Abstract:
A search engine optimizer that works independently and in parallel with a browser and search engine supercomputer to gather, analyze, and distill input information interactively. The optimizer reorganizes the input, and provides an optimized version as an output. The optimized version of the input (e.g. output) is sent to the search engine, which responds to the end user with search results. The optimizer recognizes each request as a pattern and stores the pattern in an advanced Glyph format. This permits the optimizer to identify a left and right side human brain checkmate combination required to achieve certitude.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This Continuation-In-Part Utility Application claims the benefit of co-pending U.S. Continuation-In-Part patent application Ser. No. 13/247, 964 filed on Sep. 28, 2011, which claims the benefit of U.S. Continuation-In-Part patent application Ser. No. 12/785,122, filed on May 21, 2010 (issuing as U.S. Pat. No. 8,386,456 on Feb. 26, 2013), which claims the benefit of the following applications:
         a. U.S. Continuation-In-Part patent application Ser. No. 12/146,420, filed on Jun. 25, 2008 (issued as U.S. Pat. No. 7,908,263 on Mar. 15, 2011);   b. U.S. Continuation-In-Part patent application Ser. No. 12/778,228, filed on May 12, 2010 (issued as U.S. Pat. No. 8,239,229 on Aug. 7, 2012), which claims priority to co-pending U.S. Continuation-In-Part patent application Ser. No. 12/764,934, filed on Apr. 21, 2010, which claims the benefit of U.S. Continuation-In-Part patent application Ser. No. 11/584,941, filed on Oct. 23, 2006, (issued as U.S. Pat. No. 7,809,659 on Oct. 5, 2010), which claims the benefit of U.S. Continuation-In-Part Non-Provisional patent application Ser. No. 10/852,394, filed May 24, 2004, (now abandoned), which claims priority to U.S. Provisional Application Ser. No. 60/184,537 filed on Feb. 24, 2000;   c. U.S. Continuation-In-Part patent application Ser. No. 12/778,228, filed on May 12, 2010 (issued as U.S. Pat. No. 8,239,229 on Aug. 7, 2012), which claims benefit of U.S. Continuation-In-Part patent application Ser. No. 10/926,267, filed on Aug. 25, 2004 (now abandoned), which claims the benefit of U.S. patent application Ser. No. 10/603,963, filed on Jun. 25, 2003 (issued as U.S. Pat. No. 6,842,511 on Jan. 11, 2005), which claims the benefit of U.S. Non-Provisional patent application Ser. No. 09/544,238, filed on Apr. 7, 2000 (issued as U.S. Pat. No. 6,614,893 on Sep. 2, 2003), which claims the benefit of U.S. Provisional Application Ser. No. 60/184,537 filed on Feb. 24, 2000;   d. U.S. Continuation-In-Part patent application Ser. No. 12/778,228, filed on May 12, 2010 (issued as U.S. Pat. No. 8,239,229 on Aug. 7, 2012), which claims the benefit of U.S. Non-Provisional patent application Ser. No. 10/926,446, filed on Aug. 25, 2004 (issued as U.S. Pat. No. 7,050,813 on May 23, 2006);   e. U.S. Continuation-In-Part patent application Ser. No. 12/778,228, filed on May 12, 2010 (issued as U.S. Pat. No. 8,239,229 on Aug. 7, 2012), which claims the benefit of U.S. Non-Provisional patent application Ser. No. 09/514,940, filed on Feb. 28, 2005 (issued as U.S. Pat. No. 7,058,601 on Jun. 6, 2006),   f. Continuation-In-Part Non-Provisional patent application Ser. No. 11/223,226 (now abandoned), filed on Sep. 9, 2005, which claims priority to U.S. patent Non-Provisional application Ser. No. 10/135,493 (abandoned), filed on Apr. 30, 2002, which claims priority to U.S. Provisional Application Ser. No. 60/289,033 filed on May 4, 2001;   g. U.S. Continuation-In-Part patent application Ser. No. 11/584,271 filed on Oct. 20, 2006 (now abandoned), which claims priority to U.S. Continuation-In-Part Non-Provisional patent application Ser. No. 10/852,394, (now abandoned) which claims the benefit of Continuation-In-Part patent application Ser. No. 10/603,963, filed on Jun. 25, 2003, (issued as U.S. Pat. No. 6,842,511 on Jan. 11, 2005), which claims the benefit of U.S. Non-Provisional patent application Ser. No. 09/544,238 (issued as U.S. Pat. No. 6,614,893 on Sep. 2, 2003), filed on Apr. 7, 2000, which claims priority to U.S. Provisional Application Ser. No. 60/184,537 filed on Feb. 24, 2000; and   h. U.S. Continuation-In-Part Non-Provisional patent application Ser. No. 11/085,678 filed on Mar. 21, 2005 (now abandoned), which claims the benefit of U.S. Non-Provisional patent application Ser. No. 09/819,174, filed on Mar. 27, 2001, (now abandoned), which claims priority to U.S. Provisional Application Ser. No. 60/193,160 filed on Mar. 28, 2000;   i. all of which are incorporated herein in their entireties.       

    
    
     FIELD OF THE INVENTION 
     The present invention relates generally to an Internet Search Engine Optimizer method and system, hereinafter referred as Optimizer. More particularly, the present invention relates to an interface product that works independently and in parallel with a browser client and search engine supercomputer server architecture that gathers, analyzes, and distills interactive input. The Optimizer analyses input and transforms the input into a search pattern. For each search pattern the Optimizer continuously maintains and updates pre calculated and preprocessed array or collection of best-fit web page responses. 
     BACKGROUND OF THE INVENTION 
     The limitations, drawbacks and/or disadvantages of technologies are as follows: Search Engines are based on Boolean algebra eigenvector algorithms that are used to parse and filter information indices until the top page ranks are determined and displayed to the end user. Unfortunately, some specific keywords combinations may be too narrow and confound a search by hiding optimal results. Search Engines are predominately configured to perform static search patterns. Each search is processed from the ground up without taking into account many requests belonging to one reply. A session consists of consecutive related and unrelated search requests to reach the final destination. 
     The Internet environment or (U) can be construed as a complex and massive volume network with billions. The Search engine supercomputer analyzes the billions of unique web pages, and then uses eigenvectors to determine the highest ranked pages from the end user&#39;s match criteria. As explained, in related subject matters “As the size of the environment increases the level of redundancy and tax burden of a system exponentially increases”. 
     Transform Data: The supercomputer system cleans, standardizes and organizes the spaghetti of the environment by organizing the huge amount of information in a managerial hierarchical structured supercomputer (hereinafter referred as HIVE) that removes redundancy, latency and the tax burden. 
     Synchronize tasks: the HIVE is a decentralized supercomputer consisting of a plurality of nodes, which are specifically arranged in three tiers. The summit tier coordinates and executes global tasks. The middle tier coordinates and executes regional tasks. The lower tier coordinates and executes localized tasks and processes the lion share of noncritical transactions. The summit node of each tier synchronizes tasks by sending command messages that assigns the fuzzy logic state of each node belonging to its chain of command. 
     Lateral and Vertical Synergy: A tier consisting of groups of nodes that are independent from other groups of nodes. Each tier partition performs mission critical tasks within their domain and works in parallel with other partitions of the same tier. Each node can shunt available resources using lateral and vertical synergy with parent, sibling or subordinate nodes to maximize available resources. Each node continuously analyzes its own environment current conditions and forward chains summary information until reaching the summit. At this point, the summit nodes rearward chain messages with command instructions that priorities resources, and notify each subordinate with command instructions tasks constraints taking into account present network conditions to avoid saturation, clog and eliminate the tax burden of the environment’. 
     Remove chaos and anarchy: Once the ‘spaghetti of the environment’ is eliminated the HIVE creates command messages that are also known as environment bitmap data. Command messages coordinate and synchronize each node to operate at maximal output capacity. Each node operates without adversely affecting the network flow of data. The HIVE maximizes available throughput and limits the exponential rate of growth of complexity as the size of the environment increases. 
     Convert Requests into Ideas: Search Engines dependency on Boolean algebra use inductive reasoning popularity scores to find the top results. In contrast, the HIVE using deductive reasoning to interpret interactive input as being part of an idea being formulated by both the left and the right sides of the brain. When a request is incomplete the HIVE probabilistically supplies and inserts missing gaps of information. Related art teaches that a Vector CDR can be expressed as the summation of a plurality of valid vectors. The HIVE correlates a plurality of partial vectors and creates a resultant vector containing the top (n) pages. 
     Heartbeat: the Summit tier gives a heartbeat to the HIVE. 
     The X_FOB and Y_CDIF Inventory Control Bitmaps are commonly known and referred as managerial relationship indices summary results, with pre-calculated look up information to eliminate redundant and resource intensive calculations. 
     Brief Summary of Applicant&#39;s Related Applications 
     Search Engines use Boolean algebra and eigenvector algorithms to parse and filter information indices until the top page ranks are determined and displayed as output. Unfortunately, some specific keywords combinations may be too narrow and confound a search by hiding optimal results. Search Engines are predominately configured to perform single static search patterns. Each search is processed from the ground up without taking into account many requests belonging to one session. A session consists of consecutive related and unrelated search requests while randomly surfing the web. 
     The Optimizer in parallel keeps in existence for each search pattern its corresponding virtual simulation environment that contains all relevant bound pages. Each virtual simulated environment possesses a relative Master Index. The Optimizer continuously purifies and synchronizes the plurality of relative Master Index that permits to match/merge and then correlate the Internet&#39;s Master Index in real time. 
     The Optimizer continuously scans and detects the environment in real time for new content with significant difference quality to update each search pattern&#39;s virtual environment partition relative Master Index and associated collections of top (n) pages. The Optimizer heuristically reads the content of each page, paragraph, sentence, and term clusters. Existing Master Index has an absolute rank value for each page. 
     The Optimizer rank value is dynamically adjusted by matching independent variables and related keywords belonging to the search pattern to generate a content value. The Optimizer “cherry picks” the best content value web pages as output. The output is forward chained back to the end user&#39;s terminal and displayed. 
     The Optimizer is a method and system for simulating Internet browser search capacities that cleans, standardizes, organizes, and transforms the massive amount of data into a lingua franca comprising of valid keywords, term clusters, and unique geospatial patterns contained in the Internet collectively known as patterns that exist in page. The comprehensive collection of search patterns with their relative Master Index are stored and continuously updated as web crawlers detect significant changes in the environment. 
     Each Search Pattern consists of at least one independent variable, e.g. (I), (J), (K), (X), (Y) and (Z). Search Patterns with 0 independent variables use randomly surfing the web techniques that find the final destination within the massive (U) or Internet environment. 
     Related Applications (U.S. patent application Ser. No. 10/926,446) 
     Partial Differential Equation Vectors Model: Solves solutions with two or more independent variable. The solution requires an equation with a plurality of independent variables. Thus we replace the traditional vector with Partial Differential Equation Vectors. 
     E.g. Using Set Theory, the telecommunications environment U can be divided into three independent networks: Fixed (X), IP Telephony (Y) and Wireless (Z). A Simple Call exists when the call uses a single network (X, Y or Z), whereas a Complex Call exists when the call must use more than one independent network environment to complete the call. 
     E.g. A call uses three different networks Fixed, IP Telephony and Wireless (I, J, K), each independent variable solves the billing entity and resultant vector for the call. The Switch controlling the call uses its Partial A and Partial B functions to create a final resultant vector that includes all the circuits belonging (I, J, K) for just one call. Yes, they are three independent calls one per network which is billable, yet in fact there is only one call. 
     Related Applications: (U.S. patent application Ser. No. 10/852,394) 
     Computer Network System: consists of a plurality of nodes, where each one is programmed with Artificial Intelligence to perform predefined tasks that are logistical rationalized based on the current conditions of the environment. The computer network system is synonymous with Superset (U). The cluster is divided into three geospatial tiers: a) Global, b) Regional, and c) Local. Each tier has multiple functionalities such as a) provisioning, b) Total Quality Management or (TQM), c) Data Manipulation, d) Management Information Systems (or MIS), e) Expert Information Systems (or EIS) and f) Inventory Control. 
     Computer Network System Nodes: All nodes are autonomous and in real time analyze, evaluate, gather and process information from the environment. From incipiency upon receiving the fuzzy logic piece of information that triggers a new task or update pending activities. Each node is assigned to Superset(I), Set(I, J), or Subset(I, J, K) cluster tier, and to the geospatial domains (X) or global, (Y) or regional, and (Z) local to map independent variables (I, J, K, X, Y, Z) that build the managerial hierarchy as follows: 
     Managerial Hierarchy: The Summit Tier allows users to have access to their information in real time. The Middleware Tier geographical manages physical warehouses. The Lower Tier controls a plurality of points of presence belonging to 3 rd  parties and collectively constitutes the workhorse of the system. 
     Node Synchronization and Buffer Resources: Every predefined cycle each node synchronizes the latest inventory. Nodes request siblings for any excess buffer resources to complete a task using vertical and lateral synergy. Parent nodes use their chain of command to coordinate their subordinates. Thus, all nodes synergistically collaborate to process tasks and collectively mimic a global online supplier. 
     Eliminates the Spaghetti Phenomena: The global online supplier gathers, distills, analyzes and then standardizes raw information into primed lingua franca data so that Information Certainty is achieved and thus removes the chaos and anarchy or Spaghetti Phenomena. 
     Primes Vector CDR: Lingua franca messages are Vectors and contain the vector trajectory and all transactional segments information. Legacy systems send all transactional segments to centralized billing data warehouses that match/merge each transactional component and then correlate the information into a billing entity. Whereas the computer network uses artificial intelligence to assign a hierarchical owner and plots circuit by circuit the vector trajectory and only activates relevant nodes to the transaction so that nodes can communicate amongst themselves via forward and reward chaining. Nodes send all dynamic and fixed costs to hierarchical owner so it can correlate the rated billing entity absent of a centralized billing data warehouse. 
     Avoids Taxing the Throughput: The computer network system monitors the limited resources and capacities of the network to avoid taxing available throughput in real time. Each node can create, plot and update resources as soon as new relevant messages from the environment are detected. 
     Uses Synergy to Maximize Throughput: Upon receiving environment command instructions each node can manage the flow of information of their subordinates from predefined point A to point B routes to avoid saturation. The computer network maximizes throughput by permitting each node via synergy to shares resources with other nodes that have substantial buffer resources to eliminate the tax burden and waste. 
     Analyzes Network Traffic: Network traffic is analyzed as the informational traffic is measured based on the latest command instructions and known routing throughput limitations of each given domain. The summit nodes of each tier perform the nonobvious task synchronizing and managing their subordinates to use synergy to minimizing waste before permitting data to be transmitted through their chain of command. 
     Computer Network System Reaches Informational Certainty: Nodes remove waste at incipiency one transaction at a time, so that the computer network system can be considered a real time invention. 
     Computer Network System Stabilizes the Flow of Information: Summit and Middleware nodes stabilize the flow of information and update the XLDB database with trending statistics used to optimize resources and available bandwidth. Each node of the managerial hierarchical synergy works in parallel with others nodes to work as a single unit permitting the computer network to create a virtual instance of the organizational environment. 
     Computer Network System is a Real Time System: Once the ‘Spaghetti Phenomena’ is eliminated, informational certainty is achieved removing the need for a central mainframe. Consequently, a real time solution consists of synergistically synchronizing all the computer network system functions. 
     Computer Network System Evaluates Network Resources: Each node has its own location identification means and must be assigned to one geospatial specific domain cluster such as local, regional or global. Each activity and task is processed in parallel, starting from the point of origin and ending at the point of destination. The computer network system rearward chains the routing vector information through the simulation network to the point of origin and analyzes and evaluates the best usage of network resources. 
     Related Applications (U.S. patent application Ser. No. 11/584,941/Issued U.S. Pat. No. 7,809,659) 
     XCommerce, Deductive Reasoning Supercomputer: is a method that simulates the entire superset of potential valid interactive input regular expression requests construed during an Internet browser search and converting the results set into a vector based statistical data that enable efficient and accurate searching. XCommerce simulates, standardizes and partitions the Internet into a plurality of concurrently working environment using a Managerial hierarchical method of indexing and searching as follows: 
     Managerial Hierarchical Index Relationships: a request is broken down into keywords and clusters, and then converts them into a search pattern that optimally minimizes the quantity of relevant pages. 
     Determining what is Relevant and Irrelevant: Pages that match a Relationship Index are relevant, and those that do not are irrelevant. Irrelevant web pages are discarded completely from analysis. 
     Partition the Environment into Blocks: the environment is subdivided into a plurality of blocks that are arranged based on Managerial Hierarchical levels as follows: 
     Each Search Pattern restricts the geometric rate of growth of the Internet environment by creating the relevant environment that is used by all managerial relationship levels when purifying the search process. 
     The Internet environment is considered a Super Block and is partitioned into a three level Managerial Hierarchy. First: the primary index relationship creates Blocks that maps an improved environment. Second: the secondary index relationship creates Sub Blocks that maps an optimal environment. Third: the tertiary index relationship creates Mini Blocks that maps an optimal solution. 
     Identifies Static Search Patterns: the computer network system determines if the search pattern already exist and if yes obtains the top (n) pages from the databases and sends the output to the end user. 
     Calculates Dynamic Search Patterns: uses managerial hierarchical relationship indices to create optimal size partitions and compares remaining key featured associations to determine if they match against the content of the top (n) pages. When a match occurs each page is gain factored by each key featured association vector value and then the Optimizer picks the top (n) pages with the highest values. 
     Finds New Search Patterns: stores each new search patterns and top (n) pages. 
     Displays Top (n) pages: Sends and displays the output to the end user&#39;s terminal. 
     Related Applications (U.S. patent application Ser. No. 12/146,420/Issued U.S. Pat. No. 7,908,263) 
     A search engine optimizer, hereinafter referred as Cholti, gathers interactive input from a browser. The optimizer reorganizes the interactive input as optimal input that is sent to the search engine, and then the output is sent to the end user. Each request is converted into a search pattern and stored as a mathematical equation that mimics the left (linguistics) and right (geospatial) side of the brain. 
     Related Applications (U.S. patent application Ser. No. 12/764,934) 
     Lottery Mathematics: Cholti and XCommerce teaches how to improve accuracy of a requests by using independent variables (I, J or K) to map and create managerial hierarchical partitions of the Internet environment such as: from top to bottom Superset(I), Set (I, J) and Subset (I, J, K) datasets. For this application Lottery Mathematics is hereinafter referred to as Logic Mathematics. 
     Hot and Cold analysis: uses logic mathematics to estimate the size of the environment as the end user types interactive input and assigns primary independent variable (I) to the filter with the following formula: (x!−(x−6)!)/6! E.g. the number of permutations for a 10 number draw is (10!−4!)/6! 4!=24, 6!=720 and 10!=3,628,800. (3,628,800/24)/720=210 permutations. Thus, each grid has 1/210 in being the outcome. The English language estimated Master Index size of the environment in the year 2013 is Logic — 305 Basis or 1,099,511,627,776 or (2^40) pages hereinafter for simplicity 1 trillion. 
     E.g. the number of permutations for a 305 number draw is 1 trillion or 305!−(305−6!/6! The quality of the Glyph that represents (I) or primary index relationship determines the Mass. E.g. If the keyword Civil=(I) the Mass=1, and if cluster “American Civil War”=(I) the Mass=2. 
     
       
         
               
             
               
               
             
           
               
                 TABLE 1 
               
               
                   
               
               
                 Size of environment based on Mass 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 a.  
                 Mass = 0 (Logic_305_Basis = 1 trillion) or 305! − (305 − 6)!/6! 
               
               
                 b. 
                 Mass = 1 (Logic_100_Basis = 1,192,052,400) or 100! − (100 − 6)!/6! 
               
               
                 c.  
                 Mass = 2 (Logic_70_Basis = 131,115,985) or 70! − (70 − 6)!/6! 
               
               
                 d. 
                 Mass = 3 (Logic_50_Basis = 15,890,700) or 50! − (50 − 6)!/6! 
               
               
                 e.  
                 Mass = 4 (Logic_40_Basis = 3,838,380) or 40! − (40 − 6)!/6! 
               
               
                 f.  
                 Mass = 5 (Logic_30_Basis = 593,775) or 30! − (30 − 6)!/6! 
               
               
                 g. 
                 Mass = 6 (Logic_20_Basis = 38,760) or 20! − (20 − 6)!/6! 
               
               
                 h.  
                 Mass = 7 (Logic_15_Basis = 5,005) or 10! − (10 − 6)!/6! 
               
               
                 i.  
                 Mass = 8 (Logic_6_Basis = 1) or final destination. 
               
               
                   
               
             
          
         
       
     
     I. Simulating the Human Brain: 
     Human Brain: Each linguistic Glyph is assigned to the [L] left side of the brain and each geospatial Glyph is assigned to the [R] right side of the brain and the Anchor is the best common denominator Glyph. 
     The Dominant Tendency of each request is given a [L] linguistic, and [R] geospatial tendency, and then Cholti reorganizes, maps and plots the Glyphs to create a Managerial Hierarchical Relationship Index. 
     Human Brain Intelligence: transforms each Search Pattern and identifies independent variables based on mass partitions of the Internet and creates Join, Simple, Hybrid, Complex and Optimal Pyramids. 
     Human Brain Wisdom: analyzes the top (n) pages and expands each [AX], [BX] and [CX] Glyph equation with key featured association dependent variables. 
     Cholti picks one of four Search Strategies: [LL], [LR], [RL], and [RR], which have different set of business rules to analyze the Internet and limits partitions not to exceed 1 billion or (2^30) pages and thus eliminates the principal confounding variable, which is the exponential rate of growth of the environment. 
     E.g. the environment grows geometrically to 20 billion, or 40 billion or 100 billion or 1 trillion pages, but once the Dominant Tendency is selected the relevant environment will always be a Logic — 100_Basis or 1,192,052,400 pages, while making most pages irrelevant. 
     II. [L+R] Managerial Relationship Events 
     If the independent variable (I) is represented by the Historical Event “American Civil War” {1863}, where “American Civil War” is the left side of the brain variable (I) and 1863 is the right side of the brain (X), and are merged to a Single Event or Superset(I!) with Mass=3. The Double Event or Set(I,J) !! with Mass=5 and (I, J, X, Y) independent variables, and finally for Triple Event or Subset (I, J, K)!!! with Mass=8 consisting of [L] left side of the brain (I, J, K) and [R] right side of the brain (X, Y, Z) independent variables. 
     First Significant Event or (FSE): is a vague search that maps an improved environment. The Internet environment (a, b, c, d, e, f) becomes the improved environment (FSE, b, c, d, e, f) for Superset(I) dataset. Hereinafter Lucky numbers are replaced with IV that is the abbreviation for Independent Variables. 
     
       
         
               
             
               
               
             
           
               
                 TABLE 2 
               
               
                   
               
               
                 FSE Size of environment based on Mass 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 a.  
                 Mass = 1 (Logic_100_IV_1 or 75,287,520) or 100! − (100 − 5)!/5! 
               
               
                 b. 
                 Mass = 2 (Logic_70_IV_1 or 12,103,014) or 70! − (70 − 5)!/5! 
               
               
                 c.  
                 Mass = 3 (Logic_50_IV_1 or 2,118,760) or 50! − (50 − 5)!/5! 
               
               
                   
               
             
          
         
       
     
     Second Significant Event or (SSE) is a concise search that maps an optimal environment. The Internet environment (a, b, c, d, e, f) becomes the optimal environment (FSE, SSE, c, d, e, f) for Set(I, J) dataset. 
     
       
         
               
             
               
               
             
           
               
                 TABLE 3 
               
               
                   
               
               
                 SSE Size of environment based on Mass 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 a. 
                 Mass = 1 (Logic_100_IV_2 or 3,921,225) or 100! − (100 − 4)!/4! 
               
               
                 b. 
                 Mass = 2 (Logic_70_IV_2 or 916,895) or 70! − (70 − 4)!/4! 
               
               
                 c. 
                 Mass = 3 (Logic_50_IV_2 or 230,300) or 50! − (50 − 4)!/4! 
               
               
                 d.  
                 Mass = 4 (Logic_40_IV_2 or 91,390) or 40! − (40 − 4)!/4! 
               
               
                 e.  
                 Mass = 5 (Logic_30_IV_2 or 27,405) or 30! − (30 − 4)!/4! 
               
               
                   
               
             
          
         
       
     
     Third Significant Event or (TSE) is a precise search that maps an optimal solution. The Internet environment (a, b, c, d, e, f) becomes the optimal solution (FSE, SSE, TSE, d, e, f) for Subset(I, J, K) dataset. 
     
       
         
               
             
               
               
             
           
               
                 TABLE 4 
               
               
                   
               
               
                 TSE Size of environment based on Mass 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 a. 
                 Mass = 1 (Logic_100_IV_3 or 161,700) or 100! − (100 − 3)!/3! 
               
               
                 b. 
                 Mass = 2 (Logic_70_IV_3 or 54,740) or 70! − (70 − 3)!/3! 
               
               
                 c.  
                 Mass = 3 (Logic_50_IV_3 or 19,600) or 50! − (50 − 3)!/3! 
               
               
                 d.  
                 Mass = 4 (Logic_40_IV_3 or 9,880) or 40! − (40 − 3)!/3! 
               
               
                 e.  
                 Mass = 5 (Logic_30_IV_3 or 4,060) or 30! − (30 − 3)!/3! 
               
               
                 f. 
                 Mass = 6 (Logic_20_IV_3 or 1,140) or 20! − (20 − 3)!/3! 
               
               
                 g. 
                 Mass = 7 (Logic_15_IV_3 or 445) or 10! − (10 − 3)!/3! 
               
               
                   
               
             
          
         
       
     
     Fourth Significant Event or (QSE) is an optimal search that maps the optimal answer. The Internet environment (a, b, c, d, e, f) becomes optimal answer (FSE, SSE, TSE, QSE, e, f) as follows: 
     
       
         
               
             
               
               
               
             
           
               
                 TABLE 5 
               
               
                   
               
               
                 QSE Size of environment based on Mass 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 a. 
                 Mass = 1 (Logic_100_IV_4 or 4,950) or 100! − (100 − 2)!/2! 
               
               
                   
                 b.  
                 Mass = 2 (Logic_70_IV_4 or 2,415) or 70! − (70 − 2)!/2! 
               
               
                   
                 c. 
                 Mass = 3 (Logic_50_IV_4 or 1,225) or 50! − (50 − 2)!/2! 
               
               
                   
                 d.  
                 Mass = 4 (Logic_40_IV_4 or 780) or 40! − (40 − 2)!/2! 
               
               
                   
                 e. 
                 Mass = 5 (Logic_30_IV_4 or 435) or 30! − (30 − 2)!/2! 
               
               
                   
                 f.  
                 Mass = 6 (Logic_20_IV_4 or 190) or 20! − (20 − 2)!/2! 
               
               
                   
                 g. 
                 Mass = 7 (Logic_15_IV_4 or 45) or 10! − (10 − 2)!/2! 
               
               
                   
                   
               
             
          
         
       
     
     Gamma Functions 
     Cholti and XCommerce teach how to create search patterns that improve the accuracy of a request. The Likely and Unlikely analysis uses Gamma functions to solve for the size of the environment. 
     E.g. the end user types 1863 American Civil War, which the Optimizer automatically maps the [L] left side of the brain term cluster “American Civil War” with [R] right side of the brain geospatial keyword to create “American Civil War”. The “War between the States” is also synonymous with the American Civil War, and thus “between the” which are dependent variables since they have a Mass less than 1. The Dominant Tendency and the keyword “States” which has a Mass of 1+ is Likely. Let us assume, the keywords {1861, 1862, 1864 and 1865) are Unlikely. The Likely and Unlikely Gamma function values are as follows: “American Civil War” {1863}=50!−(50−5)!/5! or 2,118,760 pages. Plus “States” Likely Analysis:=49.9!−(49.9−5)!/5! or 2,096,762 pages. Plus Unlikely Analysis:=49.86!−(49.86−5)!/5! or 2,088,014 pages. 
     Search Pattern Variables 
     Independent Variables: The IV Numbers are the control variables or independent variables that determine the Circle of Accuracy, which in turn limit the size of the environment. 
     Dependent Variables: The Dependent Variables (hereinafter DV) Numbers are the observable variables or dependent variables, and are considered strong filters. 
     Complement Variables: The Complement Variables (hereinafter CV) Numbers are the measured variables or dependent variables, and are considered weak filters. 
     
       
         
               
             
               
               
               
               
             
           
               
                 TABLE 6 
               
               
                   
               
               
                 Gamma function adjustment of the Logic Basis 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 a. 
                 Independent/Control Variables  
                 (IV Numbers) 
                 +1.00 
               
               
                 b. 
                 Dependent/Observable Variables  
                 (DV Numbers) 
                 +0.100 
               
               
                 c.  
                 Dependent/Complement Variables  
                 (CV Numbers) 
                 +0.010 
               
               
                   
               
             
          
         
       
     
     Partial Differential Equations: When using Partial Differential Equations usually the solution is not unique due to the fluid and dynamic conditions of the search process, and ergo input combination usage behavior directly affects the size of the environment (or boundary of the region) where the solution is defined. 
     Related Applications (U.S. patent application Ser. No. 13/247, 964) 
     Simulation Comparison 
     ‘Boolean Algebra: End user types the input “Napoleon” or (i) and the inductive reasoning search engine assigns a “1” when a match occurs, and sums up the number of unique occurrences which is equal to 8,000,000 pages. Like always there is good, bad and ugly content. Based, on the business model of the search engine companies the lion share of their profit comes from advertisement, we will assign as (j) the commercialization process, which shrinks the environment to 10,000 pages, and the further distill by using the page quality value to create an environment of 100 pages. At this point selecting the top (n) result is really easy, by just automatically selecting the pages with the highest page rank and then send the output to the end user&#39;s browser. In this case the top site will always be wikipedia.com since Ask.com and Powerset technologies made a great emphasis of the content quality value of this site and then search engine industry followed. Encyclopedia Britannica is (2) and Encarta is (3) have a  10  in quality value and have a very high page rank 
     Cholti: determines that “Napoleon” is the anchor and commercial keyword and using the human brain strategy creates an [LL] environment of 8,000,000 pages that possess Super Site (a, b, c) characteristics, which is used to create the Simple Pyramid and [AX] Macro Page that converts “Napoleon” into “Napoleon Bonaparte” and adds “France”, “General” and “Emperor” to the Advanced Glyph equation with magnitude of 7. At this point Cholti uses Super Site (d) actual content characteristics, to create the Hybrid Pyramid and [BX] Macro Page that adds “Austerlitz”, “Waterloo”, “Blucher” and “Wellington”, and “1801 to 1815” to the Improved Glyph equation with magnitude of 10. Cholti now uses Super Site (e) trending characteristics, to create the Complex and [CX] Macro Page that adds a collection of key featured associations to the Optimal Glyph equation with magnitude of 15. Now Cholti performs the ‘Cherry Picking’ process to select the top (n) pages by reading, deciphering, analyzing the actual content. 
     The Real Difference: Wikipedia.com will always be the optimal web page for the static and vague search, whereas Cholti has three paths (a) (Static Ranking) for concise searches Wikipedia.com is automatically the optimal response, (b) (Actual Content) for precise searches if the end user typed additional keywords, and Cholti determines that Encyclopedia Britannica is the best fit content, then Wikipedia.com is demoted from the automatic 1 st  position and sent to the 2 nd  position. (c) (TQM levels of satisfaction) for optimal searches where wikipedia.com had the top spot, but did not satisfy, and after the second request Encyclopedia Britannica had the top spot, and also did not satisfy, for the third request the top responses for the request (1-2) are demoted, and now Encarta Encyclopedia the other high quality content has the top spot. Cholti is dynamic and personalized whereas existing search engines are static. TQM is the heart and soul of the technology and thus customer satisfaction. Yes, accuracy is directly related to the time from beginning to end, and the amount of knowledge and work required from the end user to reach the final destination, Cholti previews the entire content in behalf of the human to minimize TIME and using deductive reasoning to reduce the amount of gray matter required to reach the final destination to maximize satisfaction. 
     We&#39;ve overcome these “issues” or greatly improved the search optimally by doing the following: Generally stated end user&#39;s requests are converted into the Mayan style Glyphs that have left side and right side of the brain characteristics. The system understands that each request on its own can be optimally satisfying, and also knows that some require trial and error method. To solve this dilemma the optimizer creates Super Glyphs that have weighted value for a plurality of instances within a session. 
     Cholti Method 
     Minimally the system needs to be a computer with a database means that store a ‘CORE List’ that consists of statistics for each keyword or cluster performing the following steps:
         a. Identify each keyword interactively.   b. Validate each keyword to belong to a group.   c. Verify if a keyword will be an active participant in the process of reducing the size of the environment.   d. Estimate the [AX] or ‘Before’ vague search environment size from the input typed by the end user.   e. Determine if the end user&#39;s request is significant.   f. Create Basic Glyphs that best reflects the essence of the [AX] or ‘Before’ request that will permit the creation of a hierarchical set consisting of a plurality of valid Superset(I), Set(I, J) and Subset (i, j, k).   g. Reorganize the end user&#39;s request to create Advanced Glyphs that further distills and shrinks the size of the environment using the [BX] or ‘After’ request.   h. Recognize Advanced Glyphs to determine if it already exists in the ‘CORE List’. If the Advanced Glyph exists in the ‘CORE List’ the output is readily available and preprocessed no further calculations are required. Otherwise, the system must continue with [CX] or ‘Improved’ and [DX] or ‘Optimal’ samples.   i. Request the server to perform the ‘Improved’ sample by hierarchical distributing the search amongst subordinate based on ownership of the primary, secondary and tertiary keyword or term cluster. The Basic and Advanced Glyphs are used to assign size parameter to each valid set of the hierarchical set.   j. Adjust dynamically the value of each keyword and term cluster.   k. Exclude identified Zero Cluster keywords.   l. Emphasize through rules of association and transitivity a plurality of requests that are considered to have common denominator elements and are then correlated into a partial environment. The partial environment consists of a plurality of request. The partial environment retains the characteristic of each individual request.   m. Deemphasize unrelated keywords to the last significant end user&#39;s request. This process is also known as Mulligan and is uses set theory to determine the relationship between input and the last significant request.   n. Maximize keyword values by using the Hot Algorithm that measures the usage pattern and significance of a keyword in a session.   o. Minimize keyword values by using the Cold Algorithm that weights keyword irrelevancy. ‘Zero Clusters’ and unrelated keywords have a reasonable probability of hiding the optimal result.   p. Correlate at least one partial environment into the [CX] or ‘Improved Samples. This process draconically reduces the environment size using Hot &amp; Cold Algorithm parameters and stores the essence of the matter into the Super Site of each valid and visible page.   q. Assign a corporate signature to each Super Site.   r. Pick the small [CX] Sample top results of each hierarchical set to generate a collection of Super Pages.   s. Distill the small [CX] Sample using geospatial dimensions that have exact or estimated latitude and longitude components such as Country, Region, LATA, Zip Code, IP Address and ANI.   t. Commercialize keywords if the already exist in the Commercial Glyph database.   u. Deciphers, analyzes the actual content (gray matter), measures TQM level of satisfaction (trending) of each page in order to pick using reasoning the [DX] or optimal sample.   v. Translate the interactive input into a Cholti language Super Glyph equation.   w. Respond with the output or optimal response. The output may be identified as already existing in the preprocessed ‘CORE List’ in step h) “recognize all preprocessed calculations in the search pattern database. New search patterns not found in the search pattern database perform steps i) “request” to v) “translate”.   x. Display to the end user the output or optimal request. The formatted output is considered an object.   y. Recalculate each time the “optimal button” is clicked in the web browser and significant difference event is estimated compared to the latest Super Glyph equation or partial environment.   z. Consolidate a plurality of partial environment into a resultant environment that is contained with the valid environmental size of the hierarchical set.       

     Cholti Triangulates the Search Process 
     1 st  transforms vague searches into Super Glyph ideas and simulates the human brain to assign a search strategy [LL], [LR], [RL], and [RR] and anchor or commercial cluster and employees independent variable (I) to create the improved environment with 1,000,000 pages, and thus eliminates independent variable (I) from any further calculation. 
     2 nd  amends vague searches into concise searches employing rules of association and relevance to create the optimal environment with 10,000 pages, and thus eliminates independent variable (J) from any further calculation. 
     3 rd  improves concise searches into precise searches and then measures the actual content based on likeness and trending to create the Optimal solution with 100 web pages, and thus eliminates independent variable (K) from any further calculation. 
     4 th  Ameliorates precise searches into optimal searches and then ‘Cherry Pick’ the actual content to create an optimal environment of the top response, and also expands the Search Pattern Super Glyph equation when changing the environment. 
     In conclusion static and vague searches use the Internet environment with billions of web pages. Cholti converts the vague search into static or dynamic Glyph equations that create a Search Pattern that is best described as a managerial hierarchical informational pyramid object as follows: 
     1 st  maps English language input to the left brain equation and geospatial input to the right brain equation, and the determines the dominant tendency of the brain to assign a Search Strategy to create a Join Pyramid or Super Block that maps a relevant environment with 1 billion pages as the lowest level of informational certainty. 
     2 nd  utilizes the anchor Glyph and Commercial Glyph to purify and shrink the size of the environment, and the uses the primary independent variable (I) to changes the vague search into a concise search that creates a Simple Pyramid or Block that maps an improved environment with 1,000,000 web pages that replaces and eliminates the primary independent variable (I) from further calculation. Assigning the Simple Pyramid with a partial master index. 
     3 rd  uses independent variable (J) to changes the concise search into a precise search that creates a Hybrid Pyramid or Sub Block that maps an optimal environment with 10,000 web pages that replaces and eliminates the secondary independent variable (J) from further calculation. Assigning the Hybrid Pyramid with a partial master index. 
     4 th  uses independent variable (K) to changes the precise search into an optimal search that creates a Complex Pyramid or Mini Block that maps an optimal solution with 100 web pages that replaces and eliminates the tertiary independent variable (K) from further calculation. Assigning the Complex Pyramid with a partial master index. 
     5 th  Cherry picks the optimal solution using checkmate combination independent variables finds the Optimal Pyramid with the final destination. 
     Final clarification when independent variables are eliminated from further calculation they create higher tiered Informational Pyramid Structure objects as the informational certainty improves as follows:
         a. Each Search Strategy eliminates the geometric growth of the Internet and binds a vague search into a Join Pyramid that maps a relevant environment with 1 billion pages.   b. The primary independent variable (I) changes the vague search into a concise search and creates the Simple Pyramid that maps an improved environment with the top 1,000,000 web pages.   c. The secondary independent variable (J) changes the concise search into a precise search and creates the Hybrid Pyramid that maps an optimal environment with the top 10,000 web pages.   d. The tertiary independent variable (K) changes the precise search into an optimal search and creates the Complex Pyramid that maps an optimal solution with the top 100 web pages.   e. The Simple Pyramid filters exclusively the relevant environment with the primary independent variable (I) and thus the Superset(I) mathematical notation. The Internet absent of (I) relevant to the search.   f. The Hybrid Pyramid filters exclusively the improved environment with secondary independent variable (J) and thus the Set(I, J) mathematical notation. The Internet absent of (I, J) relevant to the search.   g. The Complex Pyramid filters exclusively the optimal environment with tertiary independent variable (K) and thus the Subset(I, J, K) mathematical notation. The Internet absent of (I, J, K) relevant to the search.   h. 1 st  Simple Pyramid mapped the improved environment and expands the mathematical Glyph equation by adding the 1 st  key featured associations. 2 nd : Hybrid Pyramid mapped the optimal environment and expands the mathematical Glyph equation by adding the 2 nd  key featured associations. 3 rd : Complex Pyramid mapped the optimal solution and expands the mathematical Glyph equation by adding the 3 rd  key featured associations.   i. The ‘Cherry Picking’ process uses the checkmate combination variables to find the final destination by using the inductive reasoning popularity score and the deductive reasoning actual content score.   j. The final destination and top (n) pages are sent to the end user&#39;s browser as output.       

     BRIEF SUMMARY OF THE INVENTION 
     In a nutshell, existing Boolean algebra search engines  100  mimics inductive reasoning Watson like criminal investigation methods for finding the best results  199 , whereas the HIVE  200  solves for the optimal answer using Sherlock Holmes deductive reasoning approach to decipher the content of each page to find the final destination  299  within the best results  199 . 
     Internet environment must be organized by the HIVE  200  supercomputer. The Optimizer system  300  is the client side of the architecture and behaves in the same manner as traditional browser that is able to identify interactive input and upon detecting a new keyword or term cluster creates or updates via the Search Pattern Module  310  which is immediately displayed on the end user&#39;s browser. Then it maps and plots keywords and assigns them to [L] left or [R] right side of the brain. 
     The [L] left side of the brain consists of a managerial hierarchical relationship dataset, hereinafter referred to as the Dataset Module  350 , which is used to statistically shrink the environment. The [R] right side of the brain is the secondary method of shrinking the environment by using geospatial information. Each time the Optimizer system  300  detects a significant change the Dataset Module  350  updates Search Pattern Module  310 . The Shopping Cart system  400  interacts with the user via the Assist Module  410  that facilitates a list of assist input command instructions that refine the interactive input. Furthermore, the Human Brain Module  450  upon detecting commercial keywords inserts smart input and the corresponding GPS information. Finally, the Human Brain Module  450  facilitates a list of TQM personal input command instruction  81 - 85  that eliminate the confounding elements of the search process. 
     The Optimizer  300  and Shopping Cart  400  systems, continuously measures the interactive input and will show a (+), (++), (+++) or its graphical equivalent to notify by how much can the Assist Module  410 , can improve the search. (+) denote independent variables. When the Shopping Cart  400  figures out a direct request it will display a (++++++) or its graphical equivalent to inform the user the final destination  299  was found. The Human Brain Module  450  is designed to find the final destination  299 . This is done before, now, or after a search  150  or direct search  250  is executed when the user is interacting with the Search Engine Optimizer system  1000  to improve the search. 
     The Optimizer systems  300  has the Data Mining Module  500  that “Cherry Picks” the best results  199 , and then dynamically updates the Dataset Module  350 , while randomly surfing the web. The Shopping Cart system  400  upon obtaining from the Human Brain Module  450  a direct search (++++++) command instruction that yields the final destination  299 , facilitates the Sales Module  700  that puts buyers and sellers together. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various exemplary embodiments of this disclosure will be described in detail with reference to the following figures, wherein like numerals reference like elements, and wherein: 
         FIG. 1  presents an exemplary Search Engine Optimizer system; 
         FIG. 2  presents a flow diagram outlining an exemplary process for the Search Engine Optimizer system; 
         FIG. 3  presents an exemplary block diagram of the Optimizer subsystem; 
         FIG. 4  presents an exemplary Search Engine Optimizer system interacting with user via a browser; 
         FIG. 5  presents another exemplary Search Engine Optimizer system interacting with user via a browser; 
         FIG. 6  presents another exemplary Search Engine Optimizer system interacting with user via a browser; 
         FIG. 7  presents an exemplary block diagram of the Shopping Cart system; 
         FIGS. 8A-8D  present an exemplary Assist Module diagram that measures accuracy based on assist input; 
         FIGS. 9A-9D  present an exemplary Assist Module diagram that measures accuracy based on command instructions; 
         FIGS. 10A-10D  present an exemplary Assist Module diagram that builds assisted input; 
         FIGS. 11A-11D  present an exemplary Human Brain Module diagram that measures accuracy based on command instructions; 
         FIGS. 12A-12D  present an exemplary Human Brain Module diagram that measures accuracy based on input; 
         FIGS. 13A-13D  present an exemplary Human Brain Module diagram that interactively builds input; 
         FIGS. 14A-14D  present another exemplary Human Brain Module diagram that interactively builds input; 
         FIGS. 15A-15D  present an exemplary Human Brain Module diagram that builds a Direct Search; 
         FIGS. 16A-16D  present an exemplary Shopping Cart system that measures the accuracy of smart input; 
         FIGS. 17A-17D  present exemplary Shopping Cart system that measures the accuracy of personal input; 
         FIGS. 18A-18D  present another exemplary Shopping Cart system that measures the accuracy of personal input; 
         FIG. 19  presents a block flow diagram of the X_FOB method; and 
         FIG. 20  presents a block flow diagram of the Y_CDIF method. 
     
    
    
     DETAILED DESCRIPTION OF EMBODIMENTS 
       FIG. 1  shows an exemplary Search Engine Optimizer  1000  system using the Internet  1  comprising of an Optimizer  300  and a Shopping Cart  400  system working in parallel that includes terminals  11 - 18  computers, inductive reasoning  100 , and deductive reasoning  200  search engines, computer networks  301  and  401 . The Optimizer system  300  has a Computer Network  301  consisting of Search Pattern  310 , Dataset  350 , Data Mining  500  modules, with databases  311 ,  312 ,  351 ,  352 ,  510 , and  530 . The Shopping Cart system  400  has a Computer Network  401  consisting of Assist  410 , Human Brain  450 , and Sales  700  modules, with databases  411 ,  412 ,  451 ,  452 ,  710 , and  730 . 
     As used herein, the term “module” is intended to include one or more computers configured to execute one or more software programs configured to perform one or more functions. As used herein, the term “computer” is intended to include any data processing device, such as a desktop computer, a laptop computer, a mainframe computer, a parallel cluster supercomputer, a server, a hand held device, or any such device able to process data. The aforementioned components of the Search Engine Optimizer system  1000  represent computer hardware and/or computer-implemented software configured to perform the function described in detail within below. The components of the Search Engine Optimizer system  1000  may be implemented on one or more communicatively connected computers. The term “communicatively connected” is intended to include, but is not limited to, any type of connection, whether wired or wireless, in which data may be communicated, including, for example, a connection between devices and/or programs within a single computer or between devices and/or programs on separate computers. 
     As used herein, the term “inductive reasoning” Search Engine  100  is intended to include any search engine supercomputer that ranks and measure the popularity score of each Site and Page belonging to Internet  1  and performs “static searches” while users randomly surf the web. As used herein, the term “static search” is intended to include a single search, whereas the term “dynamic search” comprises one or more searches belonging to a session. “Static searches” use Glyphs, and “Dynamic Searches” use Super Glyphs. The term “Glyph” comprises the statistical vector components of a valid keyword and/or clusters that are used to identify relevant pages. The term cluster is a combination of two or more words that placed in an exact order have a significant meaning E g “American”, “Of”, “States” and “United” is best known for the cluster “United States of America”. The term “Super Glyph” is applied to Glyphs that are gain factored when using the Hot/Cold, Likelihood and Relevancy analyses of the incorporated references. 
     As used herein, the term “deductive reasoning” Search Engine  200  is a method for simulating the entire potential valid interactive input  10  regular expressions construed during an Internet browser search, converting the results sets into Environmental Summary reports object that enables efficient and accurate searching. Furthermore, uses index relationships to eliminate pages from the search process, and dependent and complement variable to gain factors relevancy. Finally, mimicking the Human Brain, linguistic Glyphs are assigned to the [L] left side and geospatial Glyphs are assigned to the [R] right side of the brain and the Anchor is the best common denominator Glyph that is used as the primary index relationship. 
     The Optimizer  300  is communicatively connected to a Browser  10  by users  11 - 18  while they are performing a web search. The Search Pattern Module  310  scans, distills and converts interactive input during a web search into keywords and term clusters that are then transformed into vector Glyphs. The Dataset Module  350  arranges, analyzes, and prioritizes Glyphs into a managerial hierarchical relationship, and uses Hot/Cold, Likelihood and Relevancy analyses to create a mathematical equation that optimally shrinks the environment. The Data Mining Module  500  reads and measures each page and then gain factors matches of the Search Pattern and key featured associations by document, paragraph and sentence to figure out the final weighted value. The Optimizer System  300  Cherry Picks the best pages  199  with the highest final weighted value. 
     The Shopping Cart System  400  is communicatively connected to a Browser  10  by users  11  to  18  while they are performing a web search. The Assist Module  410  interactively facilitates a list of assisted input  80  command instructions with valid keywords and term clusters that simplifies building a search, and can serves as a bona fide spellchecker when encountering a misspelled word within the input. The Human Brain Module  450  uses Hot/Cold, Likelihood and Relevancy analyses to identify key featured associations. Then measures the relevant partition of the Internet  1  with the optimal equation and figures out the final destination  299 . The Sales Module  700  performs financial transactions using X_FOB  710  and Y_CDIF  730  methods. 
     In operation of the Search Engine Optimizer system  1000 , a user at the terminal  11  may input a search request using the browser  10 . The search request consists of interactive input  10  created or assisted input  80  copied from existing document by the user. Having received the search request from the browser  10 , the terminal  11  may communicate with the Search Engine Optimizer system  1000  via the Internet  1  to search using the Optimizer system  300  in accordance with the search request. For example, for each search request, the Search Pattern Module  310  and Dataset Module  350  create a mathematical equation using a managerial hierarchical index relationship that optimally shrinks the environment. Allowing the Search Engine Optimizer system  1000  to search the databases  311 - 352  via the data network  301  and retrieve search results. The Data Mining Module  500  analyzes the search results obtained from Search Engines  100  and  200 , and perform the process of “Cherry Picking” the best responses  199 . The output display is a formatted object that may be a graphical representation of the search request that is capable of being adjusted and modified by a user and by the Search Engine Optimizer system  1000 , and will be described in greater detail below. The Search Engine Optimizer system  1000  then communicates with the terminals  11 - 18  via the browser  10  to display the output. 
     In operation of the Search Engine Optimizer system  1000 , a user at the terminal  11  may input a search request using the browser  10 . The search request consists of interactive input  10  created or assisted input  80  copied from existing document by the user, or the voice text equivalent obtained from smart input  90  technologies. Having received the search request from the browser  10 , the terminal  11  may communicate with the Search Engine Optimizer system  1000  via the Internet  1  to search using the Shopping Cart system  400  in accordance with the search request. For example, for each search request, the Shopping Cart system  400  uses the Assist Module  410  to identify assisted input that in turn is converted into a mathematical equation that optimally shrinks the environment. The Human Brain Module  450  creates the managerial index relationship that allows the Search Engine Optimizer system  1000  to search the databases  411 - 452  via the data network  401  and retrieve search results. The Sale Module  700  analyzes the search results obtained from Search Engines  100  and  200 , and finds the final destination  199 . Using the X_FOB method  710  that considers each page of the Internet a no cost In hand inventory the Search Engine Optimizer system  1000  then communicates with the terminal  11  via the  10  to display the output as well as the retrieved search results to the user. Alternatively, puts buyers and seller together using the Y_CDIF method  730  and performs a financial transaction. 
     The Search Engine Optimizer system  1000  may also provide suggestions assisted input  80  and smart input  90  command instructions to the user to refine the  350  Dataset. For example, the Search Engine Optimizer system  1000  may use the Data Mining Module  500  to analyze the search results and Sales Module  700  using smart input GPS coordinates to interact with the user to obtain command instructions that eliminates confounding variables to create an improved Dataset  350 . The improved Dataset  350  and corresponding statistical information becomes the output object that is displayed on the user&#39;s browser  10 . 
     It should be understood that responsive to the interactive input, the terminal  11  may send the changes to the Search Engine Optimizer system  1000 , repeatedly until the user is satisfied with the search results. 
     Responsive to the interactive input the terminal  11  upon determining the user&#39;s intent to perform a financial transaction the Search Engine Optimizer system  1000 , puts buyers and sellers together using X_FOB  710  and Y_CDIF  730  methods incorporated by reference. 
       FIG. 2  is a flow diagram outlining an exemplary process of the Search Engine Optimizer system  1000  working with an inductive reasoning Search Engine  100 , (e.g. Bing, Google or Yahoo) and/or Deductive Reasoning Search Engine  200 , (e.g. XCommerce). The Optimizer system  300 , helps a user using an inductive reasoning Search Engine  100  to interactively build a search request. The search request is broken up into keywords and clusters that are converted into Glyphs. The Optimizer system  300  creates a Search Pattern profile  310  based on the interactive input entered by the user. The Search Pattern profile  310  is a mathematical equation representation of the interactive input entered by the user. The Search Pattern profile  310  is converted into a Request Dataset  350 . The Request Dataset  350  is a managerial hierarchical relationship index to shrink the environment optimally using the independent variables to eliminate pages and the dependent and complement variables also known as key featured association to gain factor relevant pages 
     The Optimizer system  300 , can also help a user using a deductive reasoning Search Engine  200  to interactively build a search request. Upon detecting changes in the interactive input the Shopping Cart system  400 , is made available with a list of assisted and smart input command instructions. In this way, assisted input command instructions are readily available to begin a request. The Search Engine Optimizer system  1000  will search in its database for assisted input  80  and smart input  90  command instructions. In this case, Assist Module  410  displays a list of valid Glyph that will serve as the basis of the search. This permits the end user to type . . . Am, and selects from the valid list of Glyphs “American” and then to continue by typing C and from the list of valid Glyphs selects “American Civil War”. The assisted input  80  “American Civil War” becomes text input. 
     E.g. Assist Module converts the commercial Glyph Walmart and incorporates the user&#39;s IP Address or GPS coordinates to create smart input  90 . The smart input  90  Walmart becomes text input. 
     At any time the end user can click on the Shopping Cart graphical display and instantiate the Human Brain module  450 , to help the end user using the latest Request Dataset  350  to create new set of command instructions that improve the accuracy and precision of the Search Engine Optimizer system  1000 . The accepted end user&#39;s instructions yield: Search  150  or Direct Search  250 . 
     The Search  150  is sent to either an inductive reasoning  100  or deductive reasoning  200  Search engine, and the top results  199  are displayed on the end user&#39;s terminal. The Optimizer system  300  uses the Data Mining Module  500  to analyze the top results  199  and creates a Session Dataset  350  with dynamic instead of statics values Glyphs, since the first request failed to reach the final destination  299 . This iterative process of randomly surfing the web may be repeated until the user is satisfied with the top results  199  that now becomes the final destination  299  and stops requiring further refinement. 
     The Direct Search  250  bypasses randomly surfing the web process and maps the final destination  299  for a final decision. Now that we have input that precisely plots output we need to know what the user really wants. The Financial Transaction Module  700  can now perform X_FOB, a free of cost Inventory In hand, financial transaction by furnishing the checkmate combination information the user wants, e.g. the Address Information, Telephone and Directions to a Business location. In addition once the final destination  299  is found the Shopping Cart  400  becomes a traditional cart using the Financial Transaction Module  700  and based on the user&#39;s approved instructions puts buyer (user) and seller (best priced supplier) together and performs a Y_CDIF transaction. Note: free downloads are considered X_FOB financial transactions since they are considered free of cost Inventory In Hand. 
       FIG. 3  is a flow diagram outlining an exemplary process of the Search Engine Optimizer  1000 , interacting with a user using a browser  10  in real time. Interactive analysis means at incipiency. In this case, the Optimizer  300  and Shopping Cart  400  systems are continuously analyzing output and translating the information into the computerized version of the Mayan Cholti language, that uses the [L] left and [R] right side of the brain to break down an idea. First: we must analyze the tense of the analysis of the interactive text analysis, and assign accuracy limits. [Ax] represents any analysis before the Search command instruction is sent to a  100  or  200  Search Engine, and the accuracy is the lowest since it is an estimate. [Bx] represents a second analysis that is automatically, performed once a Search Engine  100  or  200  responds with the top results  199 . [Cx] represents a post factum analysis that is performed by the Human Brain module  450 , to identify additional key features associations. Thus, [Ax] is before, [Bx] is present, and [Cx] is after interactive input that solves for the top results  199 . The [Dx] represents the personal input that is the new features to the cross references related applications. The [Dx] Cherry Picking process mirrors what Sir. Isaac Newton did by assigning limits such as zero and infinity when developing modern calculus to improve Euclidian geometry and later the quantum physics advancements Albert Einstein did for non-Euclidean geometries in particular the theory of general relativity. 
     To reach 100% precision, we must remove the confounding components of the search, these are assumptions that search engines  100  and  200  do not take into account, these assumptions are based on Total Quality Management levels of customer satisfaction, and thus require the system to ask and obtain confirmation command instruction, to solve for Direct Searches that yield the final destination  299 . What is new for the Search Engine Optimizer system  1000 , consists of the Shopping Cart system  400  functions that permit during the [Ax] before, [Bx] present, and [Cx] after to eliminate the confounding variables of the search process, that will permit to solve for the final destination  299 . Based on the tense the search process has implicit confounding variables: [Ax] before has at least 3, [Bx] present has at least 2 and [Cx] after has at least 1 confounding variables and thus Table 7 applies. 
     
       
         
               
             
               
               
             
           
               
                 TABLE 7 
               
               
                   
               
               
                 Precision limits by input type 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 [Ax] 
                 interactive input: 2 indices with 86% accuracy and 1 million pages. 
               
               
                 [Bx]  
                 assisted input: 3 indices with 95% accuracy and 10,000 pages. 
               
               
                 [Cx]  
                 smart input: 4 indices with 99% accuracy and 100 pages. 
               
               
                 [Dx] 
                 personal input: 6 indices with 100% accuracy and 1 page. 
               
               
                   
               
             
          
         
       
     
     [Ax] Before: The Optimizer system  300  scans the browser&#39;s interactive input  10 , and the Search Pattern Module  310  breaks the input into keywords and optimally into term clusters that are transformed into Glyphs that are then arranged in a managerial hierarchical manner upon identify the independent variables within a request that will be used to create a Request Dataset  350 . This process is done until the end user performs a Search command instruction  150  that sends the interactive input to a Search Engine  100  or  200 . 
     [Bx] Present: The Optimizer system  300  now scans the Top Results  199 , and the Search Pattern Module  310 , analyzes each page to find key featured associations and the best fit paragraph content and dynamically calculates, prioritizes, arranges and find missing gaps of information within the Search Pattern that will be used to create a Session Data  350 . The static Glyphs become dynamic Super Glyphs and the interactive input  10  becomes assisted input  80 , since the Search Engine Optimizer system  1000  will analyze at least one request belonging to a session. At this point the end user can quit the search, or use the Shopping Cart system  400 . 
     [Cx] After: A NO command instruction means the user will continue to randomly surf the web and a YES activates the Assist Module  410  that interact with the Human Brain Module  450  triggering Total Quality Management customer satisfaction command instructions feedback that eliminates confounding variables and creates the Optimal Dataset  350 . If the Optimal Dataset  350  has NO checkmate combination that yields the final destination  299  the user continues randomly surfing the web the assisted input  80  becomes smart input  90 . If YES the smart input  90  becomes personal input  99  and the user is TQM Satisfied  999 . 
     Hereinafter, TQM command instructions will now become independent variables that will be used by to solve for the final destination and thus interactive text input will no longer consist of text and analysis of the top results  199 , but will include decisions that eliminate confounding elements of the search process. 
     The evolution of the Search Engine Optimizer system  1000 , where  FIG. 4  reflects the flow of the original reference application,  FIG. 5  its continuation and  FIG. 6  the new stuff. Each of these figure interact with a browser  10  and the user discretion through assisted input  80  that store and display the interactive text input of an user while directly and/or randomly surfing the web. Each keyword and cluster term is converted into a Glyph  20 , where the first letter the side of the brain that is preferably used when solving for a solution. E denotes English language based and is used by the left side of the brain, G denotes geospatial and is used by the right side of the brain, and M denotes Mixed and can be used by both sides of the brain, and D denotes Total Quality Management decision that eliminate confounding variables of the search process. 
     Each request is assigned an inductive reasoning mass value  30  that ranges from 0 to 8 (where 8 reflects Certainty, and 0 the Spaghetti Phenomena of chaos and anarchy). Each request is assigned an inductive reasoning estimated environment size value  31  that ranges from 1 to one trillion (where 1 reflects Certainty, whereas and one trillion (2^40) the Spaghetti Phenomena of chaos and anarchy). Each request is assigned an inductive reasoning Logic Grid  32  and gamma function equation  33 . Each request is assigned a deductive reasoning mass value  40  that ranges from 0 to 8 (where 8 reflects Certainty, whereas 0 relevant). Each request is assigned a deductive reasoning estimated environment size value  41  that ranges from 1 to one billion (2^30) (where 1 reflects Certainty, whereas and one billion what is relevant). Each request is assigned a deductive reasoning Logic Grid  42  and gamma function equation  43 . 
     Using the number of independent variables within a request an inductive reasoning category  50  and a deductive reasoning category  60  are displayed as follows: 0=Vague, 1=Concise, 2=Precise, 3=Optimal and 4=Answer searches. Optimal and Answer searches have at least one probabilistic total quality management command instruction that enables them to exceed the 86% accuracy threshold. 
       FIGS. 4 through 6  highlight the interactive text input process of a user to create the final request “American Civil War Robert Lee Gettysburg”. (A) Start with the user starting the process typing a single keyword in this case “American”. (B) The user continues to typing interactive input  10  or uses assisted input  80  to improve the text input to “American Civil War” the term cluster. (C) The user continues to typing in the browser  10  or uses assisted input  80  to improve the text input to “American Civil War Robert Lee” by adding the “Robert Lee” term cluster. (D) The user continues to type interactive input  10  or uses smart input  90  to improve the input to “American Civil War Robert Lee Gettysburg” by adding the geospatial and relevant “Gettysburg” keyword. 
     Cholti converts the keywords into term clusters  70  that is part of the Search Pattern, where M 1 +E 1 +E 2  becomes C 1  or “American Civil War” and E 3 +E 4  becomes C 2  or “Robert Lee”. C 1  is converted into Assist Glyph  411 , that becomes the primary index relationship that should yield the final destination, and thus the final destination will include the assisted input  80  “American Civil War” within its content. During the interactive process the system determines that the two clusters  70  and M 2  or “Gettysburg” are directly related. Since, M 2  or Gettysburg is geospatial or smart input  90  the system creates an Assist Event  412  or “Battle of Gettysburg”, and finds the missing gaps of information and by adding Pennsylvania, Gettysburg GPS coordinates, Jul. 1, 1863 time stamp, order of battle, and historic sites. Now, Cholti uses the smart input  90  to perform the user&#39;s search, and if the user wants generic description of the Battle of Gettysburg, the answer will reside in an encyclopedia or high quality content site, otherwise the search will continue and the user will continue to surf the web, until the real purpose of the search is known. 
       FIG. 7  shows an exemplary block diagram of the Shopping Cart system  400  as the Search Engine Optimizer  1000  gathers the Browser&#39;s interactive input  10 , while randomly surfing the web. [Ax] before the user selects from a list of assisted input  80  command instruction offered by the Assist Module  410  and then the Optimizer subsystem  300 , creates a Request Dataset  350  using the selected command instruction corresponding managerial index relationships. [Bx] now the user command instructs a Search  150 , and will receive as output the Top Results  199  and will continue to surf the web, with assistance of the Human Brain Module  450 , that will dynamically match/merge at least one request into a session, and create a Session Dataset  350 , and will gain factor key featured association based on their frequency and nearness to the managerial index relationships. The Human Brain Module  450  will offer additional keywords and decisions as command instruction to figure out a Direct Request  250  that yields the final destination  299 . Each command instruction is accompanied with the content derived from the analysis of the Top Results  199  and the corresponding gained factor value (from 0 bad to 10 optimal) of the Session Dataset  350 . Now the Data Mining Module analyzes the final destination  299 , since the user no longer need to randomly surf the web. Using the [Cx] after interactive text input creates an Optimal Dataset  350  that also includes the user accepted command instructions, and thus the input now maps and plots the output. The search process is finished and the user is TQM Satisfied  999 . 
       FIGS. 8 through 18  have the following common elements: Glyph conversion  20  of the user interactive text input, and the corresponding Inductive Reasoning Search statistics Mass  30 , estimated environment size  31 , displayed output logic grid  32 , gamma function  33 , and accuracy  51  and immediately after the corresponding Deductive Reasoning Search statistics Mass  40 , estimated environment size  41 , displayed logic grid  42 , and gamma function  43  and accuracy  61 . By default the Search Engine Optimizer system  1000 , updates and displays inductive reasoning statistics logic grid  32  and accuracy  51  as the end user interactively inputs text, and upon detecting a valid Request Dataset  350  and displays deductive reasoning statistics logic grid  42  and accuracy  61 . The Search Engine Optimizer system  1000  uses an accuracy algorithm from (0-100.00) based on the Request Dataset  350  and estimated environment size to display  51  and  61  accuracy output as an integer ##% or float ##.## % value format. The system converts keywords into cluster  70 , such as M 1 +E 1 +E 2  into C 1  or “American Civil War”. 
     Assist Module  410  decisions are as follows: T 1  or  71  represents a keyword or term cluster. T 3  or  73  represents an event comprising of two independent variables. T 2  or  72  enhances T 1  or  71  by having a quality filter to gain factor pages. T 4  or  74  enhances T 2  or  72  by having quality filter to gain factor pages. T 3  or  73  and T 4  or  74  from the command instructions of the Assist List  421  figures out and expands the [L] brain and [R] Glyph equations with the feedback Glyphs equation  422  or N 1  that attenuates content with confounding variables in order to stabilize and reduces sensitivity parameter variations due to the environment. 
     Human Brain Module  450  decisions are as follows: D 1  or  81  represents a commercial Glyph. D 3  or  83  represents a commercial Glyph with default GPS coordinates. D 2  or  82  enhances D 1  or  81  by having a quality filter to gain factor pages. D 4  or  84  enhances D 2  or  82  by having quality filter to gain factor pages. T 3  or  73  and T 4  or  74  from the command instructions of the Assist List  421  figures out and expands the [L] brain and [R] Glyph equations with the feedback Glyphs equation  422  or N 1  that attenuates content with confounding variables in order to stabilize and reduces sensitivity parameter variations due to the environment. D 5  or  85  is an Exact Decision that creates a Direct Search  250 , and yields the Final Destination  299 . 
       FIG. 8  shows an exemplary Assist Module  410  diagram that measures accuracy based on input. In  FIG. 8A , the user using assisted input  80  selects an Assist Glyph  411  and the command instruction  71  makes the interactive text input “Napoleon Bonaparte” or C 1  that is a cluster  70 . In  FIG. 8B , the user using Assist Glyph  411  selects a High Quality Glyph  412  and the command instruction  72  makes the text input “Napoleon Bonaparte”. In  FIG. 8C , the user picks an Assist Event  413  to improve  FIG. 8  A by adding “Waterloo” and the command instruction  73  makes the interactive text input “Napoleon Bonaparte Waterloo”. In  FIG. 8D , the user picks a High Quality Event  414  to improve  FIG. 8  B by adding “Waterloo” and the  74  command instruction makes the interactive text input “Napoleon Bonaparte Waterloo”. 
     Both  FIG. 8C  and  FIG. 8D  use Request Dataset  350  and corresponding managerial index relationships to build the [L] left brain and [R] right brain equations based on the smart input  90  historical event that took place in Waterloo, Belgium on Jun. 15, 1815, and yes Emperor of France Napoleon Bonaparte and Allied Commander Duke of Wellington as well as their chain of command are part of the equation used to gain factor the content of the Top Results  199 .  FIG. 8  is [Ax] before analysis. Both  FIG. 8B  and  FIG. 8D  have feedback Glyphs equation  422  or N 1  that attenuates confounding variables when parsing and vectoring content value. 
       FIG. 9  shows an exemplary Assist Module  410  diagram that measures accuracy based on command instructions. In  FIG. 9A , the user using assisted input  80  selects an Assist Glyph  411  and the command instruction  71  makes the interactive text input “Napoleon Bonaparte”. In  FIG. 9B , the user using Assist List  421  selects an Assist Glyph  411  and the command instruction  71  makes the interactive text input “Napoleon Bonaparte”. In  FIG. 9C , the user using Assist Glyph  411  selects a High Quality Glyph  412  and the command instruction  72  makes the interactive text input “Napoleon Bonaparte”. In  FIG. 9D , the user using Assist List  421  selects a High Quality Glyph  412  and the command instruction  72  makes the assisted input  80  “Napoleon Bonaparte”. Both  FIG. 9C  and  FIG. 9D  have Feedback Glyphs equation  422  that attenuates confounding variables when vectoring content value. 
       FIG. 10  shows an exemplary Assist Module  410  diagram that interactively builds input. In  FIG. 10A , the user using assisted input  80  selects an Assist Event  413  and the command instruction  73  makes the interactive text input “Napoleon Bonaparte Waterloo”. In  FIG. 10B , the user uses Assist List  421  to correlate “Napoleon Bonaparte” and “Battle of Waterloo” into and Assist Event  413  and the command instruction  73  makes the interactive text input “Napoleon Bonaparte Waterloo”. In  FIG. 10C , the user using Assist Event  413  selects a High Quality Event  414  and the command instruction  74  makes the smart input  90  “Napoleon Bonaparte Waterloo”. In  FIG. 10D , the user uses Assist List  421  to correlate “Napoleon Bonaparte” and “Battle of Waterloo” into High Quality Event  414  and the command instruction  74  makes the input “Napoleon Bonaparte”. Both  FIG. 10C  and  FIG. 10D  have Feedback Glyphs equation  422  that attenuates confounding variables when parsing and vectoring content value. “Napoleon Bonaparte Battle of Waterloo” is valid. 
       FIG. 11  shows an exemplary Human Brain Module  450  diagram that measures accuracy based on command instructions. In  FIG. 11A , the user using assisted input  80  selects Commercial Glyph  451  and the command instruction  81  makes the interactive text input “Walmart”. In  FIG. 11B , the user using Commercial Glyph  451  selects a High Quality Glyph  452  and the command instruction  82  makes smart input  90  “Walmart”. In  FIG. 11C , the user picks a Commercial Event  453  to improve  FIG. 11A  by adding Leg (A) (origin) GPS coordinates and the command instruction  83  makes the interactive text input “Walmart+Leg (A)”. In  FIG. 11D , the user picks a High Quality Event  454  to improve  FIG. 11C  by adding Leg (B) (destination) GPS coordinates and command instruction  84  makes smart input  90  “Walmart+Legs (A+B)”. 
       FIG. 12  shows an exemplary Human Brain Module  450  diagram that measures accuracy based on input. In  FIG. 12A , the user using assisted input  80  selects Event  451  and the command instruction  81  makes the interactive text input “American Civil War+C 2 ”, where C 2  is a valid Key Featured Association. In  FIG. 12B , the user using Event  451  selects a Combo Group  452  and the command instruction  82  makes the input “American Civil War+C 2 +C 3 ”, where C 3  is a valid Key Featured Association. In  FIG. 12C , the user the Combo Group  452  in  FIG. 12B  by selecting D 1  the First Decision  453  and the command instruction  83  makes the input “American Civil War+C 2 +C 3 +D 1 ”, and D 1  is the First Decision  453 . In  FIG. 11D , the user improves the First Decision  453  in  FIG. 11C  by selecting D 2  the Next Decision  454  and command instruction  84  makes assisted input  80  “American Civil War+C 2 +C 3 +D 2 ”, and D 2  is the Next Decision  454 . 
       FIG. 13  shows an exemplary Human Brain Module  450  diagram that measures input. In  FIG. 13A , the user using assisted input  80  selects Commercial Glyph  451  and the command instruction  81  makes the smart input  90  “Walmart”, where D 1  is a valid Zero Cluster. In  FIG. 12B , the user using Commercial Glyph  451  selects a High Quality Commercial Glyph  452  and the command instruction  82  makes the smart input  90  “Walmart”. In  FIG. 13C , the user picks a Commercial Event  453  to improve  FIG. 13B  by validating Leg (A) (origin) GPS coordinates and the command instruction  83  makes the smart input  90  “Walmart+Leg (A)”. In  FIG. 11D , the user picks a High Quality Event  454  to improve  FIG. 13C  by validating Leg (B) (destination) GPS coordinates and the command instruction  84  makes the smart input  90  “Walmart+Legs (A+B)”. 
       FIG. 14  shows another exemplary Human Brain Module  450  diagram that interactively builds input. In  FIG. 14A , the user using assisted input  80  selects Assist Glyph  451  and the command instruction  81  makes the interactive text input “American Civil War+C 2 ”, where C 2  is a valid Key Featured Association. In  FIG. 14B , the user using Assist Event  452  selects a Combo Group and the command instruction  82  makes the text input “American Civil War+C 2 +C 3 ”, where C 3  is a valid Key Featured Association. In  FIG. 14C , the user using Combo Group  453  selects D 1  the First Decision and the command instruction  83  makes the assisted input  80  “American Civil War+C 2 +C 3 +D 1 ”, and now D 1  is the First Decision  453 . In  FIG. 14D , the user improves the First Decision  453  in  FIG. 14C  by selecting D 2  the Next Decision  454  and the command instruction  84  makes the assisted input  80  “American Civil War+C 2 +C 3 +D 2 ”, and D 2  is the Next Decision  454 . 
       FIG. 15  shows an exemplary Human Brain Module diagram that builds a Direct Search.  FIG. 15A  is a continuation of  FIG. 15D , where the user created a High Quality Commercial Event  454  by selecting an Exact Decision  455  and the command instruction  85  makes a Direct Search  210  that yields to the final destination, in this case the personal input  99  has a map with driving directions to a particular store.  FIG. 15B  is another continuation of  FIG. 15D , where the user is linked to the Sales Module  700  enabling X_FOB  710  and Y_CDIF  730  financial transaction methods.  FIG. 15C  is a continuation of the personal input  99  “American Civil War+C 2 +C 3 +D 2 ”, where D 2  is the Next Decision  454 , and C 2  and C 3  are related key featured associations of the “American Civil War” in this case C 2  is Robert Lee, and C 3  is the Battle of Gettysburg, and the command instruction  85  makes an Exact Decision  455 . 
     Once the Shopping Cart system  400  creates a Direct Search  210 , the Sales Module  700  is activated and enables the X_FOB  710  and Y_CDIF  730  financial transaction methods. In  FIG. 15D  the user makes a Purchase Decision  456  and the command instruction  86  commercializes with personal input  99  that permits the user to perform a financial transaction. At this point, the search process is over, and the Sales Module  700  puts buyers and sellers together and now behaves as an online shopping cart, since the user performed a Purchase Decision  456 . The final destination  299  obtained from the Exact Decision  455  contains the personal input  99  that possess the destination Hotel C geospatial information, and also the user&#39;s point of origin, in this case a X_FOB  710  method reservation is offered, or alternatively a Y_CDIF  730  Vacation trip is offered that includes air travel, car rental, insurance and room accommodation information. 
       FIG. 16  shows an exemplary Shopping Cart system that measures the accuracy of smart input  90 .  FIG. 16A  and  FIG. 16C  are examples of the user interaction with the Shopping Cart system  400  to obtain an Exact Decision  455  and the command instruction  85  makes a personal input  99  that builds a Direct Search  250 . In  FIG. 16A  in continuation of  FIG. 15B  the Shopping Cart system  400  transforms the commercial cluster Walmart or C 1  into Z 1  the Zero Cluster or Walmart.com. In  FIG. 16C  the Shopping Cart system  400  transforms the cluster “American Civil War” or C 1  into Z 1  the Zero Cluster or “American Civil War”+High quality definition. Once, the Shopping Cart obtains personal input  99  with an Exact Decision  85  or D 5 , the system reaches optimal precision.  FIG. 16B  measures the accuracy of  FIG. 16A , and  FIG. 16  D measures the accuracy of  FIG. 16C , in both cases the personal input  99  is measured against the final decision  85 . 
       FIG. 16A  is based on smart input  90  “Walmart” the Shopping Cart offers a list of options such as Home Page, Location A to Z based on the IP Address, News, and High Quality definition searches with an accuracy of 96% to 99%. Now, to reach certainty the Shopping Cart system  400  obtains the personal input  99  with an exact decision  85  that creates a Direct Search  210  that yields the final destination  299 . 
       FIG. 17  shows exemplary Shopping Cart system measures personal input  99 . In  FIG. 17A  the user using assisted input  80  selects Assist Glyph  451  and the command instruction  81  makes the interactive text input “Walmart”, where Walmart is transformed into C 1  commercial cluster. When the user selects the Search  150  command instruction, Walmart or C 1  is transformed into a Zero Cluster or Z 1  for Walmart.com that yields the Top Results  199 . Furthermore Zip Code of the IP Address GPS coordinates becomes the smart input  90  point of origin in order to create a map that shows locations prioritized based on distance.  FIG. 17B  the Search Engine Optimizer system determines that  FIG. 17A  has two Direct Search  210  paths and analyzes both outcomes. The first Direct Search  210  path, upon receiving the Search  150  command instruction, becomes an inductive reasoning Assist search using Z 1  or Walmart.com to figure out the first Final Destination  299 . The second Direct Search  210  path, upon receiving the Search  150  command instruction, becomes an inductive reasoning smart input  90  using Zip Code of the GPS coordinates as [R] right brain checkmate equation used to figure out the second Final Destination  299 . The first path uses assisted input  80  and yields Walmart.com, and the second path uses smart input  90  and yields Location A. Since, two or more Final Destination  299  exist, one or both of the path are automatically confounding. This is the reason of the present invention is to figure out the personal input  99  using TQM command instruction that identify the true final destination  299 . 
     In  FIG. 17C  the user using assisted input  80  selects Assist Glyph  451  and the command instruction  81  makes the interactive text input “American Civil War”. When the user selects the Search  150  command instruction, the input is sent to a Search Engine and the output displayed becomes the Top Results  199 .  FIG. 17D  the Search Engine Optimizer system determines that  FIG. 17C  has two Direct Search  210  paths and analyzes both outcomes. The first Direct Search  210  path, upon receiving the Search  150  command instruction, becomes an inductive reasoning assisted input  80  high quality definition using C 1  or “American Civil War” to figure out the first Final Destination  299 . The second Direct Search  210  path, upon receiving the Search  150  command instruction, becomes an inductive reasoning page rank search using popularity score used to figure out the second Final Destination  299 . The first path uses assisted input  80  and yields Encyclopedia A.com, and the second path uses the popularity score yields Site A. Since, two or more Final Destination  299  exist, one or both of the path are automatically confounding. This is the reason of the present invention is to figure out the personal input  99  using TQM command instruction that identify the true final destination  299 . 
     Personal input  99  takes into account the [L] left brain equation (E 1  to EN) Key featured associations, and [R] right brain equation (G 1  to GN) key featured association, and then gain factors the Top Results  199 , and determines that Site B has the best content and is the Final Destination  299 . Finally, Site B is further justified by using the deductive reasoning means of the feedback Glyphs equation  422  or N 1  that attenuates content with confounding variables in order to stabilize and reduces sensitivity parameter variations due to the environment. 
       FIG. 18  shows another exemplary Shopping Cart system that measures personal input  99 .  FIG. 18A  shows the user select Walmart that becomes Z 1  or “Walmart.com”, using smart input  90 , Search Engine Optimizer System  1000 , determines using the Data Mining Module  500  that confounding elements to the search process exist, and minimally the Leg A or point of origin zip code is required to improve the search.  FIG. 18B  the user selects personal input  99  that contains an Exact Decision  85 , validating Legs (A+B), origin and destination, and yields the final destination  299 . Once input maps and directly plots the output the search process is over. Now, the Shopping Cart system  400 , offers the Sales Module  700  that puts buyers and sellers together using X_FOB  710  and Y_CDIF  730  means provided a financial transaction is required. By default the lion share of most final destination  199  is free content, address information or mapping instructions and Sales Module  700  is skipped. For the exceptions  FIG. 19  and  FIG. 20  incorporate the X_FOB  710  and Y_CDIF  730  means to further improve TQM Satisfaction  999 . 
       FIG. 18C  shows the user select “American Civil War”, using assisted input  80 , Search Engine Optimizer System  1000 , expands the mathematical equation using the Data Mining Module  500  to drill and find within the content of the Top Results  199 , what the user is searching. In this case, a list of command instruction will appear in the user&#39;s terminal based on KFA (key featured associations), military leaders, and historical events found within the Top Results  199 . Finally, using the Sherlock Holmes versus Watson paradigm the Data Mining Module  500  will use deductive reasoning exclusions that attenuate content with confounding variables in order to stabilize and reduce sensitivity parameter variations due to the environment. Now, we can paraphrase Sherlock Holmes having him say “Elementary my dear Watson, if we eliminate using deductive reasoning the confounding elements of the search, as improbable as it may seem will simplify and elucidate the exact path that yields the final destination  299 .”  FIG. 18D  the user use the Data Mining Module  500  to figure out the personal input  99  that elucidates the optimal content and thus using deductive reasoning solves the command instruction  95  or D 5  has the Optimal Dataset  350  that yields the final destination  299 . Once input maps and directly plots the output the search process is over and is displayed on the user&#39;s browser  10 . 
       FIG. 19  shows a block flow diagram of the X_FOB method. The Human Brain Module  450  and the Data Mining Module  500  after each valid Search  150  command instruction determine corresponding [Bx] now Session Dataset  350 , and [Cx] after the Optimal Dataset  350 . Both the Human Brain Module  450  and the Data Mining Module  500  interacts with the user until personal input  99  containing an Exact Decision  85  or  95  is encountered that yields the final destination  299 . If the final destination is determined to be free content  701 , the Search Engine Optimizer system  1000 , displays the Optimal Output and the user is TQM Satisfied  999  and the search process either continues with a new search or ends. 
     When the content is deemed to be a purchase, then Sales Module  700  offers the X_FOB  710 , once the system knows the destination of FOB pickup location. When paid content  702  can be provide using the revenues of an Optimal Advertisement  910 , the Sales Module  700  will display the advertisement and use the revenues to pay for the viewing of the intellectual property such as a copyrighted video of the user&#39;s favorite musical band, once the display is done the user is TQM Satisfied  999  and the search process either continues with a new search or ends. 
     When the content is a download product purchase, the Sales Module  700  will begin a X_FOB  710  best price/unit search, and perform the financial transaction upon receiving the checkout  777  command instructions the user is TQM Satisfied  999  and the search process either continues with a new search or ends. 
       FIG. 20  shows a block flow diagram of the Y_CDIF method. The Human Brain Module  450  and the Data Mining Module  500  after each valid Search  150  command instruction determine corresponding [Bx] now Session Dataset  350 , and [Cx] after the Optimal Dataset  350 . Both the Human Brain Module  450  and the Data Mining Module  500  interacts with the user until personal input  99  containing an Exact Decision  85  or  95  is encountered that yields the final destination  299 . If the final destination is determined to be free content  701 , the Search Engine Optimizer system  1000 , displays the Optimal Output and the user is TQM Satisfied  999  and the search process either continues with a new search or ends. 
     When the content is deemed to be a paid product that requires shipping, insurance, freight, then Sales Module  700  offers the Y_CDIF  730 , once the system knows Legs (A+B). The user interacts with Sales Module  700 , until the checkout command instruction  777  is received, and the transaction is executed and the user is TQM Satisfied  999  and the search process either continues with a new search or ends. 
     When the content is deemed to be an emergency paid product and time and not price is the primary variable that requires shipping, insurance, freight, then Sales Module  700  offers RUSH Y_CDIF  799 , once the system knows Legs (A+B). The user interacts with Sales Module  700 , until the checkout command instruction  777  is received, and the transaction is executed and the user is TQM Satisfied  999  and the search process either continues with a new search or ends. 
     In conclusion interactive input  10 , assisted input  80  and smart input  90  intrinsically have confounding elements that hide the final destination  299 . The Hot/Cold, Likelihood and Relevancy analyses figure out key featured association of the mathematical equation that gain factor measured quality of pages and thus improves the precision of the search to 95% accuracy. 
     To reach 100% accuracy and precision the confounding elements of the search must be eliminated. The optimizer  300  and shopping cart  400  systems allows the user to select personal input  99  containing exact decisions  95  that create Direct Search  250 . Each Direct Search  250  uses deductive reasoning means of the feedback Glyphs equation  422  or N 1  that attenuates content with confounding variables in order to stabilize and reduces sensitivity parameter variations due to the environment. Direct Searches  250  eliminate the randomly surfing the web trial and error process and in turn automatically yields the final destination  299 . 
     Personal Input  99  allows the process to be commercialized permitting the Shopping Cart system  400  to put buyers and sellers together. At this point, free content and downloads are immediately, sent to the end user as output. Otherwise X_FOB  710  decisions and Y_CDIF  730  are made available to the user. 
     X_FOB decisions, which measures the FOB price per unit of merchandise being made available through ocean and land transportation means to its physical warehouse originating from participating retailers, wholesalers and suppliers where time is measured in X days dimensions and thus the term X_FOB. 
     Y_CDIF decisions, which measures the CDIF (cargo duties insurance freight) P/U of forecasted inventory in hand merchandise through air transportation means and door to door services to the end user and time is measured in Y hours dimensions and thus the term Y_CDIF; 
     The Shopping Cart system  400  storing ‘The Price’ summary reports and pronounced ‘the price’ and thus the term Z PRICE, by using X days dimensions X_FOB calculations and Y hour dimension Y_CDIF calculations based on said inventory control of “In Hand” and domestic rush FOB, and also purchasing forecasted inventories lots based on demand, economies of scale, historical trends and storage capacities.