provide two or more strings to the system to compose context expressions in part utilizing Context Expression Tuple  3120 . In this embodiment, the system utilizes Context Expression Build Module  2904  in  FIG. 29  to create a Context Expression  2800  in  FIG. 28 , generate and add a unique identifier to Context Expression Id  2802  in  FIG. 28 , set the Context Expression Type  2812  in FIG.  28  to Simple  2748  in  FIG. 27 . For each string provided, Context Expression Build Module  2904  then utilizes Context Expression Concatenation Module  2906  in  FIG. 29  to build a single string that it then adds to Context Expression Text  2810  in  FIG. 28 . Context Expression Build Module  2904  then utilizes Context Expression Rules Module  2914  and Context Expression Validation Module  2918  in  FIG. 29  for context expression rule and definition validation. Context Expression Build Module  2904  then utilizes Context Expression Parsing Module  2912  and Context Expression Mapping Module  2910  in  FIG. 29  to parse and map Context Expression Text  2810 . 
     For each “value” in the set of Context Value  2704 - 1 , Context Value  2704 - 2 , Context Value  2704 - 3 , to Context Value  2704 -N, a new Context Item  2710  in  FIG. 27  is created. Context Expression Build Module  2904  in  FIG. 29  then generates an incremental integer which is assigned to Generated Context Key  2712  in Context Item  2710 , and the “value” is assigned to the Context Value  2704  in Context Item  2710 . Context Expression Build Module  2904  then adds the Context Item  2710  to Context Item Dictionary  2730  in  FIG. 28 . 
     In another embodiment, users provide a string to the system to compose context expressions utilizing Context Expression Tuple  3130 . In this embodiment, the system utilizes Context Expression Build Module  2904  in  FIG. 29  to create a Context Expression  2800  in  FIG. 28 , generate and add a unique identifier to Context Expression Id  2802  in  FIG. 28 , set the Context Expression Type  2812  in  FIG. 28  to Complex  2750  in  FIG. 27 , and add the string to Context Expression Text  2810  in  FIG. 28 . Context Expression Build Module  2904  then utilizes Context Expression Rules Module  2914  and Context Expression Validation Module  2918  in  FIG. 29  for context expression rule and definition validation. Context Expression Build Module  2904  then utilizes Context Expression Parsing Module  2912  and Context Expression Mapping Module  2910  in  FIG. 29  to parse and map Context Expression Text  2810 . 
     For each KVP in the set of Context Expression Key-Value Tuple  3040 - 1 , Context Expression Key-Value Tuple  3040 - 2 , Context Expression Key-Value Tuple  3040 - 3 , to Context Expression Key-Value Tuple  3040 -N, a new Context Item  2700  in  FIG. 27  is created. Context Expression Build Module  2904  in  FIG. 29  then assigns the “key” in the KVP to the Context Key  2702  in Context Item  2700  and assigns the “value” in the KVP to the Context Value  2704  in Context Item  2700 . Context Expression Build Module  2904  then adds the Context Item  2700  to Context Item Dictionary  2720  in  FIG. 28 . 
     In another embodiment, the system will auto-compose context expressions by utilizing Context Expression Template Module  2918  in  FIG. 29  to generate Context Expression Tuple  3110  and Context Expression Tuple  3120  from Context Expression Tuple  3130 . Context Expression Template Module  2918  then utilizes the embodiments detailed above to process Context Expression Tuple  3110  and Context Expression Tuple  3120  to compose their respective context expressions. When processing Context Expression Tuple  3110  however, the Context Expression Type  2812  in  FIG. 28  is set to Generated Template  2746  in  FIG. 27 . 
     The context expressions composed utilizing Context Expression Tuple  3110 , and with Context Expression Types  2812  in  FIG. 28  of User Template  2744  or Generated Template  2746  in  FIG. 27 , can be used as context expression templates for composing Context Expression Tuple  3120  and Context Expression Tuple  3130 . Utilizing context expression templates, a context key may be used as a “prompt” for context values in Context Expression Tuple  3120 , and it may be used as a “key” in a Context Expression Key-Value Tuple  3040  in Context Expression Tuple  3130 . “Prompt” is used here to mean that a system could use a “key” as a way to prompt a user to input a “value.” Additionally, the Context Expression Validation Module  2918  in  FIG. 29  checks for duplicates which may or may not be valid during composition. 
     An exemplary enablement is a system that enables users to provide a string to compose a context expression for any subject. 
     Another exemplary enablement is a system that enables users to provide two or more strings to compose a context expression for any subject. 
     Another exemplary enablement is a system that enables many users to compose many context expressions for any subject. 
     Another exemplary enablement is a system that enables users to create context expression templates. 
     Another exemplary enablement is a system that auto generates context expression templates. 
     Another exemplary enablement is a system that enables users to create context expressions from context expression templates. 
     In one embodiment, context expressions can be derived from “local colloquialisms.” Local colloquialisms are defined as ordinary or familiar language conventions that convey inherent social knowledge derived from tacit experiential learning within a communal localization. These local colloquialisms are shared by all members of a community and reflect a particular local characteristic featured as a common reference point. Innate social knowledge serves as a social context connecting members. It is learned informally in the spoken and written word, through interaction with community members over time. In another embodiment, context expressions can be derived from any language convention. 495051    49  “Convention (Stanford Encyclopedia of Philosophy).” Stanford Encyclopedia of Philosophy, plato.stanford.edu/entries/convention/#ConLan. 50  “Standard English Conventions: The Craft of Language.” The SAT Suite of Assessments—The College Board, collegereadiness.collegeboard.org/pdf/official-sat-study-guide-writing-language-standard-english-conventions.pdf. 51  “Language Conventions.” Home, education.nsw.gov.au/teaching-and-learning/student-assessment/smart-teaching-strategies/literacy/language-conventions. 
     An exemplary enablement is a system that enables users to compose, as well as update, delete, own, share, publish, manage, monetize, assign access rights to, and utilize one or more context expressions. 
     The following examples for Context Expressions  3100  illustrate context expression composition from one or more users for the subject Frank Pepe Pizza Napoletana at 157 Wooster Street, New Haven, Conn. 06511. 
     The following examples illustrate context expressions for Context Expression Tuple  910 : 
     Context Expression Tuple  3110  Example 1: 
     “Restaurant, Street, City, State” 
     Context Expression Tuple  3110  Example 2: 
     “Restaurant, City, State” 
     Context Expression Tuple  3110  Example 3: 
     “Restaurant, City” 
     Context Expression Tuple  3110  Example 4: 
     “Restaurant, Neighborhood” 
     Context Expression Tuple  3110  Example 5: 
     “Classification, City” 
     Context Expression Tuple  3110  Example 6: 
     “Classification, Neighborhood” 
     Context Expression Tuple  3110  Example 7: 
     “Classification, Subclassification, Neighborhood” 
     The following examples illustrate context expressions for Context Expression Tuple  920 : 
     Context Expression Tuple  3120  Example 1: 
     “Pepe&#39;s Pizza, Wooster St, New Haven, Conn.” 
     Context Expression Tuple  3120  Example 2: 
     “Pepe&#39;s Pizza, New Haven, Conn.” 
     Context Expression Tuple  3120  Example 3: 
     “Frank Pepe Pizzeria Napoletana, New Haven, Conn.” 
     Context Expression Tuple  3120  Example 4: 
     “Coal Fired Pizza, New Haven, Conn.” 
     Context Expression Tuple  3120  Example 5: 
     “Coal Fired Pizza, Wooster Square” 
     Context Expression Tuple  3120  Example 6: 
     “Pizza, New Haven” 
     Context Expression Tuple  3120  Example 7: 
     “Pizza, Wooster Square” 
     Context Expression Tuple  3120  Example 8: 
     “Restaurant, New Haven” 
     Context Expression Tuple  3120  Example 9: 
     “Restaurant, Wooster Square” 
     Context Expression Tuple  3120  Example 10: 
     “Restaurant, Pizza, Wooster Square” 
     The following examples illustrate context expressions for Context Expression Tuple  930 : 
     Context Expression Tuple  3130  Example 1: 
     “Restaurant: Pepe&#39;s Pizza|Street: Wooster St|City: New Haven|State: CT” 
     Context Expression Tuple  3130  Example 2: 
     “Restaurant: Pepe&#39;s Pizza|City: New Haven|State: CT” 
     Context Expression Tuple  3130  Example 3: 
     “Restaurant: Frank Pepe Pizzeria Napoletana|City: New Haven|State: CT” 
     Context Expression Tuple  3130  Example 4: 
     “Restaurant: Coal Fired Pizza|City: New Haven|State: CT” 
     Context Expression Tuple  3130  Example 5: 
     “Restaurant: Coal Fired Pizza|Neighborhood: Wooster Square” 
     Context Expression Tuple  3130  Example 6: 
     “Restaurant: Pizza|City: New Haven” 
     Context Expression Tuple  3130  Example 7: 
     “Restaurant: Pizza|Neighborhood: Wooster Square” 
     Context Expression Tuple  3130  Example 8: 
     “Classification: Restaurant|City: New Haven” 
     Context Expression Tuple  3130  Example 9: 
     “Classification: Restaurant|Neighborhood: Wooster Square” 
     Context Expression Tuple  3130  Example 10: 
     “Classification: Restaurant|Subclassification: Pizza|Neighborhood: Wooster Square” 
     The above examples illustrate twenty-seven context expressions composed by one or more users to convey attributive context for the subject, Pepe&#39;s Pizza. Several convey attributive contexts of an inferred taxonomy (e.g., Restaurant) combined with locality (e.g., Street, City, State, Neighborhood), while others, convey attributive contexts of classification (e.g., Restaurant), subclassification (e.g., Pizza) and locality (e.g., City, Neighborhood). Most examples did not convey the name of the restaurant (e.g., “Pepe&#39;s Pizza” or “Frank Pepe Pizzeria Napoletana”). However, every single one of these context expressions is a valid conveyance of attributive context for the subject. 
     An exemplary enablement is system that enables the conveyance of one or more attributive context for a subject. 
     Another exemplary enablement is system that enables the conveyance of one or more attributive actionable context for a subject. 
     Another exemplary enablement is system that enables the conveyance of one or more attributive taxonomy context for a subject. 
     Another exemplary enablement is a system that captures crowd sourced conveyance of attributive context for a context expression. 
     Another exemplary enablement is a system that enables users to attribute simple and complex taxonomies when composing context expressions. 
     Another exemplary enablement is a system that infers taxonomy where users utilize templates when creating context expressions. 
     Another exemplary enablement is a system that enables taxonomy aggregation from crowd sourced taxonomies for data analytics and machine learning. 
     Another exemplary enablement is a system that matches data analytics and machine learning results for taxonomies with user profile data to create a marketing profile. 
     Another exemplary enablement is a system that matches data analytics and machine learning results to suggest context expressions to users while they create or utilize context expressions. 
     Another exemplary enablement is a system that enables users to store their context expressions in a blockchain. 
     Another exemplary enablement is a system that enables users to monetize their context expressions through a blockchain. 
     In another embodiment, Context Expression Module Manager  2900  in  FIG. 29  utilizes Context Identifier Algorithm  200  in  FIG. 2  to create context identifiers from Context Expression  2800  in  FIG. 28 . Context Item Dictionary  2720 , Context Item Key Collection  2806 , and Context Item Value Collection  2814  in  FIG. 28  can be used to generate key-value pairs for input into Context Identifier Algorithm  200  in  FIG. 2  to create context identifiers. 
     An exemplary enablement is a system that produces a context identifier for context expressions. 
       FIG. 32  illustrates Context Expressions  3200  which is composed of Restaurant Emoji  3201 , Menu Item Emoji  3202 , Pizza Emoji  3203 , White Clam Pizza Emoji  3204 , New Haven Emoji  3205 , Cinema Emoji  3206 , Star Wars Emoji  3207 , Showtimes Emoji  3208 , Locality Emoji  3209 , Temporality Emoji  3210 , Context Expression Key-Value Tuple  3220 , Context Expression Tuple  3230 , Context Expression Key-Value Tuple  3240 , and Context Expression Tuple  3250 . Context Expression Key-Value Tuple  3220  contains Context Expression Key Tuple  3050  and Context Expression Value Tuple  3070 . Context Expression Tuple  3230  contains Context Expression Key-Value Tuple  3220 - 1 , Context Expression Key-Value Tuple  3220 - 2 , and Context Expression Key-Value Tuple  3220 -N. The ellipse at  3232  illustrates that there are N number of context expression key-value tuples in Context Expression Tuple  3230 . Context Expression Key-Value Tuple  3240  contains Context Expression Key Tuple  3050 , Context Expression Spacer Tuple  3020 , and Context Expression Value Tuple  3070 . Context Expression Tuple  3250  contains Context Expression Key-Value Tuple  3240 - 1 , Context Expression Spacer Tuple  3020 - 1 , Context Expression Key-Value Tuple  3240 - 2 , Context Expression Spacer Tuple  3020 - 2 , Context Expression Key-Value Tuple  3240 - 3 , Context Expression Spacer Tuple  3020 -N, and Context Expression Key Value Tuple  3240 -N. The ellipse at  3252  illustrates that there are N number of context expression key-value tuples and context expression spacer tuples in Context Expression Tuple  3250 . 
     Context Expression Tuple  3250  illustrates a tuple comprised of “N” Context Expression Key-Value Tuple  3240  and Context Expression Spacer Tuple  3020  in sequence. Context Expression Tuple  3250  utilizes Character Key  3052 , Glyph Key  3054 , Emoji Key  3056 , Symbol Key  3058 , Punctuation Key  3060 , and Context Key  2702  in  FIG. 30  to produce complex context keys as well as Character Value  3072 , Glyph Value  3074 , Emoji Value  3076 , Symbol Value  3078 , Punctuation Value  3080 , and Context Value  2704  in  FIG. 30  to produce complex context values. Context Expression Tuple  3230  illustrates a similar Context Expression Tuple  3250  sans Context Expression Spacer Tuple  3020 . In another embodiment, Context Expression Tuple  3250  is composed of Context Expression Key-Value Tuple  3220  in place of Context Expression Key-Value Tuple  3240 . 
     Context Expressions  3200  descends from Context Expressions  3100  in  FIG. 31 ; therefore, it inherits all the attributes and functionality of Context Expressions  3100 . Context Expressions  3200  extends Context Expressions  3100  by illustrating Context Expression Key-Value Tuple  3220 , Context Expression Tuple  3230 , Context Expression Key-Value Tuple  3240 , and Context Expression Tuple  3250  which may be used to compose and decompose context expressions for additional use cases and requirements. 
     Context Expressions  3200  also extends Context Expressions  3100  whereby Restaurant Emoji  3201  illustrates a restaurant emoji and is represented by the emoji shortcode “:restaurant:”, Menu Item Emoji  3202  illustrates a menu item emoji and is represented by the emoji shortcode “:menu_item:”, Pizza Emoji  3203  illustrates a pizza emoji and is represented by the emoji shortcode “:pizza:”, White Claim Pizza Emoji  3204  illustrates a white clam pizza emoji and is represented by the emoji shortcode “:white_clam_pizza:”, New Haven Emoji  3205  illustrates a New Haven emoji and is represented by the emoji shortcode “:new_haven:”, Cinema Emoji  3206  illustrates a cinema emoji and is represented by the emoji shortcode “:cinema:”, Star Wars Emoji  3207  illustrates a Star Wars emoji and is represented by the emoji shortcode “:star_wars:”, Showtimes Emoji  3208  illustrates a showtimes emoji and is represented by the emoji shortcode “:showtimes:”, Locality Emoji  3209  illustrates a locality emoji and is represented by the emoji shortcode “:locality:”, and Temporality Emoji  3210  illustrates a temporality emoji and is represented by the emoji shortcode “:temporality:”. In several examples in the specification, Pepe&#39;s Pizza may be represented as a Pepe&#39;s Pizza emoji with the shortcode “:pepes_pizza:”. 
     The following examples illustrate context expressions for Context Expression Tuple  3230 : 
     Context Expression Tuple  3230  Example 1: 
     :restaurant:Pizza:menu_item:White Clam Pizza:locality:New Haven 
     Context Expression Tuple  3230  Example 2: 
     :restaurant::pizza::menu_item::white_clam_pizza::locality::new_haven: 
     Context Expression Tuple  3230  Example 3: 
     :cinema:Star Wars:locality:New Haven:temporality: &gt;7:00 PM 
     Context Expression Tuple  3230  Example 4: 
     :cinema:The Rise of Skywalker:locality:New Haven:temporality: &gt;7:00 PM 
     Context Expression Tuple  3230  Example 5: 
     :cinema:Skywalker:locality:New Haven:temporality: &gt;7:00 PM 
     Context Expression Tuple  3230  Example 6: 
     :cinema::star_wars::locality::new_haven::showtimes: 
     Context Expression Tuple  3230  Example 7: 
     :cinema::locality::showtimes: 
     Context Expression Tuple  3230  Example 8: 
     :star_wars::new_haven::showtimes: 
     Context Expression Tuple  3230  Examples 1 through 6 illustrate an exemplary enablement of the system that utilizes character keys, glyph keys, emoji keys, symbol keys, punctuation keys, and context keys to compose context expressions. They also illustrate an exemplary enablement of the system that utilizes character keys, glyph keys, emoji keys, symbol keys, punctuation keys, and context keys to convey attributive context for a context expression. 
     Context Expression Tuple  3230  Examples 1 through 6 illustrate an exemplary enablement of the system that utilizes character values, glyph values, emoji values, symbol values, punctuation values, and context values to compose context expressions. They also illustrate an exemplary enablement of the system that utilizes character values, glyph values, emoji values, symbol values, punctuation values, and context values to convey attributive context for a context expression. 
     Context Expression Tuple  3230  Examples 3 through 5 illustrate an exemplary enablement of the system that enables composition of context expression with the conveyance of logical attributive context. 
     Context Expression Tuple  3230  Examples 2, 6, 7, and 8 illustrate an exemplary enablement of the system that utilizes only emoji to enable the composition of context expression. 
     Context Expression Tuple  3230  Examples 2, 6, 7, and 8 illustrate an exemplary enablement of the system that utilizes only emoji to enable the composition of context expression with the conveyance of attributive context. 
     Showtimes Emoji  3208  in Context Expression Tuple  3230  Examples 6, 7, and 8 illustrates a derived type of Context Emoji  3006  and Action Emoji  3008  in  FIG. 30 . As a derivative of Action Emoji  3008 , it conveys a “Get” actionable attributive context for the context expression, and as a derivative of Context Emoji  3006 , it conveys attributive context for the context expression of “Showtimes.” In this use case, the Showtimes Emoji  3208  is both the “key,” that is, Context Expression Key Tuple  3050  and the “value,” that is, Context Expression Value Tuple  3070  in the Context Expression Key-Value Tuple  3220 . 
     Context Expression Tuple  3230  Example 6 illustrates an exemplary enablement of the system that utilizes emoji to enable the composition of context expression with the conveyance of actionable attributive context. 
     Furthermore, Context Expression Tuple  3230  Example 6 illustrates an exemplary enablement of the system that utilizes emoji to enable the composition of context expression with the conveyance of two or more attributive contexts. 
     Furthermore, Context Expression Tuple  3230  Example 6 illustrates an exemplary enablement of the system that utilizes emoji to enable the composition of context expression where an emoji may represent and embody both a key and a value in a KVP. 
     Context Expression Tuple  3230  and Context Expression Tuple  3250  are analogous to Context Expression Tuple  3130  in  FIG. 31  in terms of its utilization and relationship to Context Expression  2800  in  FIG. 28  and Context Expression Module Manager  2900  in  FIG. 29 . 
     In another embodiment, Context Expression Key-Value  3220  is composed of only Context Expression Key Tuple  3050  in  FIG. 30  (see, Context Expression Tuple  3360  in  FIG. 33 ) whereby Context Expression Tuple  3230  is analogous to Context Expression Tuple  3110  in  FIG. 31  in terms of its utilization and relationship to Context Expression  2800  in  FIG. 28  and Context Expression Module Manager  2900  in  FIG. 29 . 
     Context Expression Tuple  3230  Example 7 illustrates an exemplary enablement of the system that utilizes emoji to enable the composition of context expression where an emoji may represent and embody a key. 
     In another embodiment, Context Expression Key-Value  3220  is composed of only Context Expression Value Tuple  3070  in  FIG. 30  (see, Context Expression Tuple  3370  in  FIG. 33 ) whereby Context Expression Tuple  3230  is analogous to Context Expression Tuple  3120  in  FIG. 31  in terms of its utilization and relationship to Context Expression  2800  in  FIG. 28  and Context Expression Module Manager  2900  in  FIG. 29 . 
     Context Expression Tuple  3230  Example 8 illustrates an exemplary enablement of the system that utilizes emoji to enable the composition of context expression where an emoji may represent and embody a value. 
     The following example illustrates sentiment context expression for Context Expression Tuple  3230 : 
     Context Expression Tuple  3230  Example 9: 
     :reviews::pepes_pizza::overall::joy::food:10:service:10 
     Context Expression Tuple  3230  Example 9 illustrates an exemplary enablement of the system that utilizes emoji to enable the composition of context expression with the conveyance of sentiment as an attributive context. 
       FIG. 33  illustrates Context Expression  3300  which is composed of Query Emoji  3301 , Execute Emoji  3302 , Context Expression Tuple  3310 , Context Expression Tuple  3320 , Context Expression Tuple  3330 , Context Expression Tuple  3340 , Context Expression Tuple  3350 , Context Expression Tuple  3360 , Context Expression Tuple  3370 , Context Expression Tuple  3380 , and Context Expression Tuple  3390 . Context Expression Tuple  3310  contains Context Expression Prefix Dictionary  2818 , Word Divider  3002 , and Context Expression Tuple  3110 . Context Expression Tuple  3320  contains Context Expression Prefix Dictionary  2818 , Word Divider  3002 , and Context Expression Tuple  3120 . Context Expression Tuple  3330  contains Context Expression Prefix Dictionary  2818 , Word Divider  3002 , and Context Expression Tuple  3130 . Context Expression Tuple  3340  contains Context Expression Prefix Dictionary  2818 , Word Divider  3002 , and Context Expression Tuple  3230 . Context Expression Tuple  3350  contains Context Expression Prefix Dictionary  2818 , Word Divider  3002 , and Context Expression Tuple  3250 . Context Expression Tuple  3360  contains Context Expression Key Tuple  3050 - 1 , Context Expression Key Tuple  3050 - 2  and Context Expression Key Tuple  3050 -N. The ellipse at  3362  illustrates that there are N number of context expression key tuples in Context Expression Tuple  3360 . Context Expression Tuple  3370  contains Context Expression Value Tuple  3070 - 1 , Context Expression Value Tuple  3070 - 2 , and Context Expression Value Tuple  3070 -N. The ellipse at  3372  illustrates that there are N number of context expression value tuples in Context Expression Tuple  3370 . Context Expression Tuple  3380  contains Context Expression Prefix Dictionary  2818 , Word Divider  3002 , and Context Expression Tuple  3360 . Context Expression Tuple  3390  contains Context Expression Prefix Dictionary  2818 , Word Divider  3002 , and Context Expression Tuple  3370 . In another embodiment, Delimiter  3004  in  FIG. 30  replaces Word Divider  3002  where it is used in  FIG. 33 . 
     Context Expressions  3300  descends from Context Expressions  3200  in  FIG. 32 ; therefore, it inherits all the attributes and functionality of Context Expressions  3200 . Context Expressions  3300  extends Context Expressions  3200  by illustrating Context Expression Tuple  3310 , Context Expression Tuple  3320 , Context Expression Tuple  3330 , Context Expression Tuple  3340 , Context Expression Tuple  3350 , Context Expression Tuple  3360 , Context Expression Tuple  3370 , Context Expression Tuple  3380 , and Context Expression Tuple  3390  which may be used to compose and decompose context expressions for additional use cases and requirements. 
     Context Expressions  3300  extends previous embodiments of Context Expressions  3200  in  FIG. 32  and Context Expressions  3100  in  FIG. 31  by adding Query Emoji  3301  and Execute Emoji  3302  as well as prepending a Context Expression Prefix Dictionary  2818  followed by Word Divider  3002  (or Delimiter  3004  in  FIG. 30 ) to Context Expression Tuple  3110 , Context Expression Tuple  3120 , Context Expression Tuple  3130 , Context Expression Tuple  3230 , Context Expression Tuple  3250 , Context Expression Tuple  3360 , and Context Expression Tuple  3370 . 
     Context Expression Prefix Dictionary  2818  illustrates an emoji prefix 52  to Context Expression Tuple  3110 , Context Expression Tuple  3120 , Context Expression Tuple  3130 , Context Expression Tuple  3230 , Context Expression Tuple  3250 , Context Expression Tuple  3360 , and Context Expression Tuple  3370 . Context Expression Prefix Dictionary  2818  contains one or more Context Emoji  3006  in  FIG. 30  in combination with, and in any order, one or more Action Emoji  3008  in  FIG. 30 . In another embodiment, Context Expression Prefix Dictionary  2818  contains one or more Context Emoji  3006  in  FIG. 30 . In yet another embodiment, Context Expression Prefix Dictionary  2818  contains one or more Action Emoji  3008  in  FIG. 30 .  52  “prefix, n.” OED Online, Oxford University Press, March 2020, www.oed.com/view/Entry/150035. Accessed 3 May 2020. 
     An exemplary enablement is a system that enables the use of an emoji prefix which may be used to adjust or modify the conveyed attributive context for a context expression or to describe and distinguish the conveyed attributive context for a context expression. 
     Query Emoji  3301  illustrates a query derived Action Emoji  3008  (see,  FIG. 30 ). Query Emoji  3301  may be used to convey a query attributive context for a context expression. Query represents query languages used to make queries in the applications and systems (see,  FIG. 26 ). Execute Emoji  3302  illustrates an execute derived Action Emoji  3008  (see,  FIG. 30 ). Execute Emoji  3302  may be used to convey an execute attributive context for a context expression. Execute represents the instructions of a computer program to be executed in the applications and systems (see,  FIG. 26 ). 
     An exemplary enablement is a system that utilizes emoji to convey query context for a context expression. 
     Another exemplary enablement is a system that utilizes emoji to convey execute context for a context expression. 
     A query statement is composed of the initial Query Emoji  3301  followed by one or more Context Emoji  3006  in  FIG. 30  in combination with, and in any order, one or more Action Emoji  3008  in  FIG. 30  to the left of the first Word Divider  3002  (or Delimiter  3004  in  FIG. 30 ) in Context Expression Tuple  3110 , Context Expression Tuple  3120 , Context Expression Tuple  3130 , Context Expression Tuple  3230 , and Context Expression Tuple  3250 , Context Expression Tuple  3360 , Context Expression Tuple  3370 , Context Expression Tuple  3380 , and Context Expression Tuple  3390 . An execute statement is composed of the initial Execute Emoji  3302  followed by one or more Context Emoji  3006  in  FIG. 30  in combination with, and in any order, one or more Action Emoji  3008  in  FIG. 30  to the left of the first Word Divider  3002  (or Delimiter  3004  in  FIG. 30 ) in Context Expression Tuple  3110 , Context Expression Tuple  3120 , Context Expression Tuple  3130 , Context Expression Tuple  3230 , and Context Expression Tuple  3250 , Context Expression Tuple  3360 , Context Expression Tuple  3370 , Context Expression Tuple  3380 , and Context Expression Tuple  3390 . Query statements and execute statements represent emoji prefixes. 
     During composition, the system utilizes Context Expression Build Module  2904  or Context Expression Template Module  2918  in  FIG. 29  to add the emoji prefix to Context Expression Prefix Dictionary  2818  whereby a new KVP is created for each emoji in the prefix. 
     The following examples illustrate context expressions for various context expression tuples: 
     Context Expression Tuple  3330  Example 1: 
     :website: :business:Pepe&#39;s Pizza 
     Context Expression Tuple  3390  Example 2: 
     :website: #Pepe&#39;s Pizza 
     Context Expression Tuple  3350  Example 3: 
     :address: :business:Pepe&#39;s Pizza 
     Context Expression Tuple  3390  Example 4: 
     :menu: #Pepe&#39;s Pizza 
     Context Expression Tuple  3390  Example 5: 
     :showtimes: #Star Wars 
     Context Expression Tuple  3390  Example 6: 
     :showtimes: #New Haven #&gt;7:00 PM 
     Context Expression Tuple  3330  Example 7: 
     :movies::actors: name: Harrison Ford 
     Context Expression Tuple  3390  Example 8: 
     :buy::movie::tickets: #Star Wars 
     Context Expression Tuple  3340  Example 9: 
     :add::review: :restaurant::pepes_pizza::overall::joy::food:9:service:8 
     Context Expression Tuple  3340  Example 10: 
     :query::reviews: :restaurant::pepes_pizza: 
     Context Expression Tuple  3340  Example 11: 
     :reviews::results: :restaurant::pepes_pizza::overall::joy::food:9.1:service:8.75 
     Context Expression Tuple  3350  Example 12: 
     :execute::on::new::review::notify: :restaurant:—Pepe&#39;s|:email:—manager@ pepespizzeria.com|:message:—A new review was shared.|:subject:—New Review Notification 
     An exemplary enablement is a system that enables the use of an emoji prefix which may be used to convey an attributive context for a context expression. 
     Another exemplary enablement is a system that enables the use of an emoji prefix which may be used to convey an actionable attributive context for a context expression. 
     Context Expression Tuple  3340  Example 9 illustrates an exemplary enablement of the system that enables the crowd sourcing of context expressions with the conveyance of sentiment as an attributive context. 
     Context Expression Tuple  3340  Example 10 illustrates an exemplary enablement of the system that utilizes emoji to compose query statements to convey query context for a context expression. 
     Context Expression Tuple  3340  Example 11 illustrates an exemplary enablement of the system that utilizes emoji to enable the composition of context expression as the result of a query. 
     Context Expression Tuple  3340  Example 11 illustrates an exemplary enablement of the system that utilizes emoji to enable the composition of context expression with the conveyance of aggregate sentiment as an attributive context. 
     Context Expression Tuple  3350  Example 12 illustrates an exemplary enablement of the system that utilizes emoji to compose execute statements to convey execute context for a context expression. 
     An exemplary enablement is a system that accepts visual, audio, or tactile input for a context expression. 
     Another exemplary enablement is a system whose output may be visual, audio, or tactile for a context expression. 
     Another enablement is a system that accepts visual, audio, or tactile input to convey attributive context for a context expression. 
     Another exemplary enablement is a system whose output may be visual, audio, or tactile to convey attributive context for a context expression. 
     Context Expression Build Module  2904  and Context Expression Rules Module  2914 , as well as Context Expression Module Manager  2900  and its constituent parts in  FIG. 29 , enable composition of new context expression key-value tuples, context expression key tuples, and context expression value tuples. Context expression tuples can be created by removing and combining the constituent parts in Context Expression Elements  3000  in  FIG. 30 , Context Expressions  3100  in  FIG. 31 , Context Expressions  3200  in  FIG. 32 , and Context Expressions  3300  in  FIG. 33 . Context Expression Module Manager  2900  enables the storage of new context expression key-value tuples, context expression key tuples, context expression value tuples, and context expression tuples in Context Expression Module Manager Storage  2922  in  FIG. 29 . 
     An exemplary enablement is a system that enables composition of new context expression key-value tuples, context expression key tuples, context expression value tuples, and context expression tuples. 
     Another exemplary enablement is a system that enables storing of context expression key-value tuples, context expression key tuples, context expression value tuples, and context expression tuples. 
     For example, adding a Word Divider  3002  in  FIG. 30  between Context Expression Key Tuple  3050  and Context Expression Value Tuple  3070  in Context Expression Key-Value Tuple  3220  in  FIG. 32  would compose a new context expression key-value tuple, hereto referenced as Context Expression Key-Value Tuple Version 2. Continuing the example, Context Expression Key-Value Tuple Version 2 could then be used to compose a new version Context Expression Tuple  3230  in  FIG. 32  to create Context Expression Tuple Version 2 which then could be used to compose a new version of Context Expression Tuple  3340  in  FIG. 33 , hereto referenced as Context Expression Tuple Version 2. The following examples illustrate context expressions for Context Expression Tuple Version 2: 
     Context Expression Tuple Version 2 Example 1: 
     :food: :article: Pizza 
     Context Expression Tuple Version 2 Example 2: 
     :food::pizza: :article: History of Pizza 
     Context Expression Tuple Version 2 Example 3: 
     :food::pizza::variety: :article: neapolitan pizza 
     Context Expression Tuple Version 2 Example 4: 
     :food::pizza::variety: :article: pizza napoletana 
     Context Expression Tuple Version 2 Example 5: 
     :food::pizza::variety: :article: New Haven Pizza 
     Context Expression Tuple Version 2 Example 6: 
     :food::pizza::variety: :article: apizza 
     Context Expression Tuple Version 2 Examples 1-6 illustrate context expression composition. 
     An exemplary enablement is a system that enables the use of an emoji prefix which may be used to convey a classification attributive context for a context expression. 
     Another exemplary enablement is a system that enables the use of an emoji prefix which may be used to convey a subclassification attributive context for a context expression. 
     Another exemplary enablement is a system that enables the use of an emoji prefix which may be used to conveyed taxonomy attributive context for a context expression. 
     Context Systems  2610  in  FIG. 26  encapsulates and utilizes context expressions as illustrated in  FIGS. 2-3  as well as  FIGS. 26-33  and the specifications that support the figures. In another embodiment, Lookup App(s)  2604 - 1  through Lookup App(s)  2604 -N in  FIG. 26  encapsulate and utilize context expressions. 
     An exemplary enablement is a system that makes context expressions available through an Application Programming Interface (API). 
     Another exemplary enablement is a system that makes context expressions available through a Software Development Kit (SDK). 
       FIG. 34  illustrates Pathway Enumeration  3400  which is composed of None=0  3402 , Pathway Complete=1  3404 , Pathway Character Increment=2  3406 , Pathway Key=4  3408 , Pathway Key-Value Pair=8  3410 , Pathway Locality=16  3412 , Pathway Partition=32  3414 , Pathway Taxonomy=64  3416 , Pathway Temporality=128  3418 , Pathway Value=256  3420 , Pathway Prefix=512  3422 , Pathway Word Increment=1024  3424 , Pathway Word-Joined Increment=2048  3426 , Pathway Word-Joined Character Increment=4096  3428 , Pathway Text=8192  3430 , Pathway Key Text=16384  3432 , Pathway Value Text=32768  3434 , and Pathway Character Increment=65556  3436 . 
     Pathway Enumeration  3400  illustrates an enumeration type with flags which could be extended and is not limited to the exemplified elements. None=0  3402  illustrates a flag value of zero as an optimization, meaning that no flags are set. Pathway Complete=1  3404  illustrates the pathway is complete. Pathway Character Increment=2  3406  illustrates the pathway is a character increment. Pathway Key=4  3408  illustrates the pathway is a key. Pathway Key-Value Pair=8  3410  illustrates the pathway is a key value pair. Pathway Locality=16  3412  illustrates the pathway is a locality. Pathway Partition=32  3414  illustrates the pathway is a partition. Pathway Taxonomy=64  3416  illustrates the pathway is a taxonomy. Pathway Temporality=128  3418  illustrates the pathway is a temporality. Pathway Value=256 illustrates the pathway is a value. Pathway Prefix=512  3422  illustrates the pathway is a prefix. Pathway Word Increment 1024  3424  illustrates the pathway is a word increment. Pathway Word-Joined Increment 2048  3426  illustrates the pathway is a word-joined increment. Pathway Word-Joined Character Increment 4096  3428  illustrates the pathway is a word-joined character increment. Pathway Text=8192  3430  illustrates the pathway is a text. Pathway Key Text=16384  3432  illustrates the pathway is a key text. Pathway Value Text=32768  3434  illustrates the pathway is a value text. Pathway Character Increment=65556  3436  illustrates the pathway is a character increment. 
       FIG. 35  illustrates Content Enumeration  3500  which is composed of None  3502 , Content Domain Name  3504 , Content HTML  3506 , Content JSON  3508 , Content Lookup  3520 , Content Lookup Reference  3522 , Content Pathway  3530 , Content Pathway Reference  3532 , Content UNC  3540 , Content URL  3542 , Content URN  3544 , Content XML  3560 , and Content XSLT  3562 . 
     Content Enumeration  3500  illustrates an enumeration type which could be extended and is not limited to the exemplified elements. None  3502  illustrates an optimization indicating that the enumeration is not set. Content Domain Name  3504  illustrates that the content is a domain name. Content HTML  3506  illustrates that the content is HTML. Content JSON  3508  illustrates that the content is JSON. Content Lookup  3520  illustrates that the content is lookup. Content Lookup Reference  3522  illustrates that the content is one or more lookup references. Content Pathway  3530  illustrates that the content is pathway. Content Pathway Reference  3532  illustrates that the content is pathway references. Content UNC  3540  illustrates that the content is Universal Naming Convention (UNC). Content URL  3542  illustrates that the content is URL. Content URN  3544  illustrates that the content is Uniform Resource Name (URN). Content XML  3560  illustrates that the content is XML. Content XSLT  3562  illustrates that the content is XSLT. 
     Both common and uncommon acronyms are illustrated in Content Enumeration  3500 . Uncommon acronyms have been specified to eliminate ambiguity. 
       FIG. 36  illustrates Content  3600  and Content Collection  3640 . Content  3600  is composed of Content Data  3602 , Content Id  3604 , and Content Type  3606 . Content Collection  3640  contains Content  3600 . 
     Content  3600  illustrates the content composition. Content Data  3602  illustrates the content data for Content  3600 . Content Id  3604  illustrates a unique content identifier for Content  3600 . Content Type  3606  illustrates the content type for Content  3600  (see, Content Enumeration  3500  in  FIG. 35 ). Collections are illustrated by Content Collection  3640 . 
     The word “content” is defined as digital data. When the word “content” is used, it refers to the content definition specified here in addition to Content  3600 , its constituent parts, and any derivatives thereof. The phrase “content item” is defined as a single element of a content collection. When the phrase “content item” is used, it refers to the content item definition specified here in addition to single content element, Content  3600 , in Content Collection  3640 , its constituent parts, and any derivatives thereof. 
     An exemplary enablement is a system that supports but is not limited to the following types of content: domain name, HTML, JSON, lookup, pathway, pathway reference, UNC, URL, URN, XML, and XSLT. 
       FIG. 37  illustrates Taxonomy Enumeration  3700  which is composed of None  3702 , Taxonomy Catalog  3704 , Taxonomy Classification  3706 , Taxonomy Crowd Sourced  3708 , Taxonomy Folk  3710 , Taxonomy Folksonomy  3712 , Taxonomy ISBN  3714 , Taxonomy Model Number  3716 , Taxonomy Serial Number  3718 , Taxonomy SKU  3720 , Taxonomy Subject Heading  3722 , Taxonomy Tag  3724 , and Taxonomy Topic  3726 . 
     Taxonomy Enumeration  3700  illustrates an enumeration type which could be extended and is not limited to the exemplified elements. None  3702  illustrates an optimization indicating that the enumeration is not set. Taxonomy Catalog  3704  illustrates that the taxonomy is catalog. Taxonomy Classification  3706  illustrates that the taxonomy is classification. Taxonomy Crowd Sourced  3708  illustrates that the taxonomy is crowd sourced. Taxonomy Folk  3710  illustrates that the taxonomy is folk taxonomy. Taxonomy Folksonomy  3712  illustrates that the taxonomy is folksonomy. Taxonomy ISBN  3714  illustrates that the taxonomy is ISBN. Taxonomy Model Number  3716  illustrates that the taxonomy is model number. Taxonomy Serial Number  3718  illustrates that the taxonomy is serial number. Taxonomy SKU  3720  illustrates that the taxonomy is SKU. Taxonomy Subject Heading  3722  illustrates that the taxonomy is subject heading. Taxonomy Tag  3724  illustrates that the taxonomy is tag. Taxonomy Topic  3726  illustrates that the taxonomy is topic. An emoji may be derived from Taxonomy Emoji  3012  in  FIG. 30  for each of the named constants in Expression Enumeration  2740  in  FIG. 27 . 
     “A folk taxonomy is a vernacular naming system, as distinct from scientific taxonomy,” and “are generated from social knowledge and are used in everyday speech.” 53  A “folksonomy is the system in which users apply public tags to online items, typically to make those items easier for themselves or others to find later.” 54  A crowd sourced taxonomy is the result of taxonomy aggregation and data analytics.  53  “Folk Taxonomy.” Wikipedia, the Free Encyclopedia, Wikimedia Foundation, Inc, 2 Feb. 2005, en.wikipedia.org/wiki/Folk_taxonomy. Accessed 28 Apr. 2020. 54  “Folksonomy.” Wikipedia, the Free Encyclopedia, Wikimedia Foundation, Inc, 8 Nov. 2004, en.wikipedia.org/wiki/Folksonomy. Accessed 28 Apr. 2020. 
     An exemplary enablement is a system that supports but is not limited to the following types of taxonomies: catalog, classification, crowd sourced, folk, folksonomy, ISBN, model number, serial number, SKU, subject heading, tag, and topic. 
     Another exemplary enablement is a system where taxonomies support the use of emoji. 
     Another exemplary enablement is a system that supports both formal and informal taxonomy systems. 
     Another exemplary enablement is a system that integrates into existing taxonomy systems. 
     Another exemplary enablement is a system where tag taxonomies support the use of ASCII 35 number sign 55 .  55  Peter. “ASCII Code #.” The Complete Table of ASCII Characters, Codes, Symbols and Signs, American Standard Code for Information Interchange, theasciicode.com.ar/ascii-printable-characters/number-sign-ascii-code-35.html. 
     Another exemplary enablement is a system where tag taxonomies support the use of a word divider (e.g., the number sign followed by any number of words separated by spaces). 
     Another exemplary enablement is a system where tag taxonomies may be case sensitive or case insensitive. 
     Another exemplary enablement is a system where tag taxonomies support the use of special characters. 
       FIG. 38  illustrates Pathway Base  3800 , Pathway Base Collection  3840 , Pathway Partition Collection  3850 , Pathway Taxonomy Collection  3860 , Pathway Partition Collections  3870 , Pathway Taxonomy Collections  3880 , and Pathway Base Collections  3890 . Pathway Base  3800  contains Pathway Index  3802 , Pathway Partition Key  3804 , and Pathway Text  3806 . Pathway Base Collection  3840  contains Pathway Base  3800  and Pathway Type  3842 . Pathway Partition Collection  3850  contains Pathway Base Collection  3840  and Pathway Partition Strategy Type  3852 . Pathway Taxonomy Collection  3860  contains Pathway Base Collection  3840  and Pathway Taxonomy Strategy Type  3862 . Pathway Partition Collections  3870  contains Pathway Partition Collection  3850 . Pathway Taxonomy Collections  3880  contains Pathway Taxonomy Collection  3860 . Pathway Base Collections  3890  contains Pathway Base Collection  3840 . 
     Pathway Base Collection  3840  is the parent type of Pathway Partition Collection  3850 , Pathway Taxonomy Collection  3860 , and Pathway Base Collections  3890 . Pathway Partition Collection  3850  is the parent type of Pathway Partition Collections  3870 . Pathway Taxonomy Collection  3860  is the parent type of Pathway Taxonomy Collections  3880 . Parent type hierarchy illustrates the Object Oriented Design (OOD) principle of inheritance where each child type may inherit characteristics of the parent type such as attributes and functions. 
     Pathway Index  3802  and Pathway Partition Key  3804  are context indexes. Pathway Text  3806  is the text used to derive Pathway Index  3802  and may be used to derive Pathway Partition Key  3804 . Pathway Type  3842  in Pathway Base Collection  3840  is a type of Pathway Enumeration  3400  in  FIG. 34 . Pathway Partition Strategy Type  3852  in Pathway Partition Collection  3850  may be utilized to indicate a partition strategy. Pathway Enumeration  3400  in  FIG. 34  may be used for Pathway Partition Strategy Type  3852 . Pathway Taxonomy Strategy Type  3862  in Pathway Taxonomy Collection  3860  may be utilized to indicate a taxonomy strategy. Taxonomy Enumeration  3700  in  FIG. 37  may be used for Pathway Taxonomy Strategy Type  3862 . Collections are illustrated by Pathway Base Collection  3840 , Pathway Partition Collection  3850 , Pathway Taxonomy Collection  3860 , Pathway Partition Collections  3870 , Pathway Taxonomy Collections  3880 , and Pathway Base Collections  3890 . 
       FIG. 39  illustrates Pathway Base  3800 , Pathway  3900 , Pathway Collection  3940 , and Pathway Collections  3950 . Pathway  3900  contains Pathway Base Collections  3890 , Content Collection  3640 , Pathway Expiration  3906 , Pathway Id  3908 , Pathway Partition Collections  3870 , Pathway Taxonomy Collections  3880 , and Pathway Type  3920 . Pathway Collection  3940  contains Pathway  3900 . Pathway Collections  3950  contains Pathway Collection  3940 . Pathway Base  3800  is the parent type of Pathway  3900 . Pathway  3900  descends from Pathway Base  3800 . Collections are illustrated by Pathway Collection  3940  and Pathway Collections  3950 . 
     Pathway Base Collections  3890  illustrates the Pathway Base Collections  3890  in  FIG. 38  for Pathway  3900 . Content Collection  3640  illustrates the Content Collection  3640  in  FIG. 36  for Pathway  3900 . Pathway Expiration  3906  illustrates a DateTime 56  which is an expiration for Pathway  3900 . If a valid DateTime is specified, it illustrates that Pathway  3900  has expired and is removed from the system. Pathway Id  3908  illustrates a unique identifier for Pathway  3900 . Pathway Partition Collections  3870  illustrates the Pathway Partition Collections  3870  in  FIG. 38  for Pathway  3900 . Pathway Taxonomy Collections  3880  illustrates the Pathway Taxonomy Collections  3880  in  FIG. 38  for Pathway  3900 . Pathway Type  3920  illustrates the Pathway Enumeration  3400  in  FIG. 34  for Pathway  3900 .  56  “DateTime Struct (System).” Technical Documentation, API, and Code Examples|Microsoft Docs, docs.microsoft.com/en-us/dotnet/api/system.datetime. 
     During Pathway  3900  construction, a unique identifier is generated for the pathway and assigned to Pathway Id  3908  and Pathway=1  3404  is assigned to the pathway&#39;s Pathway Type  3820 . A default DateTime is assigned to the pathway&#39;s Pathway Expiration  3906 —by default all pathways expire. 
     In another embodiment, pathways can be permanent wherein Pathway Expiration  3906  is set to a constant that is equivalent to 23:59:59.9999999 UTC, Dec. 31, 9999. In yet another embodiment, users specify a DateTime that is assigned to Pathway Expiration  3906 . Upon expiration, a pathway is purged automatically from all systems. In yet another embodiment, the pathway at expiration, is anonymized and retained for machine learning, data analytics, and reporting. 
     An exemplary enablement is a system capable of automatically expiring and removing pathways. 
     Another exemplary enablement is a system that enables users to set pathway expiration. 
     Another exemplary enablement is a system that enables users to create permanent pathways. 
     As stated above, “A ‘context expression’ is defined as an expression that conveys one or more attributive contexts for a subject,” and an “‘actionable context expression’ is defined as an expression that conveys actionable attributive context for a subject.” The phrases “context information” and “contextual information” are used to refer to both context and actionable context information. The context expression by virtue of its conveyances, captures the distinct context information about a subject. The process of deriving a context identifier from a context expression is called a “contextual transformation,” wherein the resulting context identifier is a distinct digital signature that uniquely represents the captured contextual information about a subject. 
     A context identifier may be referenced as a “context index” when it is utilized for its captured contextual information that serves as an index to direct or point to one or more subjects in a system. A context identifier may be referenced as a “context key” when its serves as a key in a data system (e.g., Primary, Partition, Shard, Row); it may be referenced as a “context address” when it serves as an address, in whole or in part, in a resource system. The term “context index” may be used in general to include the “context index,” “context key,” and “context address” senses illustrated above. The term, context index may also refer to contextual information that is actionable (e.g., query and execute) in which case the context identifier may reference processing functionality (e.g., an API address). A “pathway” is defined as a location or route as specified by one or more context indexes. When the word “pathway” is used, it refers to the pathway definition specified here in addition to Pathway  3900 , its constituent parts, and any derivatives thereof. 
     The term “context expression string” is defined, in the first sense, as the string representation of a context expression text members: Context Expression Key Text  2808 , Context Expression Text  2810 , and Context Expression Value Text  2816  (see,  FIG. 28  respectively). 
     The term “context expression string” is defined, in the second sense, as the string representation of the text derived from a Context Key  2702  or Context Value  2704  in Context Item  2700  or Context Item  2710  (see,  FIG. 27  respectively). The term “context expression string collection” is defined as the one or more context expression strings in each Context Item  2700  or Context Item  2710  contained in the context expression members: Context Item Dictionary  2720 , Context Item Key Collection  2806 , Context Item Value Collection  2814 , and Context Expression Prefix Dictionary  2818  (see,  FIG. 28  respectively). 
     One or more context expressions and one or more content items, as well as the content type (see, Content Type  3606  in  FIG. 36 ) relative to each content item, are used to create pathways. The one or more content items are assigned to Content Collection  3640 . 
     An exemplary enablement is a system that enables one or more content items to be add to a pathway. 
     Another exemplary enablement is a system that enables one or more content items in a pathway to reference one or more lookups. 
     Another exemplary enablement is a system that enables one or more content items in a pathway to reference one or more pathways. 
     The context expression strings in the one or more context expressions are used to create context indexes for inclusion into the Pathway Base Collections  3890 , Pathway Partition Collections  3870 , and Pathway Taxonomy Collections  3880  of a pathway. 
     An exemplary enablement is a system that derives a context index that uniquely represents the captured contextual information about a subject. 
     Another exemplary enablement is a system that enables the utilization of a context index to direct or point to a particular context expression. 
     Another exemplary enablement is a system that enables the utilization of a context index as a key in a data system. 
     Another exemplary enablement is a system that enables the utilization of a context index as an address, in whole or in part, in a resource system. 
     Another exemplary enablement is a system that derives pathways as one or more locations or routes as specified by one or more context indexes. 
     Another exemplary enablement is a system that enables the utilization of pathways as one or more locations or routes as specified by one or more context indexes. 
     An exemplary enablement is a system that utilizes pathways as the one or more locations of content. 
     Another exemplary enablement is a system that utilizes pathways as one or more routes to content. 
     Another exemplary enablement is a system that utilizes pathways as the one or more locations of lookup content. 
     Another exemplary enablement is a system that utilizes pathways as one or more routes to lookup content. 
     Another exemplary enablement is a system that utilizes pathways as the one or more locations of lookup reference content. 
     Another exemplary enablement is a system that utilizes pathways as one or more routes to lookup reference content. 
     Another exemplary enablement is a system that utilizes pathways as the one or more locations of pathway content. 
     Another exemplary enablement is a system that utilizes pathways as one or more routes to pathway content. 
     Another exemplary enablement is a system that utilizes pathways as the one or more locations of pathway reference content. 
     Another exemplary enablement is a system that utilizes pathways as one or more routes to pathway reference content. 
       FIG. 40  illustrates Pathway Module Manager  4000  which is composed of Pathway Blockchain Module  4002 , Pathway Generator Module  4004 , Pathway Machine Learning Module  4010 , Pathway Mapping Module  4012 , Pathway Parsing Module  4014 , Pathway Settings Module  4016 , and Pathway Validation Module  4018 . Pathway Generator Module  4004  contains Pathway Index Algorithm  4100 , Pathway Character Incremental Index Algorithm  4200 , Pathway Word Incremental Index Algorithm  4300 , Pathway Word-Joined Incremental Index Algorithm  4400 , and Pathway Word-Joined Character Incremental Index Algorithm  4500 . Pathway Module Manager  4000  utilizes Context Identifier Algorithm  200 , Context Expression Module Manager  2900 , Pathway Storage  4020 , and Pathway Module Manager Storage  4022 . 
     Pathway Module Manager  4000  illustrates the functionality and logic to manage pathways, modules, and algorithms. It illustrates modular functionality to generate, validate, parse, and map pathways. It exemplifies the modular functionality to create, read, update, and delete pathway data. It also illustrates modular functionality for pathway integration with blockchain and machine learning. Finally, it exemplifies that modules also have the functionality to manage pathways, modules, and algorithm settings. 
     Pathway Blockchain Module  4002  illustrates functionality and logic for pathway integration with blockchain. It exemplifies functionality to create, read, update, and delete pathway blockchain data as well as the logic for pathway blockchain management. 
     Pathway Generator Module  4004  illustrates functionality and logic to generate pathways. It exemplifies functionality to create, read, update, and delete pathway data as well as the logic for pathway generation management. It illustrates pathway generation integration functionality for blockchain and machine learning. Pathway Generator Module  4004  utilizes Pathway Index Algorithm  4100  (see, Pathway Index Algorithm  4100  in  FIG. 41 ), Pathway Character Incremental Index Algorithm  4200  (see, Pathway Character Incremental Index Algorithm  4200  in  FIG. 42 ), Pathway Word Incremental Index Algorithm  4300  (see, Pathway Word Incremental Index Algorithm  4300  in  FIG. 43 ), Pathway Word-Joined Incremental Index Algorithm  4400  (see, Pathway Word-Joined Incremental Index Algorithm  4400  in  FIG. 44 ), and Pathway Word-Joined Character Incremental Index Algorithm  4500  (see, Pathway Word-Joined Character Incremental Index Algorithm  4500  in  FIG. 45 ). 
     Pathway Machine Learning Module  4010  illustrates functionality and logic for pathway integration with machine learning. It exemplifies functionality to create, read, update, and delete pathway machine learning data as well as the logic for pathway machine learning management. 
     Pathway Mapping Module  4012  illustrates mapping functionality and logic for working with pathways. 
     Pathway Parsing Module  4014  illustrates parsing functionality and logic for working with pathways. Furthermore, Pathway Parsing Module  4014  illustrates functionality for parsing query and programming language in context expressions for pathways. 
     Pathway Settings Module  4016  exemplifies functionality and logic to create, read, update, and delete pathway settings data as well as the logic for pathway settings management. 
     Pathway Validation Module  4018  illustrates validation functionality and logic for working with pathways. Furthermore, Pathway Validation Module  4018  illustrates functionality for validating context expression language use in generating pathways. 
     Context Identifier Algorithm  200  illustrates Context Identifier Algorithm  200  in  FIG. 2 . Context Expression Module Manager  2900  illustrates Context Expression Module Manager  2900  in  FIG. 29 . Pathway Storage  4020  and Pathway Module Manager Storage  4022  illustrate data storage systems such as a database. Pathway Module Manager  4000  and its constituent parts, as well as any derivatives thereof, may access and utilize Context Identifier Algorithm  200 , Context Expression Module Manager  2900 , Context Expression Storage  2920  and Context Expression Module Manager Storage  2922 . 
     Pathway Module Manager  4000  utilizes Pathway Generator Module  4004  to generate pathways from, as stated above, “one or more context expressions and one or more content items.” When Pathway Generator Module  4004  generates pathways, it creates Pathway  3900  (see, Pathway  3900   FIG. 39 ) as well as Pathway  3900  members: Pathway Base Collections  3890 , Pathway Partition Collections  3870  and Pathway Taxonomy Collections  3880  (see,  FIG. 39  respectively). 
     For each of the one or more context expressions, Pathway Generator Module  4004  inputs the context expression and a Context Expression Type  2812  (see,  FIG. 28 ) into Pathway Index Algorithm  4100  to create context indexes. If the context expression&#39;s Context Expression Type  2812  is equal to User Template  2744 , Generated Template  2746 , Simple  2748 , or Prefix  2752  (see,  FIG. 27  respectively), then the context expression&#39;s Context Expression Type  2810  is used; if, the context expression&#39;s Context Expression Type  2810  is equal to Complex  2750  (see,  FIG. 27 ) then Pathway Generator Module  4004  utilizes Pathway Index Algorithm  4100  three times, inputting the context expression each time as well as one each of: Generated Template  2746 , Simple  2748  and Complex  2750 . Pathway Generator Module  4004  adds the resulting Pathway Base Collection  3840  generated from Pathway Index Algorithm  4100  to Pathway Base Collections  3890  in Pathway  3900 . 
     In an alternative embodiment, if the context expression&#39;s Context Expression Type  2812  is equal to Prefix  2752 , Pathway Generator Module  4004  creates a new Pathway Partition Collection  3850  (see,  FIG. 38 ) and uses Pathway Index Algorithm  4100  to derive Pathway Base Collection  3840 ; wherein the value from a bitwise OR operation of Pathway Partition=32  3414  and Pathway Prefix=512  3422  (see,  FIG. 34 ) is assigned to the Pathway Partition Strategy Type  3852  (see,  FIG. 38 ) in the returned Pathway Base Collection  3840 . Pathway Base Collection  3840  is added to the Pathway Partition Collection  3850  instance which is then added to the Pathway Partition Collections  3870  in Pathway  3900 . This embodiment enables processing of a context expression when its Context Expression Type  2810  is equal to User Template  2744  or Generated Template  2746 , the Pathway Generator Module  4004  creates a new Pathway Partition Collection  3850  and uses the Pathway Index Algorithm  4100  to derive a Pathway Base Collection  3840 ; wherein the value from a bitwise OR operation of Pathway Partition=32  3414  and Pathway Key=4  3408  is assigned to the Pathway Partition Strategy Type  3852  (see,  FIG. 38 ) in the returned Pathway Base Collection  3840 . Pathway Base Collection  3840  is added to the Pathway Partition Collection  3850  instance which is then added to the Pathway Partition Collections  3870  in Pathway  3900 . 
     As stated above, “A context identifier may be referenced as a ‘context index’ when it is utilized for its captured contextual information that serves as an index to direct or point to one or more subjects in a system. A context identifier may be referenced as a ‘context key’ when its serves as a key in a data system (e.g., Primary, Partition, Shard, Row); it may be referenced as a ‘context address’ when it serves as an address, in whole or in part, in a resource system. The term ‘context index’ may be used in general to include the ‘context index,’ context key,′ and ‘context address’ senses illustrated above. A ‘pathway’ is defined as a location or route as specified by one or more context indexes. When the word ‘pathway’ is used, it refers to the pathway definition specified here in addition to Pathway  3900 , its constituent parts, and any derivatives thereof.” 
     A “pathway index” is defined as the location or route as specified by a context index and used as a context address. When the phrase “pathway index” is used, it refers to the pathway index definition specified here in addition to Pathway Index  3802  in  FIG. 38  and any derivatives thereof. 
     A “pathway partition key” is defined as the location or route as specified by a context index and used as context key. When the phrase “pathway partition key” is used, it refers to the pathway partition key definition specified here in addition to Pathway Partition Key  3804  in  FIG. 38  and any derivatives thereof. 
     An exemplary enablement is a system that utilizes one or more context expressions to create one or more pathway indexes. 
     An exemplary enablement is a system that utilizes one or more context expressions to create one or more pathway key indexes. 
     Another exemplary enablement is a system that utilizes one or more context expressions to create one or more pathway value indexes. 
     Another exemplary enablement is a system that utilizes one or more context expressions to create one or more pathway prefix indexes. 
     Another exemplary enablement is a system that utilizes one or more context expressions to create one or more pathway key-value pair indexes, one or more pathway key indexes, and one or more pathway value indexes. 
     An exemplary enablement is a system that utilizes one or more context expressions to create one or more pathway partition keys. 
     Another exemplary enablement is a system that utilizes one or more context expressions to create a one or more pathway prefix partition keys. 
     As illustrated, Pathway Generator Module  4004  utilizes Pathway Index Algorithm  4100  to create instances of Pathway Base Collection  3840  that may be utilized as pathway indexes and pathway partition keys. Pathway Generator Module  4004  may utilize Pathway Mapping Module  4012  to map Context Expression Type  2812  in context expressions to Pathway Type  3842  in Pathway Base Collection  3840  during pathway index generation. Moreover, Pathway Mapping Module  4012  may be utilized to map Pathway Type  3842  in Pathway Base Collection  3840  to Pathway Partition Strategy Type  3852  in Pathway Partition Collection  3850  during pathway partition key generation. In another embodiment, Pathway Index Algorithm  4100  may be used to generate pathway taxonomy key generation for inclusion in Pathway Taxonomy Collection  3860  and Pathway Taxonomy Collections  3880  (see,  FIG. 38 ); moreover, Pathway Mapping Module  4012  may be utilized to map Pathway Type  3842  in Pathway Base Collection  3840  to Pathway Taxonomy Strategy Type  3862  in Pathway Taxonomy Collection  3860 . 
     An exemplary enablement is a system that maps one or more context expression types to one or more pathway types to create one or more pathway indexes. 
     Another exemplary enablement is a system that maps one or more context expression types to one or more pathway types to create one or more pathway partition keys. 
     Pathway Generator Module  4004  utilizes Pathway Word Incremental Index Algorithm  4300  to create pathway word incremental indexes. For each of one or more context expressions, each key item text in Context Item Key Collection  2806  (see, Context Item Key Collection  2806  in  FIG. 28 ), Context Item Key Text  2808  (see, Context Item Key Text  2808  in  FIG. 28 ), each value item text in the Context Item Value Collection  2814  (see, Context Item Value Collection  2814  in  FIG. 28 ), Context Item Value Text  2816  (see, Context Item Value Text  2816  in  FIG. 28 ), Context Expression Text  2810  (see, Context Expression Text  2810  in  FIG. 28 ), the string representation of each KVP in Context Item Dictionary  2720  (see, Context Item Dictionary  2720  in  FIG. 28  and Context Expression Key-Value Tuple  3040  in  FIG. 30 ), and the string representation of context expression created from each KVP in Context Expression Prefix Dictionary  2818  (see, Context Expression Prefix Dictionary  2818  in  FIG. 28  and Context Expression Key-Value Tuple  3040  in  FIG. 30 ) are utilized as Pathway Text  4308  (see, Pathway Text  4308  in  FIG. 43 ) input. 
     Pathway Generator Module  4004  may utilize a context expression composition use mapping value from Pathway Mapping Module  4012 , where the context expression&#39;s composition is mapped to its use to locate a specific Primary Partition Key  3804  in Pathway Base  3800  in Pathway Base Collection  3840  in Pathway Base Collections  3890  of Pathway  3900 , as Pathway Partition Key  4306  (see, Pathway Partition Key  4306  in  FIG. 43 ) input. 
     Pathway Generator Module  4004  inputs Pathway Text  4308  and Pathway Partition Key  4306  into Pathway Word Incremental Index Algorithm  4300  resulting in Pathway Base  4350  (see, Pathway Base  4350  in  FIG. 43 ). 
     Pathway Generator Module  4004  may utilize a context expression composition index use mapping value from Pathway Mapping Module  4012 , wherein the indexing algorithm, or indexer, used (e.g., Pathway Word Incremental Index Algorithm  4300 ) is mapped to a Pathway Enumeration  3400  (e.g., Pathway Word Increment=1024  3424  in  FIG. 34 ) and the source context expression composition used as input into indexing algorithm is mapped to a Pathway Enumeration  3400  (i.e., Pathway Text=8192  3430  in  FIG. 34 ); Pathway Mapping Module  4012  performs a bitwise OR operation of the mappings to derive the value assigned to Pathway Type  3842  in Pathway Base Collection  3840 . Hence, resulting Pathway Base  4350  (see, Pathway Base  4350  in  FIG. 43 ) are collected into new instances of Pathway Base Collection  3840  based on context expression composition index use mapping. 
     For example, Pathway Generator Module  4004  may utilize a context expression&#39;s Context Expression Text  2810  as the Pathway Text  4308  input and the context expression composition use mapping value from Pathway Mapping Module  4012  as the Pathway Partition Key  4306  input for Pathway Word Incremental Index Algorithm  4300 . Pathway Generator Module  4004  creates a new Pathway Base Collection  3840  instance, to which it assigns context expression composition index use mapping value to the Pathway Type  3842  member, and adds the resulting Pathway Base  4350 , or pathway word incremental index, to the instance. 
     An exemplary enablement is a system that utilizes context expression composition use mapping values to create one or more pathway word incremental indexes. 
     Another exemplary enablement is a system that utilizes context expression composition index use mapping values to create one or more pathway word incremental indexes. 
     Pathway Character Incremental Index Algorithm  4200  is utilized by Pathway Generator Module  4004  to derive pathway character incremental indexes. 
     In one embodiment, Pathway Generator Module  4004  utilizes Pathway Text  4206  and Pathway Partition Key  4210  as inputs (see, Pathway Text  4206  and Pathway Partition Key  4210  respectively in  FIG. 42 ) from one or more Pathway Base  3800  in one or more Pathway Base Collection  3840 , and each Pathway Base Collection  3840  its Pathway Type  3842  as input of Pathway Type  4208  (see, Pathway Type  4208  in  FIG. 42 ) derived from Pathway Word Incremental Index Algorithm  4300  for Pathway Character Incremental Index Algorithm  4200  to derive Pathway Base Collection  4250  (see, Pathway Base Collection  4250  in  FIG. 42 ), or pathway character incremental indexes. The resulting Pathway Base Collection  4250  is added to Pathway Base Collections  3890  in Pathway  3900 . 
     In another embodiment, Pathway Generator Module  4004  utilizes one or more context expressions, each key item text in Context Item Key Collection  2806 , Context Item Key Text  2808 , each value item text in Context Item Value Collection  2814 , Context Item Value Text  2816 , Context Expression Text  2810 , the string representation of each KVP in the Context Item Dictionary  2720 , and the string representation of context expression created from each KVP in Context Expression Prefix Dictionary  2818  are utilized as Pathway Text  4206  (see, Pathway Text  4206  in  FIG. 42 ) input. 
     Pathway Generator Module  4004  may utilize a context expression composition use mapping value from Pathway Mapping Module  4012 , where the context expression&#39;s composition is mapped to its use to locate a specific Primary Partition Key  3804  in Pathway Base  3800  in Pathway Base Collection  3660  in Pathway Base Collections  3890  of Pathway  3900 , as Pathway Partition Key  4210  (see, Pathway Partition Key  4210  in  FIG. 42 ) input. 
     Pathway Generator Module  4004  may utilize a context expression composition index use mapping value from Pathway Mapping Module  4012 , wherein the indexing algorithm, or indexer, used (e.g., Pathway Character Incremental Index Algorithm  4200  in  FIG. 42 ) is mapped to a Pathway Enumeration  3400  (e.g., Pathway Character Increment=65556  3436  in  FIG. 34 ), and the source context expression composition used as input into the indexer is mapped to a Pathway Enumeration  3400  (i.e., Pathway Text=8192  3430  in  FIG. 34 ); Pathway Mapping Module  4012  performs a bitwise OR operation of the mappings to derive the value utilized as Pathway Type  4208  (see, Pathway Type  4208  in  FIG. 42 ) input. 
     Pathway Generator Module  4004  sources Pathway Text  4206  input, Pathway Partition Key  4210  input, and Pathway Type  4208  input, as illustrated above, for input into Pathway Character Incremental Index Algorithm  4200  to derive Pathway Base Collection  4250 , or pathway character incremental indexes. The resulting Pathway Base Collection  4250  is added to Pathway Base Collections  3890  in Pathway  3900 . 
     An exemplary enablement is a system that utilizes context expression composition use mapping values to create one or more pathway character incremental indexes. 
     Another exemplary enablement is a system that utilizes context expression composition index use mapping values to create one or more pathway character incremental indexes. 
     Pathway Word-Joined Incremental Index Algorithm  4400  is utilized by Pathway Generator Module  4004  to derive pathway word-joined incremental indexes. 
     Pathway Generator Module  4004  utilizes one or more context expressions, where a context expression&#39;s Context Item Key Collection  2806 , Context Item Value Collection  2814 , Context Item Dictionary  2720 , and Context Expression Prefix Dictionary  2818  may be converted to string arrays to utilize as Word Array  4406  (see, Word Array  4406  in  FIG. 44 ) input. 
     Pathway Generator Module  4004  may utilize a context expression composition use mapping value from Pathway Mapping Module  4012 , where the context expression&#39;s composition is mapped to its use to locate a specific Primary Partition Key  3804  in the Pathway Base  3800  in Pathway Base Collection  3660  in Pathway Base Collections  3890  of Pathway  3900 , as Pathway Partition Key  4405  (see, Pathway Partition Key  4405  in  FIG. 44 ) input. In an alternative embodiment, Pathway Partition Key  4405  may be specified as an override by Pathway Generator Module  4004 ; in another alternative embodiment, Pathway Partition Key  4405  may be null. 
     Pathway Generator Module  4004  may utilize a context expression composition index use mapping value from Pathway Mapping Module  4012 , wherein the indexing algorithm, or indexer, used (e.g., Pathway Word-Joined Incremental Index Algorithm  4400  in  FIG. 44 ) is mapped to a Pathway Enumeration  3400  (e.g., Pathway Word-Joined Increment=2048  3426  in  FIG. 34 ), and the source context expression composition used as input into the indexer is mapped to a Pathway Enumeration  3400  (i.e., Pathway Key=4  3408 , Pathway Value=256  3420 , Pathway Key-Value Pair=8  3410 , or Pathway Prefix=512  3422  in  FIG. 34 ); Pathway Mapping Module  4012  performs a bitwise OR operation of the mappings to derive the value utilized as Pathway Type  4408  (see, Pathway Type  4408  in  FIG. 44 ) input. 
     Pathway Generator Module  4004  sources Word Array  4406  input, Pathway Partition Key  4405  input, and Pathway Type  4408  input, as illustrated above, for input into Pathway Word-Joined Incremental Index Algorithm  4400  to derive Pathway Base Collection  4450 , or pathway word-joined incremental indexes. Resulting Pathway Base Collection  4450  are added to Pathway Base Collections  3890  in Pathway  3900 . 
     An exemplary enablement is a system that utilizes context expression composition use mapping values to create one or more pathway word-joined incremental indexes. 
     An exemplary enablement is a system that utilizes context expression composition index use mapping values to create one or more pathway word-joined incremental indexes. 
     Pathway Word-Joined Character Incremental Index Algorithm  4500  is utilized by Pathway Generator Module  4004  to derive word-joined character incremental indexes. 
     Pathway Generator Module  4004  utilizes Pathway Base Collection  4450  from Pathway Word-Joined Character Incremental Index Algorithm  4500  as Pathway Base Collection  4508  (see, Pathway Base Collection  4508  in  FIG. 45 ) input. Pathway Partition Key  3842  in Pathway Base Collection  4450  may be used as Pathway Partition Key  4506  (see, Pathway Partition Key  4506  in  FIG. 45 ) input. In an alternative embodiment, Pathway Partition Key  4506  may be specified as an override by Pathway Generator Module  4004 ; in another alternative embodiment, Pathway Partition Key  4506  may be null. Pathway Type  3842  in Pathway Base Collection  4450  may be used as Pathway Type  4510  (see, Pathway Type  4510  in  FIG. 45 ) input. In an alternative embodiment, Pathway Partition Type  4510  may be specified as an override by Pathway Generator Module  4004 ; in another alternative embodiment, Pathway Generator Module  4004  sources Pathway Base Collection  4508  input, Pathway Partition Key  4506  input, Pathway Type  4510  input, as illustrated above, for input into Pathway Word-Joined Character Incremental Index Algorithm  4500  to derive Pathway Base Collection  4550 , or word-joined character incremental indexes. Resulting Pathway Base Collection  4550  are added to Pathway Base Collections  3890  in Pathway  3900 . 
     An exemplary enablement is a system that utilizes context expression composition use mapping values to create one or more pathway word-joined character incremental indexes. 
     Another exemplary enablement is a system that utilizes context expression composition index use mapping values to create one or more pathway word-joined character incremental indexes. 
     In an alternative embodiment, Pathway Generator Module  4004  utilizing Context Expression Module Manager  2900  in  FIG. 29  provides functionality for extractions of partition and taxonomy, in whole or in part, from one or more context expressions to generate additional partition and taxonomy pathway indexes and pathway partition keys. Resulting pathways may be utilized for inclusion into Pathway Base Collections  3890 , Pathway Partition Collections  3870 , and Pathway Taxonomy Collections  3880  in Pathway  3900 . 
     An exemplary enablement is a system that utilizes extractions of partition and taxonomy, in whole or in part, from one or more context expressions to create one or more pathway indexes. 
     An exemplary enablement is a system that utilizes extractions of partition and taxonomy, in whole or in part, from one or more context expressions to create one or more pathway key indexes. 
     Another exemplary enablement is a system that utilizes extractions of partition and taxonomy, in whole or in part, from one or more context expressions to create one or more pathway value indexes. 
     Another exemplary enablement is a system that utilizes extractions of partition and taxonomy, in whole or in part, from one or more context expressions to create one or more pathway prefix indexes. 
     Another exemplary enablement is a system that utilizes extractions of partition and taxonomy, in whole or in part, from one or more context expressions to create one or more pathway key-value pair indexes, one or more pathway key indexes, and one or more pathway value indexes. 
     An exemplary enablement is a system that utilizes extractions of partition and taxonomy, in whole or in part, from one or more context expressions to create one or more pathway partition keys. 
     Another exemplary enablement is a system that utilizes extractions of partition and taxonomy, in whole or in part, from one or more context expressions to create a one or more pathway prefix partition keys. 
     In another alternative embodiment, Pathway Generator Module  4004  utilizing Context Expression Module Manager  2900  in  FIG. 29  provides functionality for extractions of context, action context, taxonomy context, locality context, and temporality context, in whole or in part, from one or more context expressions to generate additional pathway indexes and pathway partition keys. Resulting pathways may be utilized for inclusion into Pathway Base Collections  3890 , Pathway Partition Collections  3870 , and Pathway Taxonomy Collections  3880  in Pathway  3900 . 
     An exemplary enablement is a system that utilizes extractions of context, action context, taxonomy context, locality context, and temporality context, in whole or in part, from one or more context expressions to create one or more pathway indexes. 
     An exemplary enablement is a system that utilize extractions of context, action context, taxonomy context, locality context, and temporality context, in whole or in part, from one or more context expressions to create one or more pathway key indexes. 
     Another exemplary enablement is a system that utilize extractions of context, action context, taxonomy context, locality context, and temporality context, in whole or in part, from one or more context expressions to create one or more pathway value indexes. 
     Another exemplary enablement is a system that utilize extractions of context, action context, taxonomy context, locality context, and temporality context, in whole or in part, from one or more context expressions to create one or more pathway prefix indexes. 
     Another exemplary enablement is a system that utilize extractions of context, action context, taxonomy context, locality context, and temporality context, in whole or in part, from one or more context expressions to create one or more pathway key-value pair indexes, one or more pathway key indexes, and one or more pathway value indexes. 
     An exemplary enablement is a system that utilize extractions of context, action context, taxonomy context, locality context, and temporality context, in whole or in part, from one or more context expressions to create one or more pathway partition keys. 
     Another exemplary enablement is a system that utilize extractions of context, action context, taxonomy context, locality context, and temporality context, in whole or in part, from one or more context expressions to create a one or more pathway prefix partition keys. 
     Pathway Module Manager  4000  utilizes Pathway Generator Module  4004  to generate several types of pathway incremental indexes: character incremental indexes, word incremental indexes, word-joined incremental indexes, and word-joined character incremental indexes. These pathway incremental indexes may be utilized by the system to provide context expression suggestions. 
     Context expression suggestions may be used as data for user interface drop-down such as the HTML &lt;select&gt; and &lt;option&gt; elements used to create drop-down lists. Drop-downs may offer existing pathway suggestions for user pathways during context expression composition or when users enter context expressions into the system to locate pathways. Context expression suggestions may also be used as data for user interface input masks for edit fields providing for the application of rule and validation logic in combination with pathway incremental index driven data to suggest context expressions for composition or pathway location. For example, word-joined incremental indexes may provide a user several emoji suggestions while composing a context expression. 
     An exemplary enablement is a system that enables the utilization of pathway incremental indexes to provide one or more context expressions, in whole or in part, as suggestions for context expression composition. 
     Another exemplary enablement is a system that enables the utilization of pathway incremental indexes to provide one or more context expressions, in whole or in part, as suggestions to locate one or more pathways. 
     Combining pathway incremental indexes with streaming analytics and machine learning enables system generated context expression predictive intelligence for context expression composition and pathway location suggestions. Moreover, combining prefix, context, action context, taxonomy context, locality context, and temporality context with context expression predictive intelligence enables context expression predictive intelligence context, utilized to suggest context expressions for composition and pathway location. Context intelligence (CI) suggestions enable context driven suggestions that guide with precision, pertinence, locality, and temporality. 
     An exemplary enablement is a system that enables the combination of pathway incremental indexes with streaming analytics and machine learning to generate context intelligence for context expression composition and pathway location suggestions. 
     Context intelligence may also be used to create context expressions and derived pathway index and pathway partition keys to be utilized for context sharding. Data center, host, system, service, and storage sharding extend beyond the horizontal and vertical in the physical or virtual (i.e., IaaS, CaaS, PaaS, FaaS, SaaS) to become a multi-dimension context intelligence abstraction layer. Wherein, context intelligence may be utilized to create routes based on pathway index and pathway partition keys derived from context that incorporate data center, host, system, service, and storage designations as well as prefix and extractions of context (i.e., directory, media, politics, social, etc.), action context (i.e., announce, play, purchase, tag, etc.), taxonomy context, locality context, and temporality context. These context graph routes become the edges to content nodes incorporating both the physical and logical implementation layers through the context intelligence abstraction layer to create distributed context graphs. 
     An exemplary enablement is a system that enables context intelligence generation of pathway index and pathway partition keys for context sharding. 
       FIG. 41  illustrates Pathway Index Algorithm  4100 . The algorithm starts with Initial  4102  which is connected to Input Context Expression Type and Context Expression  4104 . Input Context Expression Type and Context Expression  4104  is connected to Context Expression Type  4106 , Context Expression  4108  and Create Pathway Base Collection  4110 . Create Pathway Base Collection  4110  is connected to Pathway Base Collection  4150  and Load Context Items  4112 . Load Context Items  4112  is connected to Pathway Text  4152 , Context Items  4154 , and Create Pathway Index  4114 . Create Pathway Index  4114  is connected to Context Items  4154 , Pathway Index  4156 , Pathway Partition Key  4158 , Context Identifier Algorithm  200 , and Create Pathway Base  4116 . Create Pathway Base  4116  is connected to Pathway Type  4160 , Pathway Base  4162 , and Add Pathway Base and Pathway Type to Collection  4118 . Add Pathway Base and Pathway Type to Collection  4118  is connected to Pathway Base Collection  4150  and Final  4120  illustrating the end of the algorithm. 
     Pathway Index Algorithm  4100  generates context indexes for pathways and returns Pathway Base Collection  4150 . Input Context Expression Type and Context Expression  4104  inputs a Context Expression Type  4106  (see, Context Expression Enumeration  2740  in  FIG. 27 ) and Context Expression  4108  (see Context Expression  2800  in  FIG. 28 ) for processing. Create Pathway Base Collection  4110  creates Pathway Base Collection  4150 . Load Context Items  4112  creates Context Items  4154  as a dictionary. 
     If Context Expression Type  4106  is equal to User Template  2744  or Generated Template  2746  (see,  FIG. 27  respectively), then Load Context Items  4112  assigns the context expression key text (see, Context Expression Key Text  2808  in  FIG. 28 ) from Context Expression  4108  to Pathway Text  4152 , and Load Context Items  4112  loads the Context Item Key Collection  2806  (see, Context Item Key Collection  2806  in  FIG. 28 ) from Context Expression  4108  into Context Items  4154  (see, Context Items Dictionary  2730  in  FIG. 27 ). In alternative embodiment, Context Expression Text  2810  and Context Item Dictionary  2720  are used respectively (see, Context Item Dictionary  2720  and Context Expression Text  2810  in  FIG. 28 ). 
     If Context Expression Type  4106  is equal to Simple  2748  (see,  FIG. 27 ), then Load Context Items  4112  assigns the context expression value text (see, Context Expression Value Text  2816  in  FIG. 28 ) from Context Expression  4108  to Pathway Text  4152 , and Load Context Items  4112  loads context item value collection (see, Context Item Value Collection  2814  in  FIG. 28 ) from Context Expression  4108  into Context Items  4154  (see, Context Items Dictionary  2730  in  FIG. 27 ). In alternative embodiment, Context Expression Text  2810  and Context Item Dictionary  2720  in  FIG. 28  are used respectively 
     If Context Expression Type  4106  is equal to Complex  2750  (see,  FIG. 27 ), then Load Context Items  4112  assigns the context expression text from Context Expression  4108  to Pathway Text  4152 , and Load Context Items  4112  loads context item dictionary from Context Expression  4108  into Context Items  4154  (see, Context Items Dictionary  2720  in  FIG. 27 ). 
     If Context Expression Type  4106  is equal to Prefix  2752  (see,  FIG. 27 ), then Load Context Items  4112  constructs a prefix string from the prefix values in the context expression prefix dictionary (see, Context Expression Prefix Dictionary  2818  in  FIG. 28 ) in Context Expression  4108  and assigns the prefix string to Pathway Text  4152 . Load Context Items  4112  then loads context expression prefix dictionary from Context Expression  4108  into Context Items  4154  (see, Context Items Dictionary  2730  in  FIG. 27 ). 
     Create Pathway Index  4114  utilizes Context Items  4154  for input into Context Identifier Algorithm  200 ; the resulting context identifier (see, Context Identifier  228  in  FIG. 2 ) is assigned to Pathway Index  4156  and Pathway Partition Key  4158 . Create Pathway Base  4116  creates Pathway Base  4162 . If Context Expression Type  4106  is equal to User Template  2744  or Generated Template  2746  (see,  FIG. 27 ), then Pathway Type  4160  is assigned to Pathway Key=4  3408  (see,  FIG. 34 ). If Context Expression Type  4106  is equal to Simple  2748  (see,  FIG. 27 ), then Pathway Type  4160  is assigned to Pathway Value=256  3420  (see,  FIG. 34 ). If Context Expression Type  4106  is equal to Complex  2750  (see,  FIG. 27 ), then Pathway Type  4160  is assigned to Pathway Key-Value Pair=8  3412  (see,  FIG. 34 ). If Context Expression Type  4106  is equal to Prefix  2752 , then Pathway Type  4160  is assigned to Pathway Prefix=512  3422  (see,  FIG. 34 ). Create Pathway Base  4116  assigns Pathway Index  4156 , Pathway Partition Key  4158 , and Pathway Text  4152  to the equivalent members in the Pathway Base  4162  instance. 
     Assign Pathway Type and Add Pathway Base to Collection  4118  assigns Pathway Type  4160  to the equivalent member in the Pathway Base  4162  instance and adds Pathway Base  4162  to Pathway Base Collection  4150 . Final  4120  returns Pathway Base Collection  4150  and the algorithm concludes. In alternative embodiments of Pathway Index Algorithm  4100 , overloads specify settings for Context Identifier Algorithm  200  (e.g., Culture and Case Settings  213  and Sort Settings  222  in  FIG. 2 ). 
     An exemplary enablement is a system that enables context index generation utilizing context identifiers. 
     Another exemplary enablement is a system that enables context index generation from context expressions keys. 
     Another exemplary enablement is a system that enables context index generation from context expressions values. 
     Another exemplary enablement is a system that enables context index generation from context expressions. 
     Another exemplary enablement is a system that enables context index generation from context expression prefixes. 
     Another exemplary enablement is a system that enables context index generation from attributive context in context expressions. 
     Another exemplary enablement is a system that enables context index generation from actionable attributive context in context expressions. 
     Another exemplary enablement is a system that enables context index generation from taxonomy attributive context in context expressions. 
     Another exemplary enablement is a system that enables context index generation from temporality attributive context in context expressions. 
     Another exemplary enablement is a system that enables context index generation from locality attributive context in context expressions. 
       FIG. 42  illustrates Pathway Character Incremental Index Algorithm  4200 . The algorithm starts with Initial  4202  which is connected to Input Pathway Text, Pathway Type, and Pathway Partition Key  4204 . Input Pathway Text, Pathway Type, and Pathway Partition Key  4204  is connected to Pathway Text  4206 , Pathway Type  4208  (see, Pathway Enumeration  3400  in  FIG. 34 ), Partway Partition Key  4210 , and Create Pathway Base Collection  4212 . Create Pathway Base Collection  4212  is connected to Pathway Base Collection  4250  (see, Pathway Base Collection  3840  in  FIG. 38 ) and Initialize Variable, [i=StartPosition−1]  4214 . Initialize Variable, [i=StartPosition−1]  4214  is connected to Start Position CHAR Setting  4216  and Decision  4220 . Decision  4220  is connected to note [i&lt;Text Length]  4218 , which illustrates the looping criteria where the Start Position CHAR Setting  4216  sets the start position of the loop. 
     While i&lt;Text Length, control proceeds to Get Substring [StartPosition, i] of Text  4222 . Get Substring [StartPosition, i] of Text  4222  is connected to Pathway Text  4252  (see, Pathway Text  3806  in  FIG. 38 ) and Generate and Assign Context Identifier  4224 . Generate and Assign Context Identifier  4224  is connected to Context Identifier Algorithm  200 , Pathway Index  4254  (see, Pathway Index  3802  in  FIG. 38 ), and Create and Add Pathway Base to Collection  4226 . Create and Add Pathway Base to Collection  4226  is connected to Pathway Base Collection  4250  and Increment Variable, [i++]  4228  which is connected back to Decision  4220  via Line Segment  4230 . When i=Text Length, control proceeds to Final  4234  via Line Segment  4232 , illustrating the end of the algorithm. 
     Pathway Character Incremental Index Algorithm  4200  generates character incremental context indexes for pathways and returns Pathway Base Collection  4250 . Input Pathway Text, Pathway Type, and Pathway Partition Key  4204  inputs a context expression string as illustrated by Pathway Text  4206  and a pathway type as illustrated by Pathway Type  4208  for processing. Create Pathway Base Collection  4212  creates Pathway Base Collection  4250  and sets Pathway Type  3842  in  FIG. 38  to Pathway Type  4208 . Initialize Variable, [i=StartPosition−1]  4214  initializes the variable utilizing Start Position CHAR Setting  4216 . Get Substring [StartPosition, i] of Text  4222  gets the substring of Pathway Text  4206  which is then assigned to Pathway Text  4252 . Generate and Assign Context Identifier  4224  utilizes Context Identifier Algorithm  200  to generate a context identifier from Pathway Text  4252  and assigns the context identifier to Pathway Index  4254 . Create and Add Pathway Base to Collection  4226  creates a new Pathway Base  3800  instance (see, Pathway Base  3800  in  FIG. 38 ) and assigns Pathway Text  4252 , Pathway Partition Key  4210 , and Pathway Index  4254  to the equivalent members in the Pathway Base  3800  instance whereupon it is added to Pathway Base Collection  4250 . Increment Variable, [i++]  4228  increments the variable and passes control to Decision  4220  to continue or conclude processing. 
     In an alternative embodiment an incremental increase for Increment Variable, [i++]  4228  may be configured by an “increment” variable such that “i++” is replaced by “i+ increment” for the algorithm configured by setting or parameter in an overload of the algorithm method signature. In another alternative embodiment, the Start Position CHAR Setting  4216  may be configured by a parameter in an overload of the algorithm method signature. Alternative embodiments of Pathway Character Incremental Index Algorithm  4200  provide method overloads to take parameters of Context Expression  2800  members and settings for Context Identifier Algorithm  200  (e.g., Culture and Case Settings  213  and Sort Settings  222  in  FIG. 2 ). 
     An exemplary enablement is a system that enables pathway character incremental index generation utilizing context identifiers. 
     Another exemplary enablement is a system that enables pathway character incremental index generation from context expressions keys. 
     Another exemplary enablement is a system that enables pathway character incremental index generation from context expressions values. 
     Another exemplary enablement is a system that enables pathway character incremental index generation from context expressions. 
     Another exemplary enablement is a system that enables pathway character incremental index generation from context expression prefixes. 
     Another exemplary enablement is a system that enables pathway character incremental index generation from attributive context in context expressions. 
     Another exemplary enablement is a system that enables pathway character incremental index generation from actionable attributive context in context expressions. 
     Another exemplary enablement is a system that enables pathway character incremental index generation from taxonomy attributive context in context expressions. 
     Another exemplary enablement is a system that enables pathway character incremental index generation from temporality attributive context in context expressions. 
     Another exemplary enablement is a system that enables pathway character incremental index generation from locality attributive context in context expressions. 
       FIG. 43  illustrates Pathway Word Incremental Index Algorithm  4300 . The algorithm starts with Initial  4302  which is connected to Input Pathway Text and Pathway Partition Key  4304 . Input Pathway Text and Pathway Partition Key  4304  is connected to Pathway Partition Key  4306 , Pathway Text  4308 , and Create Pathway Base  4310 . Create Pathway Base  4310  is connected to Pathway Base  4350  and Assign Pathway Text and Pathway Partition Key  4312 . Assign Pathway Text and Pathway Partition Key  4312  is connected to Pathway Base  4350  and Generate and Assign Pathway Index  4314 . Generate and Assign Pathway Index  4314  is connected to Context Identifier Algorithm  200 , Pathway Base  4350 , and Final  4316  illustrating the end of the algorithm. 
     Pathway Word Incremental Index Algorithm  4300  generates word incremental context indexes for pathways and returns Pathway Base  4350 . Input Pathway Text and Pathway Partition Key  4304  inputs Pathway Text  3806  (see, Pathway Text  3806  in  FIG. 38 ) and Pathway Partition Key  3804  (see, Pathway Partition Key  3804  in  FIG. 38 ) which are assigned to Pathway Text  4308  and Pathway Partition Key  4306  respectively for processing. Create Pathway Base  4310  creates Pathway Base  4350  (see, Pathway Base  3800  in  FIG. 38 ). Assign Pathway Text and Pathway Partition Key  4312  assigns Pathway Text  4308  and Pathway Partition Key  4306  to the equivalent members in the Pathway Base  4350  instance. 
     Generate and Assign Pathway Index  4314  creates Generated Context Key  2712  (see, Generated Context Key  2712  in  FIG. 27 ) which is utilized to create Context Item  2710  (see, Context Item  2710  in  FIG. 27 ) wherein Generate and Assign Pathway Index  4314  assigns Pathway Text  4308  to Context Value  2704  (see, Context Value  2704  in  FIG. 27 ) in Context Item  2710 . Generate and Assign Pathway Index  4314  creates Context Item Dictionary  2730  (see, Context Item Dictionary  2730  in  FIG. 27 ) to which Generate and Assign Pathway Index  4314  adds Context Item  2710 . Generate and Assign Pathway Index  4314  inputs Context Item Dictionary  2730  into Context Identifier Algorithm  200  to generate Context Identifier  228  (see, Context Identifier  228  in  FIG. 2 ). The resulting Context Identifier  228  is assigned to the Pathway Index  3802  in the Pathway Base  4350  instance. Control flows to Final  4314  which returns Pathway Base  4350 . Alternative embodiments of Pathway Word Incremental Index Algorithm  4300  provide method overloads to take parameters settings for Context Identifier Algorithm  200  (e.g., Culture and Case Settings  213  and Sort Settings  222  in  FIG. 2 ). 
     An exemplary enablement is a system that enables pathway word incremental index generation utilizing context identifiers. 
     Another exemplary enablement is a system that enables pathway word incremental index generation from context expressions keys. 
     Another exemplary enablement is a system that enables pathway word incremental index generation from context expressions values. 
     Another exemplary enablement is a system that enables pathway word incremental index generation from context expressions. 
     Another exemplary enablement is a system that enables pathway word incremental index generation from context expression prefixes. 
     Another exemplary enablement is a system that enables pathway word incremental index generation from attributive context in context expressions. 
     Another exemplary enablement is a system that enables pathway word incremental index generation from actionable attributive context in context expressions. 
     Another exemplary enablement is a system that enables pathway word incremental index generation from taxonomy attributive context in context expressions. 
     Another exemplary enablement is a system that enables pathway word incremental index generation from temporality attributive context in context expressions. 
     Another exemplary enablement is a system that enables pathway word incremental index generation from locality attributive context in context expressions. 
       FIG. 44  illustrates Pathway Word-Joined Incremental Index Algorithm  4400 . The algorithm starts with Initial  4402  which is connected to Input Word Array, Partition Key, and Pathway Type  4404 . Input Word Array, Partition Key, and Pathway Type  4404  is connected to Pathway Partition Key  4405 , Word Array  4406 , Pathway Type  4408 , and Create Output Pathway Base Collection  4410 . Create Output Pathway Base Collection  4410  is connected to Pathway Base Collection  4450  and Allocate Words Variable  4412 . Allocate Words Variable  4412  is connected to Words  4414  and Decision  4416 . Decision  4416  is connected to note [Foreach Word]  4418 , Pathway Base Processing  4420 , and Final  4436  via Line Segment  4434 . Pathway Base Processing  4420  contains Create Pathway Base  4422 , Concatenate  4424 , Generate Context Identifier  4426 , Assign  4428 , and Add Pathway Base to Collection  4430 . Pathway Base Processing  4420  connects to Pathway Base  4452 , Context Expression Spacer Tuple  3020 , Context Identifier Algorithm  200 , Pathway Text  4454 , Pathway Index  4456 , Pathway Partition Key  4458 , Pathway Base Collection  4450 , and Decision  4416  via Line Segment  4432 . 
     Pathway Word-Joined Incremental Index Algorithm  4400  generates word-joined incremental context indexes for pathways and returns Pathway Base Collection  4450 . Algorithm control flow starts with Initial  4402  and proceeds to Input Word Array, Partition Key, and Pathway Type  4404  which inputs Word Array  4406 , Pathway Partition Key  4405  (see, Pathway Partition Key  3804  in  FIG. 38 ) and Pathway Type  4408  (see, Pathway Type  3842  in  FIG. 38 ). Pathway Partition Key  4405  is a nullable value type 57 . Control proceeds to Create Output Pathway Base Collection  4410  which creates Pathway Base Collection  4450  (see, Pathway Base Collection  3840  in  FIG. 38 ) and then proceeds to Allocate Words Variable  4412  which allocates the Words  4414  variable as an empty string. At Decision  4416 , control proceeds to a for each loop, illustrated by the note [Foreach Word]  4418 , where each word in Word Array  4406  undergoes processing in Pathway Base Processing  4420 . Following Pathway Base Processing  4420 , control passes to Decision  4416 , illustrated by Line Segment  4432 , until the iteration of the for each loop concludes at Decision  4416  which terminates the algorithm at Final  4436 , illustrated by Line Segment  4434 .  57  “Nullable Value Types—C# Reference.” Technical Documentation, API, and Code Examples|Microsoft Docs, docs.microsoft.com/en-us/dotnet/csharp/language-reference/builtin-types/nullable-value-types. 
     Create Pathway Base  4422  is used by Pathway Base Processing  4420  to create a new Pathway Base  4452  for each iteration of Word Array  4406  and sets the pathway type member of the new instance to Pathway Type  4408 . 
     Concatenate  4424  is used by Pathway Base Processing  4420  to concatenate Words  4414 , Context Expression Spacer Tuple  3020 , and the word for a specific iteration of Word Array  4406 . 
     Generate Context Identifier  4426  is used by Pathway Base Processing  4420  to input Pathway Text  4454  into Context Identifier Algorithm  200  and returns a context identifier (see, Context Identifier  228  in  FIG. 2 ). 
     Assign  4428  is used by Pathway Base Processing  4420  to assign values to the instance members of Pathway Base  4452 : Pathway Text  4454  (see, Pathway Text  3806  in  FIG. 38 ), Pathway Index  4456  (see, Pathway Index  3802  in  FIG. 38 ), and Pathway Partition Key  4458  (see, Pathway Partition Key  3804  in  FIG. 38 ). 
     Pathway Base Processing  4420  provides the following functionality for each word in Word Array  2206 : 
     If Words  4414  is equal to an empty string, then Create Pathway Base  4422  is processed and Assign  4428  assigns the pathway text of the current pathway base item to Pathway Text  4454  and Words  4414 . Processing proceeds to Generate Context Identifier  4426 , where upon Assign  4428  assigns the resulting context identifier to Pathway Index  4456 . If Pathway Partition Key  4405  is not null, then Assign  4428  assigns Pathway Partition Key  4405  to Pathway Partition Key  4458 . If Pathway Partition Key  4405  is null, then Assign  4428  assigns Pathway Index  4456  to Pathway Partition Key  4458 . Processing continues to Add Pathway Base to Collection  4430 . 
     If Words  4414  is not equal to an empty string, then Create Pathway Base  4422  is processed followed by Concatenate  4424  processing where upon Assign  4428  assigns the resulting concatenated string to Pathway Text  4454  and Words  4414 . Processing proceeds to Generate Context Identifier  4426 , where upon Assign  4428  assigns the resulting context identifier to Pathway Index  4456 . If Pathway Partition Key  4405  is null, then Assign  4428  assigns to Pathway Partition Key  4458  the value from Pathway Index  4456  in the last item (Pathway Base  4452 ) added to Pathway Base Collection  4450 . If Pathway Partition Key  4405  is not null, then Assign  4428  assigns Pathway Partition Key  4405  to Pathway Partition Key  4458 . Processing continues to Add Pathway Base to Collection  4430 . 
     Alternative embodiments of Pathway Word-Joined Incremental Index Algorithm  4400  provide method overloads to take parameters of settings for Context Identifier Algorithm  200  (e.g., Culture and Case Settings  213  and Sort Settings  222  in  FIG. 2 ) and settings for Context Expression Spacer Tuple  3020  (e.g., Word Divider  3002  and Delimiter  3004 ). 
     An exemplary enablement is a system that enables word-joined incremental index generation wherein words are joined iteratively; each iteration generates a unique index as a location in the word-joined aggregate; the unique index associated with a string representation of the word-joined iterative aggregate; the unique index associated with the previous iteration&#39;s unique index as a partition key or a provided partition key. 
     Another exemplary enablement is a system that enables a word-joined incremental index for context expression prediction. 
     Another exemplary enablement is a system that enables word-joined incremental index generation utilizing context identifiers. 
     Another exemplary enablement is a system that enables word-joined incremental index generation from context expressions. 
     Another exemplary enablement is a system that enables word-joined incremental index generation from context expression prefixes. 
     Another exemplary enablement is a system that enables word-joined incremental index generation from attributive context in context expressions. 
     Another exemplary enablement is a system that enables word-joined incremental index generation from actionable attributive context in context expressions. 
     Another exemplary enablement is a system that enables word-joined incremental index generation from taxonomy attributive context in context expressions. 
     Another exemplary enablement is a system that enables word-joined incremental index generation from temporality attributive context in context expressions. 
     Another exemplary enablement is a system that enables word-joined incremental index generation from locality attributive context in context expressions. 
     Another exemplary enablement is a system that enables word-joined incremental index generation utilizing context identifiers for pathway partition key generation. 
     Another exemplary enablement is a system that enables word-joined incremental index generation from context expressions for pathway partition key generation. 
     Another exemplary enablement is a system that enables word-joined incremental index generation from context expression prefixes for pathway partition key generation. 
     Another exemplary enablement is a system that enables word-joined incremental index generation from attributive context in context expressions for pathway partition key generation. 
     Another exemplary enablement is a system that enables word-joined incremental index generation from actionable attributive context in context expressions for pathway partition key generation. 
     Another exemplary enablement is a system that enables word-joined incremental index generation from taxonomy attributive context in context expressions for pathway partition key generation. 
     Another exemplary enablement is a system that enables word-joined incremental index generation from temporality attributive context in context expressions for pathway partition key generation. 
     Another exemplary enablement is a system that enables word-joined incremental index generation from locality attributive context in context expressions for pathway partition key generation. 
     Another exemplary enablement is a system that enables word-joined incremental index generation utilizing context identifiers for pathway partition text generation. 
     Another exemplary enablement is a system that enables word-joined incremental index generation from context expressions for pathway partition text generation. 
     Another exemplary enablement is a system that enables word-joined incremental index generation from context expression prefixes for pathway partition text generation. 
     Another exemplary enablement is a system that enables word-joined incremental index generation from attributive context in context expressions for pathway partition text generation. 
     Another exemplary enablement is a system that enables word-joined incremental index generation from actionable attributive context in context expressions for pathway partition text generation. 
     Another exemplary enablement is a system that enables word-joined incremental index generation from taxonomy attributive context in context expressions for pathway partition text generation. 
     Another exemplary enablement is a system that enables word-joined incremental index generation from temporality attributive context in context expressions for pathway partition text generation. 
     Another exemplary enablement is a system that enables word-joined incremental index generation from locality attributive context in context expressions for pathway partition text generation. 
       FIG. 45  illustrates Pathway Word-Joined Character Incremental Index Algorithm  4500 . The algorithm starts with Initial  4502  which is connected to Input Pathway Base Collection, Partition Key, and Pathway Type  4504 . Input Pathway Base Collection, Partition Key, and Pathway Type  4504  is connected to Pathway Partition Key  4506 , Pathway Base Collection  4508 , Pathway Type  4510 , and Create Output Pathway Base Collection  4512 . Create Output Pathway Base Collection  4512  is connected to Pathway Base Collection  4550  and Allocate Words Variable  4514 . Allocate Words Variable  4514  is connected to Words  4516  and Decision  4518 . Decision  4518  is connected to Final  4536  via Line Segment  4534 , note [Foreach Pathway Base]  4520  and Pathway Base Processing  4522 . Pathway Base Processing  4522  contains Concatenate  4524 , Generate Pathway Character Incremental Index  4526 , Assign Pathway Type and Pathway Partition Key  4528 , and Append Pathway Base Collection to Collection  4530 . Pathway Base Processing  4522  is connected to Context Expression Spacer Tuple  3020 , Pathway Character Incremental Index Algorithm  4200 , Pathway Base Collection  4550 , Start CHAR Position  4552 , Pathway Base Collection  4554 , and Decision  4518  via Line Segment  4532 . 
     Pathway Word-Joined Character Incremental Index Algorithm  4500  generates word-joined character incremental context indexes for pathways and returns Pathway Base Collection  4550 . Algorithm control flow starts with Initial  4502  and proceeds to Input Pathway Base Collection, Partition Key, and Pathway Type  4504  which inputs Pathway Base Collection  4508  (see, Pathway Base Collection  3840  in  FIG. 38 ), Pathway Partition Key  4506  (see, Pathway Partition Key  3804  in  FIG. 38 ) and Pathway Type  4510  (see, Pathway Type  3842  in  FIG. 38 ). Pathway Partition Key  4506  is a nullable value type. Control proceeds to Create Output Pathway Base Collection  4512  which creates Pathway Base Collection  4550  and then proceeds to Allocate Words Variable  4514  which allocates the Words  4516  variable as an empty string. At Decision  4518 , control proceeds to a for each loop, illustrated by the note [Foreach Pathway Base]  4520 , where each pathway base in Pathway Base Collection  4508  undergoes processing in Pathway Base Processing  4522 . Following Pathway Base Processing  4522 , control passes to Decision  4518 , illustrated by Line Segment  4532 , until the iteration of the for each loop concludes at Decision  4518  which terminates the algorithm at Final  4536 , illustrated by Line Segment  4534 . 
     Concatenate  4524  is used by Pathway Base Processing  4522  to concatenate Words  4516 , Context Expression Spacer Tuple  3020 , and the pathway text of the next pathway base item—relative to the current item in the iteration—in Pathway Base Collection  4508 . The resulting concatenated string is assigned to Words  4516 , and the integer of the character position of the first character of the pathway text in the concatenated string is assigned to Start CHAR Position  4552 . 
     Generate Pathway Character Incremental Index  4526  is used by Pathway Base Processing  4522  to input Start CHAR Position  4552  and Words  4516  into Pathway Character Incremental Index Algorithm  4200 , which returns a pathway base collection (see, Pathway Base Collection  4250  in  FIG. 42 ). The resulting pathway base collection is assigned to Pathway Base Collection  4554 . 
     Assign Pathway Type and Pathway Partition Key  4528  is used by Pathway Base Processing  4522  first, to assign Pathway Type  4510  to the pathway type member in Pathway Base Collection  4554 , and second, to iterate through each pathway base item in Pathway Base Collection  4554  wherein, if Pathway Partition Key  4506  is not null, it is assigned to the pathway base item&#39;s pathway partition key member, else if Pathway Partition Key  4506  is null, then the pathway partition key of the pathway base item in the current iteration of Pathway Base Collection  4508  is assigned to the pathway base item&#39;s pathway partition key member. 
     Append Pathway Base Collection to Collection  4530  is used by Pathway Base Processing  4522  to add the pathway base items in Pathway Base Collection  4554  to Pathway Base Collection  4550 . 
     Pathway Base Processing  4522  provides the following functionality for each pathway base item (see, Pathway Base  3800  in  FIG. 38 ) in Pathway Base Collection  2308 : 
     If the current iteration of Pathway Base Collection  4508  is the last iteration, processing proceeds to Decision  4518 . 
     If Words  4516  is equal to an empty string, Pathway Base Processing  4522  assigns the pathway text (see, Pathway Text  3806  in  FIG. 38 ) of the current pathway base item in the iteration of Pathway Base Collection  4508  to Words  4516 . 
     Pathway Base Processing  4522  processes Concatenate  4524 , Generate Pathway Character Incremental Index  4526 , Assign Pathway Type and Pathway Partition Key  4528 , and Append Pathway Base Collection to Collection  4530  in successive order. 
     Alternative embodiments of Pathway Word-Joined Character Incremental Index Algorithm  4500  provide method overloads to take parameters of settings for Context Identifier Algorithm  200  (e.g., Culture and Case Settings  213  and Sort Settings  222  in  FIG. 2 ) and settings for Context Expression Spacer Tuple  3020  (e.g., Word Divider  3002  and Delimiter  3004  in  FIG. 30 ). 
     An exemplary enablement is a system that enables word-joined character incremental index generation wherein each word is joined iteratively with each character of the next word; each iteration generates a unique index as a location in the word-joined character aggregate; the unique index associated with a string representation of the word-joined character iterative aggregate; the unique index associated with the previous iteration&#39;s unique index as a partition key or a provided partition key. 
     Another exemplary enablement is a system that enables a word-joined character incremental index for context expression prediction. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation utilizing context identifiers. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation from context expressions. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation from context expression prefixes. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation from attributive context in context expressions. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation from actionable attributive context in context expressions. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation from taxonomy attributive context in context expressions. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation from temporality attributive context in context expressions. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation from locality attributive context in context expressions. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation utilizing context identifiers for pathway partition key generation. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation from context expressions for pathway partition key generation. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation from context expression prefixes for pathway partition key generation. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation from attributive context in context expressions for pathway partition key generation. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation from actionable attributive context in context expressions for pathway partition key generation. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation from taxonomy attributive context in context expressions for pathway partition key generation. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation from temporality attributive context in context expressions for pathway partition key generation. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation from locality attributive context in context expressions for pathway partition key generation. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation utilizing context identifiers for pathway partition text generation. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation from context expressions for pathway partition text generation. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation from context expression prefixes for pathway partition text generation. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation from attributive context in context expressions for pathway partition text generation. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation from actionable attributive context in context expressions for pathway partition text generation. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation from taxonomy attributive context in context expressions for pathway partition text generation. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation from temporality attributive context in context expressions for pathway partition text generation. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation from locality attributive context in context expressions for pathway partition text generation. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation utilizing emoji. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation utilizing emoji for pathway partition key generation. 
     Another exemplary enablement is a system that enables word-joined character incremental index generation utilizing emoji for pathway partition text generation; pathway partition text containing emoji shortcodes. 
     Pathway Systems  2612  in  FIG. 26  encapsulates and utilizes pathways as illustrated in  FIGS. 34 through 45  and the specifications that support the figures. In another embodiment, Lookup App(s)  2604 - 1  through Lookup App(s)  2604 -N in  FIG. 26  encapsulate and utilize pathways. 
     An exemplary enablement is a system that makes pathways available through an Application Programming Interface (API). 
     Another exemplary enablement is a system that makes pathways available through a Software Development Kit (SDK). 
       FIG. 46  illustrates Lookup Taxonomy  4600  and Lookup Taxonomy Collection  4640 . Lookup Taxonomy  4600  is composed of Lookup Taxonomy Data  4602 , Lookup Taxonomy Id  4604 , and Lookup Taxonomy Type  4606 . Lookup Taxonomy Collection  4640  contains Lookup Taxonomy  4600 . 
     Lookup Taxonomy  4600  illustrates the taxonomy for a lookup. Lookup Taxonomy Data  4602  illustrates the data for taxonomy attributive context for one or more context expressions for Lookup Taxonomy  4600 . Lookup Taxonomy Id  4604  illustrates a unique taxonomy identifier for Lookup Taxonomy  4600 . Lookup Taxonomy Type  4606  illustrates the taxonomy type for Lookup Taxonomy  4600  (see, Taxonomy Enumeration  3700  in  FIG. 37 ). A collection is illustrated by Lookup Taxonomy Collection  4640 . 
       FIG. 47  illustrates Lookup Header  4700  which is composed of Context Expression Prefix Dictionary  2818 , Context Expression Collections  2850 , Lookup Header Copyright  4704 , Lookup Header Temporality  4706 , Lookup Header Temporality Id  4708 , Lookup Header Description  4710 , Lookup Header Expiration  4712 , Lookup Header Inception  4714 , Lookup Header Id  4716 , Lookup Header Locality  4718 , Lookup Header Locality Id  4720 , Lookup Header Name  4722 , Lookup Taxonomy Collection  4640 , Lookup Header Title  4724 , and Pathway Collections  3950 . 
     Context Expression Prefix Dictionary  2818  illustrates a dictionary of one or more prefixes for Lookup Header  4700 . Context Expression Collections  2850  illustrates a collection of context expressions for Lookup Header  4700  (see, Context Expression Collections  2850  in  FIG. 28 ). Lookup Header Copyright  4704  illustrates a copyright for Lookup Header  4700 . Lookup Header Temporality  4706  illustrates a temporality for Lookup Header  4700 . Lookup Temporality Id  4708  illustrates a unique temporality identifier for Lookup Header  4700 . Lookup Header Description  4710  illustrates a description for Lookup Header  4700 . Lookup Header Expiration  4712  illustrates an expiration for Lookup Header  4700 . Lookup Header Inception  4714  illustrates an inception for Lookup Header  4700 . Lookup Header Id  4716  illustrates a unique identifier for Lookup Header  4700 . Lookup Header Locality  4718  illustrates a locality for Lookup Header  4700 . Lookup Header Locality Id  4720  illustrates a unique locality identifier for Lookup Header  4700 . Lookup Header Name  4722  illustrates a name for Lookup Header  4700 . Lookup Taxonomy Collection  4640  illustrates a lookup taxonomy collection for Lookup Header  4700  (see, Lookup Taxonomy Collection  4640  in  FIG. 46 ). Lookup Header Title  4724  illustrates a title for Lookup Header  4700 . Pathway Collections  3950  illustrates a collection of pathways for Lookup header  4700  (see, Pathway Collections  3950  in  FIG. 39 ). 
     An exemplary enablement is a system that provides one or more prefixes for a lookup header. 
     Another exemplary enablement is a system that provides one or more context expressions in a lookup header. 
     Another exemplary enablement is a system that provides a copyright in a lookup header. 
     Another exemplary enablement is a system that provides a temporality in a lookup header. 
     Another exemplary enablement is a system that provides a unique temporality identifier in a lookup header. 
     Another exemplary enablement is a system that provides a description in a lookup header. 
     Another exemplary enablement is a system that provides an expiration in a lookup header. 
     Another exemplary enablement is a system that provides an inception in a lookup header. 
     Another exemplary enablement is a system that provides a unique identifier in a lookup header. 
     Another exemplary enablement is a system that provides a locality in a lookup header. 
     Another exemplary enablement is a system that provides a unique locality identifier in a lookup header. 
     Another exemplary enablement is a system that provides a name in a lookup header. 
     Another exemplary enablement is a system that provides one or more taxonomies in a lookup header. 
     Another exemplary enablement is a system that provides a title in a lookup header. 
     Another exemplary enablement is a system that provides a one or more pathways for a lookup header. 
     Another exemplary enablement is a system that utilizes attributive context for a context expression in a lookup header. 
     Another exemplary enablement is a system that utilizes a lookup header to enable the visual, audio, or tactile output of one or more context expressions. 
     Another exemplary enablement is a system that utilizes a lookup header to enable the visual, audio, or tactile conveyance of attributive context for a context expression. 
       FIG. 48  illustrates Lookup  4800 , Lookup Collection  4840 , and Lookup Collections  4850 . Lookup  4800  is composed of Emoji Mark  3010 , Action Emoji Collection  4802 , Context Emoji Collection  4804 , Content Collection  3640 , Lookup Header  4700 , Lookup Id  4806 , Lookup Type  4808 , and Taxonomy Emoji Collection  4810 . Lookup Collection  4840  contains Lookup  4800 , and Lookup Collections  4850  contains Lookup Collection  4840 . 
     A “lookup” is defined as the context expressions and pathways that comprise knowledge creation, communication, discovery, and acquisition. When the phrase “lookup” is used it refers to the lookup definition specified here in addition to Lookup  4800 , its constituent parts, and any derivatives thereof. 
     Emoji Mark  3010  illustrates an emoji mark for Lookup  4800 . Action Emoji Collection  4802  illustrates a collection of Action Emoji  3008  in  FIG. 30  for Lookup  4800 . Context Emoji Collection  4804  illustrates a collection of Context Emoji  3006  in  FIG. 30  for Lookup  4800 . Content Collection  3640  illustrates the collection of Content  3600  in  FIG. 36  for Lookup  4800 . Lookup Header  4700  illustrates the lookup header for Lookup  4800 . Lookup Id  4806  illustrates a unique identifier for Lookup  4800 . Lookup Type  4808  is reserved for a lookup enumeration for Lookup  4800 . Taxonomy Emoji Collection  4810  illustrates a collection of Taxonomy Emoji  3012  in  FIG. 30  for Lookup  4800 . Collections are illustrated by Lookup Collection  4840  and Lookup Collections  4850 . 
     An exemplary enablement is a system that enables the utilization of an emoji mark in a lookup. 
     Another exemplary enablement is a system that enables the utilization of an action emoji in a lookup. 
     Another exemplary enablement is a system that enables the utilization of a context emoji in a lookup. 
     Another exemplary enablement is a system that enables the utilization of a taxonomy emoji in a lookup. 
     Another exemplary enablement is a system that enables the use of an emoji mark within the content of a lookup. 
     Another exemplary enablement is a system that enables the use of one or more action emoji or any of its derived types within the content of a lookup. 
     Another exemplary enablement is a system that enables the use of one or more context emoji or any of its derived types within the content of a lookup. 
     Another exemplary enablement is a system that enables the use of one or more taxonomy emoji or any of its derived types within the content of a lookup. 
     Another exemplary enablement is a system that enables lookups which may be user communicated by sharing, sending, posting, and publishing. 
     Another exemplary enablement is a system that enables lookups which may be user defined, owned, and controlled. 
     Another exemplary enablement is a system that utilizes a lookup to enable the visual, audio, or tactile output of one or more context expressions. 
     Another exemplary enablement is a system that utilizes a lookup to enable the visual, audio, or tactile conveyance of attributive context for a context expression. 
       FIG. 49  illustrates Lookup Module Manager  4900  which is composed of Lookup Access Control Module  4902 , Lookup Accounting Module  4904 , Lookup Blockchain Module  4906 , Lookup Decommissioning Module  4908 , Lookup Domain Name Module  4910 , Lookup Encryption Module  4912 , Lookup Generator Module  4914 , Lookup Machine Learning Module  4916 , Lookup Mapping Module  4918 , Lookup Parsing Module  4920 , Lookup Publishing Module  4922 , Lookup Reporting Module  4924 , Lookup Settings Module  4926 , Lookup Subscription Module  4928 , and Lookup Validation Module  4930 . Lookup Domain Name Module  4910  contains Create Lookup Domain Name Algorithm  5300 . Lookup Module Manager  4900  utilizes Context Identifier Algorithm  200 , Context Expression Module Manager  2900 , Pathway Module Manager  4000 , Lookup Storage  4940 , and Lookup Module Manager Storage  4942 . 
     Lookup Module Manager  4900  illustrates the functionality and logic to manage lookups, modules, and algorithms. It illustrates modular functionality to generate, validate, parse, encrypt, decrypt, decommission, and map lookups. It exemplifies the modular functionality to create, read, update, and delete lookup data. It also illustrates modular functionality for lookup integration with blockchain, machine learning, access control, accounting, publishing, subscription, and reporting systems. Finally, it exemplifies that modules also have the functionality to manage lookups, modules, and algorithm settings. 
     Lookup Access Control Module  4902  illustrates functionality and logic for lookup integration with an access control system. It exemplifies functionality to create, read, update, and delete lookup access control data as well as the logic for lookup access control management. 
     Lookup Accounting Module  4904  illustrates functionality and logic for lookup integration with an accounting system. It exemplifies functionality to create, read, update, and delete lookup accounting data as well as the logic for lookup accounting management. 
     Lookup Blockchain Module  4906  illustrates functionality and logic for lookup integration with blockchain. It exemplifies functionality to create, read, update, and delete lookup blockchain data as well as the logic for lookup blockchain management. 
     Lookup Decommissioning Module  4908  exemplifies functionality and logic to create, read, update, and delete lookup decommissioning data as well as the logic for lookup decommissioning management. 
     Lookup Domain Name Module  4910  illustrates functionality and logic to generate lookup domain names (see, Lookup Domain Name  5200  in  FIG. 52 ). It exemplifies functionality to create, read, update, and delete lookup domain name data as well as the logic for lookup domain name generation management. It illustrates lookup domain name generation integration functionality for domain name registration and domain name systems. Lookup Domain Name Module  4910  utilizes Create Lookup Domain Name Algorithm  5300  (see,  FIG. 53 ) to create lookup domain names. 
     Lookup Encryption Module  4912  exemplifies functionality and logic to manage the encryption and decryption of lookup data as well as the logic for lookup encryption management. 
     Lookup Generator Module  4914  illustrates functionality and logic to generate lookups. It exemplifies functionality to create, read, update, and delete lookup data as well as the logic for lookup generation management. It illustrates lookup generation integration functionality for blockchain and machine learning. Lookup Generator Module  4914  also contains all the functionality and logic to generate and integrate emoji (see, Emoji  302  in  FIG. 3 ) into a lookup. It exemplifies functionality to create, read, update, and delete emoji data as part of a lookup as well as the logic for emoji generation and integration management as part of a lookup. 
     Lookup Machine Learning Module  4916  illustrates functionality and logic for lookup integration with machine learning. It exemplifies functionality to create, read, update, and delete lookup machine learning data as well as the logic for lookup machine learning management. 
     Lookup Mapping Module  4918  illustrates mapping functionality and logic for working with lookups. 
     Lookup Parsing Module  4920  illustrates parsing functionality and logic for working with lookups. Furthermore, Lookup Parsing Module  4920  illustrates functionality for parsing query and programming language in lookups. 
     Lookup Publishing Module  4922  illustrates functionality and logic for lookup integration with a publishing system. It exemplifies functionality to create, read, update, and delete lookup publishing data as well as the logic for lookup publishing management. 
     Lookup Reporting Module  4924  illustrates functionality and logic for lookup integration with a reporting system. It exemplifies functionality to create, read, update, and delete lookup reporting data as well as the logic for lookup reporting management. 
     Lookup Settings Module  4926  exemplifies functionality and logic to create, read, update, and delete lookup settings data as well as the logic for lookup settings management. 
     Lookup Subscription Module  4928  illustrates functionality and logic for lookup integration with a subscription system. It exemplifies functionality to create, read, update, and delete lookup subscription data as well as the logic for lookup subscription management. 
     Lookup Validation Module  4930  illustrates validation functionality and logic for working with lookups. Furthermore, Lookup Validation Module  4930  illustrates functionality for validating the generation of lookups. 
     Context Identifier Algorithm  200  illustrates the Context Identifier Algorithm  200  in  FIG. 2 . Context Expression Module Manager  2900  illustrates Context Expression Module Manager  2900  in  FIG. 29 . Pathway Module Manager  4000  illustrates Pathway Module Manager  4000  in  FIG. 40 . Lookup Module Manager Storage  4942  and Lookup Storage  4940  illustrate data storage systems such as a database. Lookup Module Manager  4900 , its constituent parts, and derivatives thereof, may access and utilize Context Identifier Algorithm  200 , Context Expression Module Manager  2900 , Pathway Module Manager  4000 , Lookup Storage  4940  and Lookup Module Manager Storage  4942 . 
     An exemplary enablement is a system that utilizes blockchain for permanent storage of lookups and all of their versions to provide a system of record. For example, content on the internet today is susceptible to memory holing 58 , deliberate or not. While there are internet archives such as the Internet Archive (see, archive.org), there are known issues (e.g., videos removed from YouTube), limitations (e.g., Internet content may change significantly between indexing), explicate government censorship, and complicit revisionism (e.g., labeling already-deleted articles about COVID-19, as ‘disinformation’ 59 ) that diminish their capacity to document important changes happening in our health, geopolitics, economy, etc. Historically, for centuries, newspapers have served as a public record. However, with the demise of traditional publishing, we&#39;ve lost that capacity. This enablement restores the societal benefit of a permanent and public system of record.  58  “Memory Hole.” Wikipedia, the Free Encyclopedia, Wikimedia Foundation, Inc, 1 September 2004, en.wikipedia.org/wiki/Memory_hole. Accessed 21 May 2020. 59  “What Critical Thinking? Wayback Machine is Now Complicit in Big Tech Censorship.” RT International, 19 May 2020, www.rt.com/op-ed/489118-wayback-machine-big-tech-censorship/. 
     Another exemplary benefit is a system that enables users to control access to and discovery of content through lookups. For example, user&#39;s regain control of content distribution through lookups which enable them cut out the platform operators who engage in shadow banning, 60  ensuring that the content that users share is seen by users with whom they share.  60  “Shadow Banning.” Wikipedia, the Free Encyclopedia, Wikimedia Foundation, Inc, 17 Dec. 2011, en.wikipedia.org/wiki/Shadow_banning. Accessed 21 May 2020. 
     Another exemplary enablement is a system that utilizes machine learning and streaming analytics to generate lookup use ranking by context expression. For example, locality and temporality contexts may be used to generate ranking for users in their location; moreover, temporality contexts may be used to provide a narrow or broad ranking in terms of time and date. Considering pretext, action and taxonomy attributive contexts in context expressions provide unique capabilities for complex analytics for ranking. 
     Another exemplary enablement is a system that utilizes machine learning and streaming analytics to generate lookup use ranking by context expression for prediction. For example, users benefit from predictive lookup use ranking by context expression through user interface input mask and dropdown suggestions in real-time, by locality, by action, taxonomy, or any context that has been crowd sourced. 
     Another exemplary enablement is a system that makes available lookup generation, management and utilization through an Application Programming Interface (API). 
     Another exemplary enablement is a system that makes available lookup generation, management and utilization through a Software Development Kit (SDK). 
       FIG. 50  illustrates Lookups  5000  which is composed of Knowledge Domain  5002 . Knowledge Domain  5002  contains Data  5004 , Knowledge  5006 , and Information  5008 . Knowledge  5006  connects to Data  5004  at Line Segment  5010 . Knowledge  5006  connects to Information  5008  at Line Segment  5012 . Quaesitum  5016  is connected to Knowledge Domain  5002  at Line Segment  5014 . Quaesitum  5016  is connected to Context Expression  2800  at Line Segment  5018 . Context Expression  2800  is connected to Pathway Base Collections  3890  at Line Segment  5020 . Quaesitum  5016  is connected to Pathway Base Collections  3890  at Line Segment  5022 . Pathway  3800  is recursively connected to Pathway  3800  at Line Segment  5024 . Context Expression  2800  is connected to Pathway  3800  at Line Segment  5026 . Pathway Base Collections  3890  is connected to Pathway  3800  at Line Segment  5028 . Lookup  4600  is recursively connected to Lookup  4600  at Line Segment  5030 . Lookup  4600  is connected to Pathway  3800  at Line Segment  5032 . Pathway  3800  is connected to Lookup  4600  at Line Segment  5034 . Lookup  4600  is connected to Connection  5070  at Line Segment  5036 . Lookup  4600  is connected to Placard  5060  at Line Segment  5038 . Placard  5060  is connected to Pathway  3800  at Line Segment  5040 . Placard  5060  is connected to Connection  5070  at Line Segment  5042 . Connection  5070  is connected to Knowledge  5006  at Line Segment  5044 . 
     Lookups  5000  illustrates Knowledge Domain  5002 . A “knowledge domain” is defined as a domain consisting of data 61 , information 62 , and knowledge 63 . It represents that “there are known knowns. These are things that we know that we know. There are known unknowns. That is to say, there are things that we know we don&#39;t know. But there are also unknown unknowns. There are things we don&#39;t know we don&#39;t know.” 64  Knowledge  5006 , or “knowledge,” is used to represent Knowledge Domain  5002  as well as Data  5004 , or “data,” as illustrated by Line Segment  5010  and Information  5008 , or “information,” as illustrated by Line Segment  5012 .  61  “data, n.” OED Online, Oxford University Press, December 2019, www.oed.com/view/Entry/296948. Accessed 8 Feb. 2020. 62  “information, n.” OED Online, Oxford University Press, December 2019, www.oed.com/view/Entry/95568. Accessed 8 Feb. 2020. 63  “knowledge, n.” OED Online, Oxford University Press, March 2020, www.oed.com/view/Entry/104170. Accessed 19 May 2020. 64  “Defense.gov Transcript: DoD News Briefing—Secretary Rumsfeld and Gen. Myers.” United States Department of Defense (defense.gov), archive.defense.gov/Transcripts/Transcript.aspx?TranscriptID=2636. 
     Quaesitum  5016  illustrates “quaesitum” as defined in the OED, “that which is sought; the answer to a problem.” 65  It is also defined in Latin as that which is “sought out, looked for.” 66  Quaesitum  5016  connects to Knowledge Domain  5002  at Line Segment  5014  illustrating knowledge which is sought out, knowledge which is looked for, or knowledge as the answer to a problem. Context Expression  2800  illustrates context expressions. Line Segment  5018  illustrates that context expressions can be derived out of quaesitum or that context expressions may be attributive context for a quaesitum subject. A “quaesitum subject” is defined as that which forms or is chosen as the matter being sought out, looked for, or answering a problem. Pathway Base Collections  3890  illustrates a collection of Pathway Base Collection  3840  in  FIG. 38 ; each Pathway Collection  3840  is a collection of Pathway Base  3800  in  FIG. 38 , with each pathway derived out of a context expression. This is illustrated by Line Segment  5020  connecting Pathway Base Collections  3890  and Context Expression  2800 . Each pathway&#39;s context expression, as stated above, can be derived out of quaesitum or that context expression may be attributive context for a quaesitum subject. This is illustrated by Line Segment  5022 .  65  “quaesitum, n.” OED Online, Oxford University Press, December 2019, www.oed.com/view/Entry/155792. Accessed 10 Feb. 2020. 66  William Whitaker&#39;s Words, archives.nd.edu/cgi-bin/wordz.pl?keyword=quaesitum. 
     Pathway  3800  illustrates pathways as well as their relationship to Context Expression  2800  as illustrated by Line Segment  5026  and Line Segment  5028 . Pathway  3800  also illustrates their inclusion in Pathway Base Collections  3890  as illustrated by Line Segment  5028 . Line Segment  5024  illustrates that a pathway may reference one or more pathways. Lookup  4600  illustrates a lookup. Lookup  4600  connects to Pathway  3800  at Line Segment  5032  illustrating that a lookup may have one or more pathways. Lookup  4600  connects to Pathway  3800  at Line Segment  5034  illustrating that a pathway may have one or more lookups. Line Segment  5030  illustrates that a lookup may reference one or more lookups. Lookup  4600  connects to Connection  5070  via Line Segment  5036 , and Connection  5070  connects to Knowledge  5006  via Line Segment  5044 , illustrating a lookup&#39;s connection. Connection  5070  illustrates “connection” which is defined as the creating, communicating, discovering, and acquiring of quaesitum. 
     Placard  5060  illustrates “placard” which is defined as the visual, audio, or tactile representation of the data, information, or knowledge that is publicly or privately displayed as a lookup. Pathway  3800  connects to Placard  5060  via Line Segment  5040  illustrating that a pathway may utilize one or more placards and a placard may reference one or more pathways. Lookup  4600  connects to Placard  5060  via Line Segment  5038  illustrating that a lookup may utilize one or more placards and a placard may reference one or more lookups. Placard  5060  connects to Connection  5070  via Line Segment  5042 , and Connection  5070  connects to Knowledge  5006  via Line Segment  5044 , illustrating a placard&#39;s connection. 
     Lookups  5000  illustrates a system that allows users to create, communicate, discover, and acquire an infinite amount of data, information and knowledge—starting with Quaesitum  5016  and resulting in Connection  5038 . It aggregates and makes available every single lookup that is created about a particular quaesitum subject, so that those who consume the lookups see the data, information, and knowledge as it actually is, without any algorithmic manipulation or social engineering. Users of the system are able to create an unlimited number of lookups as well as consume an unlimited number of lookups, which allows for incredibly rich, up to date, dynamic, crowd sourced knowledge. Lookups  5000  also represents a connection discovery platform, enabling users to create meaningful connections (e.g. individual to individual, or business, or government, etc.) in new ways that have not been available before. 
     An exemplary enablement is a system that enables the creation and communication of data, information, and knowledge (i.e. users create lookups visually, audible, or tactilely). 
     Another exemplary enablement is a system that enables the discovery and acquisition of data, information, and knowledge (i.e. users consume lookups visually, audibly, or tactilely). 
     Another exemplary enablement is a system that enables users, such as, but not limited to, individuals, businesses, non-profits, and government entities, to be found, whereby a user creates one or more context expressions and their associated pathways which enables other users to discover their lookups. The following examples are five lookups that were created by a single individual user whereby each context expression describes something unique about the individual. 
     Community Affiliation Lookup Example #1: 
     “Community: Yale University|Name: William Thomson|Attended: 1985-1989” 
     In this example, the user is describing their affiliation to a community. The community could be a workplace, school, neighborhood, etc. that the user specifies in the context expression, along with the name that people knew them by in that community. It could also include a date or range of dates, indicating when they were a part of that community. 
     Name and Location Lookup Example #2: 
     “Name: William Thomson|City: New Haven|When: 1985-1991” 
     In this example, the user is providing their name and a city. It may also include a date or range of dates, indicating when they were at that location. 
     Name and Location Lookup Example #3: 
     “Name: William Thomson|Street: Mansfield Street|When: 1989-1991” 
     In this example, the user is providing their name and a street. It may also include a date or range of dates, indicating when they were at that location. 
     Pseudonym and Location Lookup Example #4: 
     “Nickname: Will|Where: Stiles|When: 1985-1989” 
     In this example, the user is providing the nickname people knew them by at a college for a specified number of years. 
     In each of these examples, the user gets to choose what they want people to see (i.e. opt-in), and the system creates individual pathways, including for each year, plus the range of years inputted. 
     Platform Affiliation Lookup Example #5: 
     “Twitter: @willyboy” 
     In this example, the user is providing their handle on one of the social media platforms they use. Other users will be able to use lookups to access the user&#39;s information on that platform plus anything related to what&#39;s been said using that handle. In this instance, there is no opt-in. The context expression and its associated pathways and lookups are public. 
     Platform Affiliation Lookup Example #6: 
     “:fire::earth::waten:air:” 
     In this example, the user opts to provide an emoji mark as a means to be found privately. 
     An exemplary enablement is a system that enables lookups which may be opt-in or opt-out. 
     Another exemplary enablement is a system that enables lookups which may be private or public. 
     Another exemplary enablement is a system that enables users to create context expressions and their associated pathways and lookups wherein the content resides outside of the system. For example, our user William Thomson sees a blog piece on Zero Hedge that points out how Twitter is the impetus for cancel culture and deplatforming as well as the negative effect Twitter has had on people by stealing their livelihoods. He creates a context expression and associated pathway and lookup that contains the link to the blog piece for other users to discover. Our user William Thomson then takes his own blog piece that he wrote about how he was personally doxed by Twitter users and lost his job based on incorrect information and creates a context expression and associated pathway and lookup which contains his blog piece. He also creates context expressions and associated pathways and lookups using “#Deplatforming”, “#Cancel Culture”, “#I&#39;ve been doxed”, “#Thief” “@jack” for users to discover more data, information, and knowledge, with no limits imposed on his freedom of speech. 
     Another exemplary enablement is a system whereby lookups enable the physical separation of hosting content and content location services while providing users unified ownership and control over their pathways to connect others to content. 
     Another exemplary enablement is a system that enables lookups which provide incredibly accurate and current data, information, and knowledge since users can update lookups in real-time and make those changes available in real-time. 
     Another exemplary enablement is a system that enables pathway clustering for speed and efficiency, whereby only exact match lookups are served up in response to quaesitum. 
     Another exemplary enablement is a system that provides an extensibility, such that it can be integrated with traditional subjecting systems and search engines. 
     Another exemplary enablement is a system that enables crowd sourced pathways to subjects pointed to by traditional subjecting systems and search engines. 
     Another exemplary enablement is a system that enables lookups which may capture crowd sourced context and crowd sourced context mapping. Oftentimes, things like misspellings, languages, words, colloquialisms, idioms, phrases, abbreviations, acronyms as well as location and the passage of time make it difficult to find data, information, and knowledge on the internet. Traditional subjecting systems use singular and rigid taxonomies that don&#39;t have the capacity to capture crowd sourced context attribution. Search engine algorithms may infer context attribution, but they are biased, flawed, and intentionally manipulated for profit or social engineering. The present disclosure highlights and enables the multi-dimensional nature of knowledge, whereby crowd sourcing gives voice to all different cultures, experiences, moments in time, regional differences, capacities to understand, points of view, etc. Learning is further amplified because every single distinction of data, information, and knowledge can be captured and communicated without limits. For example, consider the index of a book. While an author, editor, etc. may accurately present certain aspects of the book to be indexed, they are doing it from the perspective of understanding having originated the content for the subject. Given the natural proclivity for bias, error, etc. it&#39;s not possible for them to create a comprehensive, all knowing index from a universe of possible perspectives about the subject (i.e. indexes are imperfect). Lookups  5000  enables a crowd sourced perspective, understanding, and insight to create many, many pathways as lookups to the subject, providing a crowd sourced index vis a vie crowd sourced context and crowd sourced context mapping. 
     Another exemplary enablement is a system that provides for a crowd sourced epistemology, that is, it provides processes for investigating the origin of knowledge and documents the historical development of its form and meaning. 
     Another exemplary enablement is a system that enables crowd sourced distinctions or accurately observed differences of knowledge. 
     Another exemplary enablement is a system that enables context expressions and their associated pathways and lookups to capture commonly accepted contexts or conventions as well as uncommonly accepted context or conventions. 
     Another exemplary enablement is a system that eliminates ambiguity through lookups as well as their associated one or more context expressions and one or more pathways. 
     Another exemplary enablement is a system where multiple pathways are defined by users using formal classification systems (e.g. subject headings) as well as informal user derived classification systems (e.g. folksonomy). 
     Another exemplary enablement is a system that enables lookups which may capture local colloquialisms and local idioms. 
     Another exemplary enablement is a system that enables the modeling of human colloquial inquiry. 
     Another exemplary enablement is a system that enables lookups which may capture shared personal and social experiences in a community (e.g. how one thinks, learns, seeks out knowledge, based on specific experiences in life). For example, consider how human beings interact and talk when they discover connections with one another. When they find out someone went to the same school or worked at the same place, the conversation goes, “Oh really, I went there, worked there at this time. What about you? Do you know so and so? Yes, I know so and so.” Lookups are a way for us to do this digitally. It is not just about discovering data, information, and knowledge; it is also about discovering connections with the individual, business, government, etc. behind the data, information, and knowledge. 
     Another exemplary enablement is a system that enables lookups which may create economic connections (i.e. increased opportunities for commerce). For example, if a user looks up “Boise Café,” they do not see a million results in a search engine. They only see (or hear, or touch) the 10 or 15 cafés in Boise that have created context expressions and their associated pathways and lookups that are specific to “Boise Café.” The café owner is happy, and the customer is happy, because they made a quick and easy connection via lookups that resulted in a hot cup of coffee exchanging hands for money. 
     Another exemplary enablement is a system that enables lookups which may be secured through authentication and authorization. 
     Another exemplary enablement is a system that enables secret lookups through the inclusion of a passcode in a context expression. 
     Another exemplary enablement is a system that enables lookups which may be monetized or tokenized by means such as, but not limited to, subscriptions, paywalls, and microtransactions (e.g. broker curated lookup content for profit). 
     Another exemplary enablement is a system that enables users to follow the curated lookup content of another user. 
     Another exemplary enablement is a system that enables lookups which may be ephemeral or permanent. 
     Another exemplary enablement is a system that enables lookups which may have an expiration DateTime whereby users may attribute an expiration date or the system may provide a default expiration date. 
     Another exemplary enablement is a system that enables lookups which may have a specific DateTime, collection of DateTimes, DateTime range and/or DateTime interval which provides temporality context. 
     Another exemplary enablement is a system that enables lookups which may have a specific or broad locality (such as, but not limited to, street address, city, state, country, geolocation) which provides locality context. 
     Another exemplary enablement is system that enables lookup context to enable pertinent and concise connections to quaesitum. 
     Another exemplary enablement is a system that enables actionable context to enable pertinent and concise connections to quaesitum. 
     Another exemplary enablement is a system that enables taxonomy context to enable pertinent and concise connections to quaesitum. 
     Another exemplary enablement is a system that enables locality context to enable pertinent and concise connections to quaesitum. 
     Another exemplary enablement is a system that enables temporality context to enable pertinent and concise connections to quaesitum. 
     Another exemplary enablement is a system that enables prefix context to enable pertinent and concise connections to quaesitum. 
     Another exemplary enablement is a system that enables lookups which allow for message routing and queues. 
     Another exemplary enablement is a system that enables lookups which allow for actionable single purpose events or processes such as lookups as POST, PUT and DELETE. 
     Another exemplary enablement is a system that enables lookups which allow for query and execution processing. 
     Another exemplary enablement is a system that enables lookups which allow for processing through API. 
     Another exemplary enablement is a system that enables lookups which allow for loose coupling between pathways and resources. 
     Lookup Systems  2614  in  FIG. 26  encapsulates and utilizes lookups as illustrated in  FIGS. 46 through 50  and the specifications that support the figures. 
     An exemplary enablement is a system that makes lookups available through an Application Programming Interface (API). 
     Another exemplary enablement is a system that makes lookups available through a Software Development Kit (SDK). 
       FIG. 51  illustrates Context Systems  2610  which connects to Context Expression Module Manager  2900 , Pathway Systems  2612  which connects to Pathway Module Manager  4000 , and Lookup Systems  2614  which connects to Lookup Module Manager  4900 . 
     Lookup Systems and Modules  5100  illustrates Context Systems  2610  and its relationship to Context Expression Module Manager  2900 , Pathway Systems  2612  and its relationship to Pathway Module Manager  4000 , and Lookup Systems  2614  and its relationship to Lookup Module Manager  4900 . 
       FIG. 52  illustrates Lookup Domain Name  5200  which is comprised of Domain Name  5202 , Has Locality  5204 , Has Taxonomy  5206 , Has Temporality  5208 , Locality Context Index  5210 , Pathway Index  5212 , Taxonomy Context Index  5214 , and Temporality Context Index  5216 . 
     Lookup Domain Name  5200  illustrates the lookup domain name composition. Domain Name  5202  illustrates the domain name for Lookup Domain Name  5200 . Has Locality  5204  illustrates a Boolean indicating whether or not Lookup Domain Name  5200  has a locality. Has Taxonomy  5206  illustrates a Boolean indicating whether or not Lookup Domain Name  5200  has a taxonomy. Has Temporality  5208  illustrates a Boolean indicating whether or not Lookup Domain Name  5200  has a temporality. Locality Context Index  5210  illustrates a locality context index for Lookup Domain Name  5200 . Pathway Index  5212  illustrates a pathway index for Lookup Domain Name  5200 . Taxonomy Context Index  5214  illustrates a taxonomy context index for Lookup Domain Name  5200 . Temporality Context Index  5216  illustrates a temporality context index for Lookup Domain Name  5200 . 
       FIG. 53  illustrates Create Lookup Domain Name Algorithm  5300 . The algorithm starts with Initial  5302  which is connected to Input Lookup, Derive Index and Ids  5304 . Input Lookup, Derive Index and Ids  5304  is connected to Lookup  5305 , Lookup Module Manager  4900 , Lookup Domain Name  5350 , and Concatenate Top-Level Domain Label, Dot, Pathway Index  5306 . Concatenate Top-Level Domain Label, Dot, Pathway Index  5306  is connected to Lookup Domain Name  5350 , Top-Level Domain Label  5307 , and Decision  5308 . Decision  5308  is connected to note [Has Locality]  5310 , which illustrates Boolean criteria. If Has Locality is true, control proceeds to Concatenate Domain Name, Dot, Locality Context Index  5312  which is connected to Lookup Domain Name  5350  and Decision  5314 . If Has Locality is false, control proceeds to Decision  5314  via Line Segment  5318 . Decision  5314  is connected to note [Has Taxonomy]  5316 , which illustrates Boolean criteria. If Has Taxonomy is true, control proceeds to Concatenate Domain Name, Dot, Taxonomy Context Index  5320  which is connected to Lookup Domain Name  5350  and Decision  5322 . If Has Taxonomy is false, control proceeds to Decision  5322  via Line Segment  5326 . Decision  5322  is connected to note [Has Temporality]  5324 , which illustrates Boolean criteria. If Has Temporality is true, control proceeds to Concatenate Domain Name, Dot, Temporality Context Index  5330  which is connected to Lookup Domain Name  5350  and Final  5332 , illustrating the end of the algorithm. If Has Temporality is false, control proceeds to Final  5328 , illustrating the end of the algorithm. 
     Create Lookup Domain Name Algorithm  5300  illustrates the creation of Lookup Domain Name  5350  using context intelligence. The algorithm starts at Initial  5302 . Input Lookup, Derive Index and Ids  5304  inputs a lookup illustrated by Lookup  5305 . Input Lookup, Derived Index and Ids  5304  creates a new instance of Lookup Domain Name  5350  and determines Boolean values for Has Locality  5204 , Has Taxonomy  5206 , and Has Temporality  5208  (see,  FIG. 52  respectively) from Lookup  5305 . The Boolean values are assigned to the respective members in Lookup Domain Name  5350 . Input Lookup, Derive Index and Ids  5304  utilizes Lookup Module Manager  4900  to generate Pathway Index  5212  in  FIG. 52 . If Has Locality  5204  in  FIG. 52  is true, then Input Lookup, Derive Index and Ids  5304  utilizes Lookup Module Manager  4900  to generate Locality Context Index  5210  in  FIG. 52 . If Has Taxonomy  5206  in  FIG. 52  is true, then Input Lookup, Derive Index and Ids  5304  utilizes Lookup Module Manager  4900  to generate Taxonomy Context Index  5214  in  FIG. 52 . If Has Temporality  5208  in  FIG. 52  is true, then Input Lookup, Derive Index and Ids  5304  utilizes Lookup Module Manager  4900  to generate Temporality Context Index  5216  in  FIG. 52 . Locality Context Index  5210 , Taxonomy Context Index  5214 , and Temporality Context Index  5216  are assigned to their respective members in Lookup Domain Name  5350 . Top-Level Domain Label  5307  may be a setting or provided as a parameter in the call to Create Lookup Domain Name Algorithm  5300 . It contains a top-level domain label. Concatenate Top-Level Domain Label, Dot, and Pathway Index  5306  concatenates Top-Level Domain Label  5307 , dot 67 , Pathway Index  5212  (see,  FIG. 52 ) in Lookup Domain Name  5350 . The concatenated value is assigned to Domain Name  5202  (see,  FIG. 52 ) in Lookup Domain Name  5350 . Decision  5308  determines if Has Locality  5204  in Lookup Domain Name  5350  is true. If true, Concatenate Domain Name, Dot, Locality Context Index  5312  concatenates Domain Name  5202 , dot, Locality Context Index  5210 . The concatenated value is assigned to Domain Name  5202 , and control proceeds to Decision  5314 . If Has Locality  5204  is false, control proceeds to Decision  5314 , as illustrated by Line Segment  5318 . Decision  5314  determines if Has Taxonomy  5206  in Lookup Domain Name  5350  is true. If true, Concatenate Domain Name, Dot, Taxonomy Context Index  5320  concatenates Domain Name  5202 , dot, Taxonomy Context Index  5214 . The concatenated value is assigned to Domain Name  5202 , and control proceeds to Decision  5322 . If Has Taxonomy  5206  is false, control proceeds to Decision  5322 , as illustrated by Line Segment  5326 . Decision  5322  determines if Has Temporality  5208  in Lookup Domain Name  5350  is true. If Has Temporality  5208  is false, the algorithm concludes at Final  5328 , returning Lookup Domain Name  5350 . If true, Concatenate Domain Name, Dot, Temporality Context Index  5330  concatenates Domain Name  5202 , dot, Temporality Context Index  5216 . The concatenated value is assigned to Domain Name  5202 , and the algorithm concludes at Final  5332 , returning Lookup Domain Name  5350 .  67  Peter. “ASCII Code. , Dot, Full Stop.” The Complete Table of ASCII Characters, Codes, Symbols and Signs, American Standard Code for Information Interchange, theasciicode.com.ar/ascii-printable-characters/dot-full-stop-ascii-code-46.html. 
     Alternative embodiments of Create Lookup Domain Name Algorithm  5300  provide method overloads to take parameters of settings for Context Identifier Algorithm  200  (e.g., Culture and Case Settings  213  and Sort Settings  222  in  FIG. 2 ) and Top-Level Domain Label  5307 . 
     In some embodiments, Locality Context Index  5210  is the equivalent of Lookup Header Locality Id  4720  in  FIG. 47 , and Temporality Context Index  5216  is the equivalent of Lookup Header Temporality Id  4708  in  FIG. 47 . Both Ids may be found in Lookup Header  4700  in Lookup  4800  in  FIG. 48 . 
     Create Lookup Domain Name Algorithm  5300  illustrates context intelligence use for deriving machine readable domain names which may be used to replace human readable domain names, creating an abstraction whereby humans utilize context expressions to create domain names without the underlying technical syntactical requirements of the current domain name system paradigm. Furthermore, the context intelligence domain name abstraction enables domain names to be created with attributive context. Locality Context Index  5210  and Lookup Domain Name  5200  illustrate how an attributive context of locality may be used to tie a domain name to a specific locality. Therefore, the domain name can be utilized in many localities. This reflects, for example, how similar business names can be found across multiple locations. Using a locality as an attributive context for the business named “AAA Plumbing” in a context expression enables the same domain name to be used in micro locations such as a neighborhood or town; moreover, well known brands may utilize regional or national locality attributive context. 
     An exemplary enablement is a system that enables the generation of context intelligent domain names based on context expressions. 
     Another exemplary enablement is a system that enables the generation of context intelligent domain names based on attributive context for a context expression. 
     Another exemplary enablement is a system that enables the generation of context intelligent domain names that may be used more than once. 
       FIG. 54  illustrates Lookup Domain Name Registration  5400  where User  5402 - 1  has Lookup Browser  5404 - 1  which connects via Network(s)  2608  to Lookup Apps and Systems  2600 , Domain Name Registrar  5422 , and Domain Name System (DNS)  5424 . Domain Name Registrar  5422  is connected to Domain Name System (DNS)  5424  via Network(s)  2608 . User  5402 - 2  has Lookup Browser  5404 - 2 , and User  5402 -N has Lookup Browser  5404 -N. The ellipse at  5407  between Lookup Browser  5404 - 2  and Lookup Browser  5404 -N illustrates that there are N number of lookup browsers for every user on the network. Every browser connects via Networks(s)  2608  to Lookup Apps and Systems  2600 , Domain Name Registrar  5422 , and Domain Name System (DNS)  5424 . Lookup Browser  5404 - 1  generates Lookup Domain Name  5450  (see, Lookup Domain Name  5200  in  FIG. 52 ). Network(s)  2608  also provides access to Social Media Systems  2620  and Blockchain  2622  in  FIG. 26 . 
     Lookup Domain Name Registration  5400  illustrates the lookup domain name systems architecture for creating context intelligent domain names. Lookup Apps and Systems  2600  illustrates the utilization of the context intelligent domain name systems (e.g., Context Systems  2610 , Pathway Systems  2612 , and Lookup Systems  2614 ) by Lookup Browser  5404 - 1 , Lookup Browser  5404 - 1 , through Lookup Browser  5404 -N (see, Lookup Browser  5600  in  FIG. 56 ) to create and manage context intelligent domain names. For example, User  5402 - 1  utilizes Lookup Browser  5404 - 1  to create Lookup Domain Name  5450  through Lookup Module Manager  4900  and Create Lookup Domain Algorithm  5300  (see,  FIG. 53  respectively) in Lookup Systems  2614  illustrated by Lookup Apps and Systems  2600  and accessible through Network(s)  2608 . 
     Lookup Domain Name  5450  registration and utilization follows industry standards to register and utilize domain names as illustrated by Domain Name Registrar  5422  and Domain Name System (DNS)  5424  accessible through Network(s)  2608 . Domain management of and association with underlying internet protocol follows industry standards; however, users utilize context expressions and a context intelligent domain name systems abstraction layer to interface with internet websites and lookups. 
     An exemplary enablement is a system that creates a lookup domain name systems architecture that provides a context intelligent domain name systems abstraction layer. 
     Another exemplary enablement is a system that utilizes a context intelligent domain name systems abstraction layer which enables users to create, utilize, and manage context intelligent domain names based on a context expression. 
     Another exemplary enablement is a system that utilizes a context intelligent domain name systems abstraction layer which enables users to create, utilize, and manage context intelligent domain names based on attributive context for a context expression. 
       FIG. 55  illustrates Browser Module Manager  5500  which contains Browser Blockchain Module  5502 , Browser Context Expression User Interface Module  5504 , Browser Pathway Module  5506 , Browser Lookup DNS Module  5508 , Browser Lookup Module  5510 , Browser Lookup New Media Module  5512 , Browser Lookup User Interface Module  5514 , Browser Machine Learning Module  5516 , Browser Placard Layout Module  5518 , and Browser Settings Module  5520 . Browser Module Manager  5500  is connected to Lookup Systems and Modules  5100 . 
     Browser Module Manager  5500  illustrates the functionality and logic to manage browser modules and algorithms. Browser Module Manager  5500  and its modules utilize Lookup Systems and Modules  5100  to create, utilize, and manage context expressions, pathways, and lookups. 
     Browser Blockchain Module  5502  illustrates functionality and logic for context expression, pathway, and lookup integration with blockchain. Browser Context Expression User Interface Module  5504  illustrates functionality and logic to create, utilize, and manage context expressions, pathways, and lookups in a user interface. Browser Pathway Module  5506  illustrates pathway creation, utilization, and management functionality and logic for pathways. Browser Lookup DNS Module  5508  illustrates Lookup Domain Name  5450  (see  FIG. 5754 ) creation, utilization, and management functionality and logic for lookup domain names. Browser Lookup Module  5510  illustrates lookup creation, utilization, and management functionality and logic for lookups. Browser Lookup New Media Module  5512  illustrates a reserved module whose functionality brings traditional media forward into Internet 2.0 as new media. Browser Lookup User Interface Module  5514  illustrates user interface specific functionality and logic to support Browser Manager Module  5500 . Browser Machine Learning Module  5516  illustrates functionality and logic for context expression, pathway, and lookup integration with machine learning. Browser Placard Layout Module  5518  illustrates user interface layout functionality and logic for working with placards and placard integration with context expressions, pathways, and lookups. Browser Settings Module  5520  exemplifies functionality and logic to create, utilize and manage context expression, pathway, and lookup settings. 
       FIG. 56  illustrates Lookup Browser  5600  which is comprised of Lookup Browser User Interface  5602 . Lookup Browser User Interface  5602  contains Context Expression User Interface  5604 , Lookup  5606 , Placard  5608 - 1 , Placard  5608 - 2 , and Placard  5608 -N. The ellipse at  5610  illustrates that there are N number of placards in Lookup Browser User Interface  5602 . Lookup Browser  5600  connects to Browser Module Manager  5500 , Lookup Apps and Systems  2600 , and Lookup Domain Name  5200 . Lookup Browser  5600  also contains Settings Emoji  5620 , Website Emoji  5622 , Lookup Emoji  5624 , and Cafe Emoji  5626 . Settings Emoji  5260  is a settings emoji and is represented by the emoji shortcode “:settings:”, Website Emoji  5622  is a website emoji and is represented by the emoji shortcode “:website:”, Lookup Emoji  5624  is a lookup emoji and is represented by the emoji shortcode “:lookup:” Cafe Emoji  5626  illustrates a cafe emoji and is represented by the emoji shortcode “:cafe:”. 
     Lookup Browser  5600  illustrates a new approach to internet browsing by utilizing context expressions, pathways, lookups, and lookup domain names. Lookup Browser  5600  is the user interface that utilizes a context intelligent abstraction layer to interface between context expressions, pathways, lookups, and lookup domain names and the internet as it stands today—hereto referred to as “Internet 1.0.” Beyond the backward compatibility with Internet 1.0, Lookup Browser  5600  ushers in a new paradigm for knowledge creation, communication, discovery, and acquisition through context expressions, pathways, lookups, and lookup domain names called Internet 2.0. 
     An exemplary enablement is a system that enables a browser to create, utilize, and manage context expressions. 
     Another exemplary enablement is a system that enables a browser to create, utilize, and manage pathways. 
     Another exemplary enablement is a system that enables a browser to create, utilize, and manage lookups. 
     Another exemplary enablement is a system that enables a browser to create, utilize, and manage lookup domain names. 
     Another exemplary enablement is a system that enables a browser to create, utilize, and manage placards. 
     Lookup Browser User Interface  5602  illustrates the classic interface of an internet browser containing Context Expression User Interface  5604 , Lookup  5606 , as well as Placard  5608 - 1 , Placard  5608 - 2 , and Placard  5608 -N. Context Expression User Interface  5604  illustrates a lookup input for user data entry (e.g., in HTML5, &lt;input type=“text” id=“lookup” name=“lookup”&gt;). Lookup  5606  illustrates a button to initiate a lookup (e.g., in HTML5, &lt;input type=“submit” value=“Submit”&gt;). Placard  5608 - 1 , Placard  5608 - 2 , and Placard  5608 -N illustrate content results from a lookup. 
     Lookup Browser User Interface  5602  connects to Browser Module Manager  5500 , Lookup Domain Name  5200 , and Lookup Apps and System  2600  to illustrate the browser connection to and utilization of each the respective elements. 
     Users create lookups by defining context expressions and pathways, and users enter context expressions to find lookups. Context Expression User Interface  5604  illustrates lookup composition and results through placards. Prefixes enable browser specific processing for lookups. For example, consider the Context Expression Tuple  3340  (see,  FIG. 33 ), “:settings: :locality::new_haven:” illustrates Settings Emoji  5620  plus Locality Emoji  3209  and New Haven Emoji  3205  in  FIG. 32 . The Settings Emoji  5620  prefix is used by Lookup Browser User Interface  5602  to work with browser settings to set the location to New Haven. 
     An exemplary enablement is a system that utilizes context expressions to configure a browser. 
     Prefixes in context expressions enable contextual disambiguation. For example, consider a context expression in Context Expression User Interface  5604  which contains either a Website Emoji  5622  or Lookup Emoji  5624  prefix. The following examples illustrate contextual disambiguation for lookups utilizing Context Expression Tuple  3390  (see,  FIG. 33 ): 
     Contextual Disambiguation Example 1: 
     “:website: @Pepe&#39;s Pizza” 
     Contextual Disambiguation Example 2: 
     “:lookup: @Pepe&#39;s Pizza” 
     Each prefix in their respective context expression derives unique pathways. In Contextual Disambiguation Example 1, Lookup Browser  5600  will display the Pepe&#39;s Pizza website content for the context expression with the Website Emoji  5622  prefix. In Contextual Disambiguation Example 2, Lookup Browser  5600  will display Business Listing Summary Placard  5800  (see,  FIG. 58 ) for Pepe&#39;s Pizza. 
     An exemplary enablement is a system that utilizes context expression prefixes to enable contextual disambiguation for lookups. 
     Internet 2.0 lookups may offer exact match results, where users focus on concise and pertinent results from context expressions and pathways. The following example illustrates an exact match lookup utilizing Context Expression Tuple  1190 : 
     Exact Match Example 1: 
     “#New Haven Pizza” 
     In Exact Match Example 1, Lookup Browser  5600  will display results for the example context expression and pathway associated content. 
     An exemplary enablement is a system that enables exact match results for lookups. 
     Each lookup result displays rich contextual information to inform the end user. Context emoji, action emoji, taxonomy emoji, and emoji mark may be available for enriching content display as well as lookup header information such as prefix, context expression, temporality, locality, inception, and expiration. 
     An exemplary enablement is a system that utilizes context emoji, action emoji, taxonomy emoji, and emoji mark for content display. 
     Another exemplary enablement is a system that utilizes prefix, context expression, temporality, locality, inception, and expiration information for content display. 
       FIG. 57  illustrates Business Listing Placard  5700  which contains Business Address  5702 , Business App Collection  5704 , Business Attribution  5706 , Business Calendar Collection  5708 , Business Contact Collection  5710 , Business Coupon Collection  5712 , Business Description  5714 , Business Hours  5716 , Business Logo  5718 , Business Name  5720 , Business Offering Collection  5722 , Business Id  5724 , Business Profile Picture Collection  5726 , and Business Specials Collection  5728 . 
     Business Listing Placard  5700  illustrates a placard providing specifics for a business listing. Business Address  5702  illustrates a business address placard. Business App Collection  5704  illustrates a business app collection placard. Business Attribution  5706  illustrates a business attribution placard. Business Calendar Collection  5708  illustrates a business calendar collection placard. Business Contact Collection  5710  illustrates a business contact collection placard. Business Coupon Collection  5712  illustrates a business coupon collection placard. Business Description  5714  illustrates a business description placard. Business Hours  5716  illustrates a business hours placard. Business Logo  5718  illustrates a business logo placard. Business Name  5720  illustrates a business name placard. Business Offering Collection  5722  illustrates a business offering collection placard. Business Id  5724  illustrates a business identification placard. Business Profile Picture Collection  5726  illustrates a business profile picture collection placard. Business Specials Collection  5728  illustrates a business specials collection placard. 
       FIG. 58  illustrates Business Listing Summary Placard  5800  which contains Business Address  5702 , Business Attribution  5706 , Business Contact Collection  5710 , Business Description  5714 , Business Name  5720 , and Business Id  5724 . 
     Business Listing Summary Placard  5800  illustrates a placard providing a summary for a business listing. Business Address  5702  illustrates a business address placard. Business Attribution  5706  illustrates a business attribution placard. Business Contact Collection  5710  illustrates a business contact collection placard. Business Description  5714  illustrates a business description placard. Business Name  5720  illustrates a business name placard. Business Id  5724  illustrates a business identification placard. 
       FIG. 59  illustrates Business Placards  5900  which contains Business About Us Placard  5902 , Business Appointment Placard  5904 , Business Blog Placard  5906 , Business Call to Action Placard  5908 , Business Careers Placard  5910 , Business Cart Placard  5912 , Business Checkout Placard  5914 , Business Confirmation Placard  5916 , Business Contact Us Placard  5918 , Business Delivery Placard  5920 , Business Event Placard  5922 , Business Feature Placard  5924 , Business Feedback Placard  5926 , Business Form Placard  5928 , Business Gallery Placard  5930 , and Business Inventory Placard  5932 . 
     Business Placards  5900  illustrates different business placards. Business About Us Placard  5902  illustrates a business about us placard. Business Appointment Placard  5904  illustrates a business appointment placard. Business Blog Placard  5906  illustrates a business blog placard. Business Call to Action Placard  5908  illustrates a business call to action placard. Business Careers Placard  5910  illustrates a business careers placard. Business Cart Placard  5912  illustrates a business cart placard. Business Checkout Placard  5914  illustrates a business checkout placard. Business Confirmation Placard  5916  illustrates a business confirmation placard. Business Contact Us Placard  5918  illustrates a business contact us placard. Business Delivery Placard  5920  illustrates a business delivery placard. Business Event Placard  5922  illustrates a business event placard. Business Feature Placard  5924  illustrates a business feature placard. Business Feedback Placard  5926  illustrates a business feedback placard. Business Form Placard  5928  illustrates a business form placard. Business Gallery Placard  5930  illustrates a business gallery placard. Business Inventory Placard  5932  illustrates a business inventory placard. 
       FIG. 60  illustrates Business Placards  6000  which contains Business Map Placard  6002 , Business Menu Placard  6004 , Business My Account Placard  6006 , Business Order Placard  6008 , Business Portfolio Placard  6010 , Business Purchase Placard  6012 , Business Q &amp; A Placard  6014 , Business Reservation Placard  6016 , Business Scheduling Placard  6018 , Business Shop Placard  6020 , Business Store Locator Placard  6022 , Business Team Placard  6024 , Business Terms and Conditions Placard  6026 , Business Testimonials Placard  6028 , Business Timeline Placard  6030 , and Business Tracking Placard  6032 . 
     Business Placards  6000  illustrates different business placards. Business Map Placard  6002  illustrates a business map placard. Business Menu Placard  6004  illustrates a business menu placard. Business My Account Placard  6006  illustrates a business my account placard. Business Order Placard  6008  illustrates a business order placard. Business Portfolio Placard  6010  illustrates a business portfolio placard. Business Purchase Placard  6012  illustrates a business purchase placard. Business Q &amp; A Placard  6014  illustrates a business Q &amp; A placard. Business Reservation Placard  6016  illustrates a business reservation placard. Business Scheduling Placard  6018  illustrates a business scheduling placard. Business Shop Placard  6020  illustrates a business shop placard. Business Store Locator Placard  6022  illustrates a business store locator placard. Business Team Placard  6024  illustrates a business team placard. Business Terms and Conditions Placard  6026  illustrates a business terms and conditions placard. Business Testimonials Placard  6028  illustrates a business testimonials placard. Business Timeline Placard  6030  illustrates a business timeline placard. Business Tracking Placard  6032  illustrates a business tracking placard. 
       FIG. 61  illustrates Business Lookups  6100  which is comprised of Cafe  6102 . Cafe  6102  is connected to Business Listing Placard  5700 , Business Event Placard  5922 , Business Menu Placard  6004 , and Business Listing Summary Placard  5800 . Network(s)  2608  is connected to Business Lookup App  6104 , Lookup Browser  5404 - 1 , Lookup Systems and Modules  5100 , Resources  2624 , and Lookup Domain Name Registration  5400 . Lookup Systems and Modules  5100  is connected to Context Expression Collections  2850 , Pathway Base Collections  3890 , and Lookup Collections  4850 . Cafe  6102  utilizes Business Lookup App  6104 . User  5402 - 1  utilizes Lookup Browser  5404 - 1 . Business Lookups  6100  also contains Help Wanted Placard  6120 , Share Placard  6122 , Share Authorization Placard  6124 , Share Editor Placard  6126 , Broadcast Share Placard  6128 , and Merge Share Placard  6130 . 
     A “placard” is defined as an internet component that may integrate context expressions, pathways, lookups, and lookup domain names in whole or in part; they encapsulate common personal, business, non-profit, and government use case functionality while enabling end users the ability to control content and style as well as add custom functionality. When the phrase “placard” is used, it refers to the placard definition specified here in addition to Placard  5060  (see,  FIG. 50 ), Placards  5608 - 1 , Placards  5608 - 2 , and Placards  5608 -N (see,  FIG. 56  respectively), Business Listing Placard  5700  (see,  FIG. 57 ), Business Listing Summary Placard  5800  (see,  FIG. 58 ), Business Placards  5900  (see,  FIG. 59 ), Business Placards  6000  (see,  FIG. 60 ), and their constituent parts and any derivatives thereof. Other examples of placards include, but are not limited to, status (e.g., flight status, table status), resume, classified (e.g., for sale, housing, help wanted, services), happening, mention, share, broadcast share, merge share, share authorization, and share editor. 
     An exemplary enablement is a system that utilizes placards for static content creation, lookup, and display. 
     An exemplary enablement is a system that utilizes placards for dynamic content creation, lookup, display, and processing. 
     Business Lookups  6100  illustrates Cafe  6102 , a cafe business which utilizes Business Lookup App  6104 . Business Lookup App  6104  connects to Lookup Systems and Modules  5100  through Network(s)  2608  to utilize Context Expressions Collections  2850 , Pathway Base Collections  3890 , and Lookup Collections  4850 . To illustrate, Cafe  6102  utilizes Business Lookup App  6104  to create Lookup Domain Name  5200  (see,  FIG. 52 ) for the cafe business “Willoughby&#39;s Coffee &amp; Tea”. User  5402 - 1  utilizes Lookup Browser  5404 - 1  to browse Lookup Domain Name  5200  for Cafe  6102  using the context expression: “:website:@Willoughby&#39;s Coffee &amp; Tea”. Cafe  6102  utilizes Business Lookup App  6104  to creates two more Lookup Domain Name  5200  for the cafe business using their local colloquialism and locality: 
     “:website:@Willoughby&#39;s:location::new_haven:” and
 
“:website:@Willoughby&#39;s:location::brandford:”. User  5402 - 1  utilizes Lookup Browser  5404 - 1  to browse Lookup Domain Name  5200  for Cafe  6102  using the context expression: “:website:@Willoughby&#39;s”. The lookup result for the user will depend on the locality that is set in their lookup browser.
 
     An exemplary enablement is a system that utilizes local colloquialisms for lookup domain names. 
     Another exemplary enablement is a system that utilizes locality for lookup domain names. 
     In another embodiment, Lookup Browser  5404 - 1  is able to set the browser&#39;s location automatically (e.g., HTML5 Geolocation API). 
     Cafe  6102  is connected to Business Listing Placard  5700 , Business Event Placard  5922 , Business Menu Placard  6004 , and Business Listing Summary Placard  5800 . Cafe  6102  utilizes Business Lookup App  6104  to create business placards resulting in one or more Lookup  4800  (see,  FIG. 48 ) being created for each placard. Resources  2624  illustrates business placard content for Lookup  4800  through Network(s)  2608 . As an example, Cafe  6102  utilizes Business Lookup App  6104  to create Business Menu Placard  6004  resulting in one or more Lookup  4800  being created for each menu item. To illustrate, the “Willoughby&#39;s House Blend” cafe menu item uses the context expression: “:restaurant::cafe::menu_item:Willoughby&#39;s House Blend:locality::new_haven:” User  5402 - 1  utilizes Lookup Browser  5404 - 1  to set their default location using the context expression: “:settings: :locality::new_haven:”, and places a takeout order of the menu item with the context expression: “:cafe::takeout: #Willoughby&#39;s House Blend”. 
     Help Wanted Placard  6120  illustrates a help wanted placard which may be used to post a help wanted lookup. In this example, Cafe  6102  utilizes Business Lookup App  6104  to create Help Wanted Placard  6120  resulting in one or more Lookup  4800  being created for the help wanted post. To illustrate, the “Roaster Level I” help wanted posting uses the context expression: “:classified::help_wanted: :title:Roaster Level I:locality::new_haven:” User  5402 - 1  utilizes Lookup Browser  5404 - 1  to browse the help wanted classified lookups using any of the following context expressions: “#Classifieds #Roaster Level I”, “#Classifieds #Help Wanted #Roaster Level I” or “#Help Wanted #Roaster Level I”. Lookup Browser  5404 - 1  then displays help wanted placard results. 
     An exemplary enablement is a system that utilizes placards for lookup generation. 
     Another exemplary enablement is a system that utilizes placards to automatically generate synonymous pathways from a single context expression. 
     Share Placard  6122  illustrates a share placard which may be used to create a share lookup. In this example, Cafe  6102  utilizes Business Lookup App  6104  to create Share Placard  6122  resulting in one or more Lookup  4800  being creating for the share post. To illustrate, the “Willoughby&#39;s East Rock Blend” share uses the context expression: “:share: :title:Willoughby&#39;s East Rock Blend:” User  5402 - 1  utilizes Lookup Browser  5404 - 1  to browse their received share lookups using any of the following context expressions: “#New Shares”, “#Shares”, “#Willoughby&#39;s East Rock Blend”, or “#East Rock Blend”. Lookup Browser  5404 - 1  then displays share placard lookup results. 
     An exemplary enablement is a system that utilizes share placards to send and receive share lookups. 
     Another exemplary enablement is a system that utilizes share placards to automatically generate synonymous share pathways from a single context expression. 
     Share Authorization Placard  6124  illustrates a share authorization placard which may be used to configure share lookup authorization. Cafe  6102  utilizes Business Lookup App  6104  to set share authorization utilizing Share Authorization Placard  6124 . To illustrate, setting authorization for the “Willoughby&#39;s East Rock Blend” share, Cafe  6102  uses the context expression: “:set::share::authorization::public: :title:Willoughby&#39;s East Rock Blend:” Share Authorization Placard  6124  uses this context expression to set the Willoughby&#39;s East Rock Blend share authorization to public thereby granting the general public access to the share. In yet another example, Cafe  6102  utilizes Business Lookup App  6104  to create a private share for an employee that contains personal identifiable information (PII) with the context expression: “:share: :title:Roaster Level II Promotion”. Share Authorization Placard  6124  is utilized to create a reauthorization for the share with the following context expression: “:set::share::authorization::reauthorization: :title:Roaster Level II Promotion:”. The reauthorization causes the system to reauthorize the user before granting the user access to the share. 
     An exemplary enablement is a system that utilizes share authorization placards to configure share authorization. 
     Another exemplary enablement is a system that utilizes reauthorization to require the system to reauthorize a user before allowing access. 
     Share Editor Placard  6126  illustrates a share editor placard which may be used to perform editorial functions for share lookups. Cafe  6102  utilizes Business Lookup App  6104  to enable editorial functions for shares utilizing Share Editor Placard  6126 . Share Editor Placard  6126  may be used to set share attributes (e.g., read only, comment, comment with editorial review, download, reshare, encrypt). The share attribute comment with editorial review enables Cafe  6102  to review comments for approval, deletion, markup, correction, etc. Broadcast Share Placard  6128  illustrates a broadcast share placard which may be used to broadcast share lookups. Cafe  6102  utilizes Business Lookup App  6104  to create broadcast shares utilizing Broadcast Share Placard  6128 . Merge Share Placard  6130  illustrates a merge share placard which may be used to merge share lookups. Cafe  6102  utilizes Business Lookup App  6104  to create merge shares utilizing Merge Share Placard  6130 . A merge share enables all authorized users to contribute content individually and share the total combined content collectively. 
     In an alternative embodiment, context expressions may be generated by a user interface context expression designer. 
       FIG. 62  illustrates Account, Identity and Lookup Systems  6200  which is comprised of Network(s)  2608 . Network(s)  2608  is connected to Account Systems  6202 , Identity Systems  6204 , Lookup Systems  2616 , and Lookup Systems and Modules  5100 . 
     Account, Identity and Lookup Systems  6200  illustrates a systems architecture for Account Systems  6202 , Identity Systems  6204 , and Lookup Systems  2616 . Lookup Systems and Modules  5100  is available to all systems through Network(s)  2608 . Account Systems  6202  illustrates systems for accounting. Identity Systems  6204  illustrates a system for creating and maintaining user credentials and claims. Lookup System  2616  illustrates, in this use case, user listing information lookups. 
     Account Systems  6202  is connected to Identity Systems  6204  via Line Segment  6208  and to Lookup Systems  2616  via Line Segment  4006 ; these connections illustrate that Account Systems  6202  utilizes a Context Identifier  228  (see,  FIG. 2 ) for each of the users in Identity Systems  6204  and Lookup Systems  2616  respectively. The use of Context Identifier Algorithm  200  (see,  FIG. 2 ) to create a Context Identifier  228  for users of each system enables better security than using a hash (e.g., SHA1) of a user&#39;s name or system identifier. Each system defines a set of KVP that is unknown outside the system, to create context items (see, Context Item Dictionary  2720  in  FIG. 27 ) as input for Context identifier Algorithm  200  to derive a Context Identifier  228  for a user which can then be shared with another system. 
     An exemplary enablement is a system that utilizes context identifiers to generate user identifier references for external systems. 
     While Account Systems  6202  has a reference to the users in Identity Systems  2604  and Lookup System  2616 , the reference does not contain usable information that can be used for hacking. This capability is very important for Identity Systems  6204  which is responsible user credentials and claims. As illustrated, Identity Systems  6204  is connected to Account Systems  6202  via Line Segment  6210  and to Lookup Systems  2616  via Line Segment  6212 . However, Identity Systems  6204  does not retain a hard or slightly obscured user identity from the other systems; it retains a context identifier only, making it very difficult to leverage user information from other systems. 
     Lookup Systems  2616  may contain lookups to user listing information such as contact details (e.g., street address, phone numbers, email address, etc.). However, Lookup Systems  2616  does not hold references to Identity Systems  6204  and Account Systems  6202 . The separation of user identity from user contact details reduces attack surface areas for all systems involved. This separation or decoupling is further illustrated by one-way connections utilizing context identifiers between Identity Systems  6204  and Account Systems  6202  to Lookup Systems  2616 . 
     An exemplary enablement is a system architecture that enables separation of concerns for user credentialing, accounting, and directory information.