Patent Publication Number: US-2020279278-A1

Title: Method and system for combining offline and online identities with associated purchasing intention indicators in view of a geographic location

Description:
CO-PENDING APPLICATION 
     This Nonprovisional Patent Application is a Continuation Application to U.S. Nonprovisional patent application Ser. No. 15/712,036 as filed on Sep. 21, 2017 by self-same Inventors named herein and titled METHOD AND SYSTEM FOR COMBINING OFFLINE AND ONLINE IDENTITIES WITH ASSOCIATED PURCHASING INTENTION INDICATORS IN VIEW OF A GEOGRAPHIC LOCATION. Said U.S. Nonprovisional patent application Ser. No. 15/712,036 is incorporated in its entirety and for all purposes into this Continuation-in-Part Nonprovisional Patent Application. 
    
    
     FIELD OF THE INVENTION 
     The method of the present invention relates to systems and methods for evaluating a likelihood of purchasing intent in view of both information and behavior associated with a potential buyer. More particularly, the present invention relates to systems and methods adapted to generate probabilities of identified entities of selecting and purchasing particular models of product types and makes in view of online actions and other information related to the identified entities. 
     BACKGROUND OF THE INVENTION 
     The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section, or associated with the subject matter of the background section, should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves, may also be inventions. 
     Accurate forecasting of intentions of identified persons and other entities to purchase products by type and by specific product model is of value to vendors and providers of various goods and services in many consumer markets. The prior art includes modeling online behavior and information provided online by users of web browsers as well as formulas for estimating purchase timing and intention in view of information related to identified entities, e.g., individual persons, organizations, and associations. For example, knowing that a particular person is searching the web for performance, configuration, pricing and availability information describing a type of automobile or a make and model of an automobile is interpreted in the prior art to indicate a likelihood that that person might be considered a near-term sales prospect for one or more automobile or truck models. In addition, the prior art teaches that the online and offline purchasing history of an entity and other factors related to a same entity, such as age, annual income level, marital status, home ownership status, domicile location, work address and other factors can also be relevant in assessing the timing and purchasing preferences of the identified entity. Yet the prior art fails to optimally integrate information related to a same entity that can be sourced from both online behavior and additional information to indicate purchasing intent and immediacy of possible purchasing of specific goods and services by category or by make, model, year of manufacture or generation, brand, or reputation. 
     Towards this and other objects made obvious to one of ordinary skill in the art in view of the present disclosure, a system and method are provided that improve the accuracy and reliability of forecasting purchasing intent and likely immediacy of purchase of a specific product or service or type of product or service in view of both (a.) online behavior associated with an identified entity and (b.) additional information descriptive of and/or related to the identified entity. Additional information that might be applied in generating an expectation of likelihood to purchase a good or service includes, but is not limited to, geographic locations of potential purchasers, geographic locations product or service marketing operations, indications of a time and day that information was acquired, indications of a time and day that a specified event occurred, was observed or ended, a demographic category with which an associated entity is classified, and one or a plurality of purchasing history data, financial data, and documentations of events occurring during the life of and affecting an associated entity. 
     SUMMARY AND OBJECTS OF THE INVENTION 
     Towards these objects and other objects that will be made obvious in light of the present disclosure, a method and system for deriving probabilities of user purchasing intentions and intensity in relation to a product or service type, service provider, productized service and/or product model are provided. The method of the present invention (hereinafter, “the invented method”) includes considerations of online actions, i.e., online behavior, associated with a persistent online identifier and with additional information relatable to the persistent online identifier. In one aspect of the invented behavior, purchasing intent is evaluated in view of web searching behavior associated with a particular persistent identity. 
     In a first optional aspect of the invented method, a value of an intensity to purchase is generated by a comprehensive mathematical function that derives the purchasing intensity value from a first plurality of datapoints generated by online activity and a second plurality of datapoints harvested from other parameters and qualities, wherein both pluralities of datapoints are associated with a same entity. In one alternative, a first partial value is derived solely or primarily from the first plurality of datapoints generated by online activity and a second partial value is derived solely or primarily from the second plurality of datapoints, and a joining mathematical function then derives the purchasing intensity value from the first partial value and the second partial value. It is understood that various alternate preferred embodiments of the invented method consider an associated time and date datum in weighting the importance or mathematical magnitude of one or more items of information in a calculation of a purchasing intensity value. 
     In another optional aspect of the invented method, web pages are evaluated and scored for degree of relevance to a particular product, service, product type, service type, or brand, such as an estimation of relevance to information describing an automobile product or service quality. Observed web searching associated with the persistent online identifier that produces an evaluated web page or leads to a request to view an evaluated web page is considered in deriving an evaluation of purchasing interest of an entity associated with the persistent online identifier. In yet another optional aspect of the invented method, visiting a scored web page in a web browser session associated with a persistent online identifier is alternatively or additionally considered in deriving an evaluation of a purchasing interest of an entity associated with the persistent online identifier. 
     In a still other optional aspect of the invented method, the persistent online identifier may be or comprise a hash of a unique user address, wherein the user address may be or comprise an email address, a telephone number, a government issued identification number, an online account number, a postal address, a geolocational identifier, or other personally identifiable information known in the art. 
     A first alternate preferred embodiment of the invented method includes one or more of the aspects of associating a user identity with (a.) recordations of activity in requesting information from assets accessible via an electronic communications network by addressing universal resource identifiers or universal resource locators, to include applying a web browser to render web pages addressable by registered domain names of the World Wide Web; (b.) additional information such as purchasing history, residence address and income level; and/or (c.) estimations of proximity and ease of travel between a geographic location associated with the user identity and a point of sales or services of a product or service type, category, brand, make, or model. 
     In an even additional optional aspect of the invented method, the product or service of interest may be related to or comprise an automobile or other consumer product and/or the associated purchasing history may include citations of previous purchases related to automobile purchase, use, or maintenance. 
     In an even other additional optional aspect of the invented method, a visual rendering of map images is presented by a display screen as directed by a computational device, wherein the map images optionally, additionally or alternatively indicate locations of domiciles, work or employment locations, and/or point of sales facilities as harvested from digitized records of online and/or offline activities, events and associations. 
     In yet other additional optional aspects of the invented method, marketing messaging may be posted by mail service or courier, electronically transmitted, faxed or otherwise delivered by suitable means known in the art and addressed to an intended recipient as identified by the invented method. 
     In accordance with the invented method, a computational system is provided that may comprise or relate to acquiring online behavior associable with an entity identifier. The computational system may be or comprise a digital cellular smart phone, a bundled software and hardware interne-enabled personal computer or workstation. The computational system may be or include a bundled software and hardware product that includes a web browser and one or more user identifiers associable with an entity, a user identity, a cellular smartphone, a network communications-enabled computational system, or other suitable electronic communications device known in the art. 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. The details of one or more embodiments are set forth in the accompanying drawings and the description below. Other features, objects, and advantages will be apparent to one of skill in the art from the description and drawings, and from the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       These, and further features of the invention, may be better understood with reference to the accompanying specification and drawings depicting the preferred embodiment, in which: 
         FIG. 1  is a block diagram of an electronic communications network which enables the invented method and that includes the Internet, a plurality of web servers, a plurality of point of sale servers, an online activity tracking information aggregator system, a mapping web service server, a content publishing system, an evaluator system and a user device; 
         FIG. 2  is process chart of a first preferred embodiment of the invented method; 
         FIG. 3  is a detailed block diagram of the evaluator system of  FIG. 1 ; 
         FIG. 4A  is a consumer database record table comprising a subset of information contained within a first exemplary consumer database record of a consumer database of the evaluator system of  FIG. 3 ; 
         FIG. 4B  is a consumer database record table comprising a plurality of consumer database records of the consumer database of the evaluator system of  FIG. 3  and the first exemplary consumer database record of  FIG. 4A ; 
         FIG. 5  is a flowchart of a generation of an online activity record by one or more servers and systems of the electronic communications network of  FIG. 1 ; 
         FIG. 6A  is an online activity database record table comprising a subset of information contained within an exemplary first activity database record of an online activity database of the evaluator system of  FIG. 3 ; 
         FIG. 6B  is an online activity database table comprising a plurality of online activity database records of the online activity database of the evaluator system of  FIG. 3  and the first exemplary online activity data record of  FIG. 4A ; 
         FIG. 7A  is a flowchart of the generation of a first exemplary entity record by the evaluator system of  FIG. 3 ; 
         FIG. 7B  is a flowchart of an alternate method of correlating newly received hashes of personally identifying information with a volume of information accessible via the entity database of  FIG. 3  and the generation of entity record by the evaluator system of  FIG. 3 ; 
         FIG. 8A  is an entity database record table comprising a subset of information contained within a first exemplary entity database record of an entity database of the evaluator system of  FIG. 3  whereby a consumer database record of  FIG. 4B  is associated with one or more activity database records of  FIG. 6B ; 
         FIG. 8B  is an entity database table comprising a plurality of entity database records of the entity database of the evaluator system of  FIG. 3 , wherein each entity database record preferably associates at least one consumer record of  FIG. 4B  and at least one online activity record of  FIG. 6B ; 
         FIG. 9A  is a first formula database record table comprising a subset of information contained within a first exemplary formula database record of a formula database of the evaluator system of  FIG. 3 ; 
         FIG. 9B  is a second formula database record table comprising a subset of information contained within a second exemplary formula database record of a formula database of the evaluator system of  FIG. 3 ; 
         FIG. 9C  is a third formula database record table comprising a subset of information contained within a third exemplary formula database record of a formula database of the evaluator system of  FIG. 3 ; 
         FIG. 9D  is a fourth formula database record table comprising a subset of information contained within a fourth exemplary formula database record of a formula database of the evaluator system of  FIG. 3 ; 
         FIG. 9E  is a fifth formula database record table comprising a subset of information contained within a fifth exemplary formula database record of a formula database of the evaluator system of  FIG. 3 ; 
         FIG. 9F  is a formula database table comprising a plurality of formula database records of the formula database of the evaluator system of  FIG. 3  and the first exemplary formula database record of  FIG. 9A ; 
         FIG. 10  is a flowchart of a generation of a purchasing intensity value by the evaluator system of  FIG. 3  by application of a formula selected from the formula database of  FIG. 9B  in view of an entity record selected from the entity record database of  FIG. 8B ; 
         FIG. 11A  is a first exemplary point of sale database record table comprising a subset of information contained within a first exemplary point of sale agent record of the POS database of the evaluator system of  FIG. 3 ; 
         FIG. 11B  is a point of sale database table comprising a plurality of point of sale database records of the point of sale database of the evaluator system of  FIG. 3  and the first exemplary point of sale database record of  FIG. 11A ; 
         FIG. 12  is a flowchart of the evaluator system of  FIG. 3  in generating purchasing intensity values without necessity of a query message from a point of sale agent; 
         FIG. 13  is a representation of aspects of a first exemplary purchasing intensity value message as sent from the evaluator system of  FIG. 3 ; 
         FIG. 14  is a flowchart of a point of sale system of  FIG. 1  in generating a first exemplary query message and sending the first exemplary query message to the evaluator system of  FIG. 3 ; 
         FIG. 15  is a query table of aspects of the first exemplary query message as sent to the evaluator system of  FIG. 3 ; 
         FIG. 16  is a flowchart of the evaluator system of  FIG. 3  in generating a first exemplary query response message and sending the first exemplary query message to a point of sale system of  FIG. 1 ; 
         FIG. 17  is a response table of aspects of the first exemplary query response message as sent from the evaluator system of  FIG. 3 ; 
         FIG. 18  is a flowchart of one of the point of sale systems of  FIG. 1  visually rendering a map image, wherein the map images indicates locations selected from the consumer database records of  FIG. 4B ; 
         FIG. 19  is a representation of a rendering of the map image as generated by the method of  FIG. 18  and by a point of sale system of  FIG. 1 ; 
         FIG. 20  is a representation of aspects of a targeted marketing message as sent from the evaluator system of  FIG. 3  and addressed to an address selected from the consumer database of  FIG. 4B ; 
         FIG. 21  is a flowchart of the evaluator system of  FIG. 3  in scoring universal resource locators in relation to specific product models, product types, services, service types and brands and in view of content accessible via a particular universal resource locator; 
         FIG. 22  is URI score record table of selected contents of an exemplary URI scoring record as applied by the method of  FIG. 21 ; 
         FIG. 23  is a URI database table of selected contents of a plurality of URI score records; 
         FIG. 24  is a block diagram of an exemplary webserver of the electronic communications network of  FIG. 1 ; 
         FIG. 25  is a block diagram of the user device of the electronic communications network of  FIG. 1 ; 
         FIG. 26  is a block diagram of content publishing system of the electronic communications network of  FIG. 1 ; 
         FIG. 27  is a block diagram of aggregator system of the electronic communications network of  FIG. 1 ; 
         FIG. 28  is a block diagram of an exemplary point of sale system of the electronic communications network of  FIG. 1 ; and 
         FIG. 29  is a block diagram of the mapping web service server of the electronic communications network of  FIG. 1 . 
     
    
    
     DETAILED DESCRIPTION 
     A method and apparatus for developing and managing Internet transactions is described. In the following description, numerous specific details are set forth in order to provide a more thorough description of the present invention. It will be apparent, however, to one skilled in the art, that the present invention may be practiced without these specific details. In other instances, well-known features have not been described in detail so as not to obscure the invention. 
     It is understood that word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any aspect described herein as “exemplary” is not necessarily to be construed as exclusive, preferred or advantageous over other aspects. 
     Referring now generally to the Figures and particularly to  FIG. 1 ,  FIG. 1  is a block diagram of an electronics communications network  100  by which a first preferred embodiment of the method of the present invention (“first method”). The electronics communications network  100  (hereinafter, “network”  100 ) that optionally comprises the Internet  102 , a telephony network  103 , a plurality of web servers  104 A- 104 N, a user device  106 , a content publishing system  108  (hereinafter, “content publisher”  108 ), an online activity tracking information aggregator system  109  (hereinafter, “aggregator”  109 ), an evaluator system  110 , a plurality of point of sale systems  112 A- 112 N and a mapping web service server  114 . The telephony network  103  is bi-directionally communicatively coupled with the Internet  102  and may be or comprise one or more wireless telephone communications networks and landline telephone networks. It is understood that the network  100  may further comprise additional electronic communications systems or networks, a plurality of content publishers  108  and aggregators  109  that are not indicated on  FIG. 1  for the sake of clarity. 
     The user device  106  may be or comprise a bundled hardware and software product such as (a.) an IPHONE™ cellular telephone as marketed by Apple, Inc. of Cupertino, Calif.; (b.) an HTC TITAN II™ cellular telephone as marketed by AT&amp;T, Inc. of Dallas, Tex. and running a WINDOWS 7™ operating system as marketed by Microsoft Corporation of Redmond, Wash.; (c.) a GALAXY NEXUS™ smart phone as marketed by Samsung Group of Seoul, Republic of Korea and/or running an ANDROID™; (d.) a network-communications enabled THINKSTATION WORKSTATION™ notebook computer marketed by Lenovo, Inc. of Morrisville, N.C.; (e.) a NIVEUS 5200 computer workstation marketed by Penguin Computing of Fremont, Calif. and running a LINUX™ operating system or a UNIX™ operating system; (f) a network-communications enabled personal computer configured for running WINDOWS XP™, VISTA™ or WINDOWS 7™ operating system marketed by Microsoft Corporation of Redmond, Wash.; (g.) a MACBOOK PRO™ personal computer as marketed by Apple, Inc. of Cupertino, Calif.; (h.) an IPAD™ tablet computer as marketed by Apple, Inc. of Cupertino, Calif.; (i.) a TOUGHPAD™ tablet computer as marketed by Panasonic Corporation of Kadoma, Osaka, Japan and running an ANDROID™ operating system as marketed by Google, Inc. of Mountain View, Calif.; or (j.) other suitable computational system or electronic communications device known in the art. 
     One or more of the plurality of web servers  104 A- 104 N, the content publishers  108 , the aggregators  109 , the evaluator system  110 , the plurality of point of sale systems  112 A- 112 N or the mapping web service server  114  may be or comprise a bundled hardware and software product such as (a.) a network-communications enabled THINKSTATION WORKSTATION™ notebook computer marketed by Lenovo, Inc. of Morrisville, N.C.; (b.) a NIVEUS 5200 computer workstation marketed by Penguin Computing of Fremont, Calif. and running a LINUX™ operating system or a UNIX™ operating system; (c.) a network-communications enabled personal computer configured for running WINDOWS XP™, VISTA™ or WINDOWS 7™ operating system marketed by Microsoft Corporation of Redmond, Wash.; (d.) a MACBOOK PRO™ personal computer as marketed by Apple, Inc. of Cupertino, Calif.; (e.) an IPAD™ tablet computer as marketed by Apple, Inc. of Cupertino, Calif.; or (f.) other suitable computational system or electronic communications device known in the art. 
     The mapping web service server  114  (hereinafter, “the mapping system”  114 ) may be, comprise, host or enable bi-directional communications with a suitable commercially available mapping information provider known in the art, such as GOOGLE MAPS™ as provided by Google, Inc. of Mountain View, Calif. and accessible via a web browser at the domain name https://www.google.com/maps of the World Wide Web, MAPQUEST™ provided by Verizon Communications, Inc. and accessible via a web browser at the domain name https://www.google.com/maps of the World Wide Web https://www.mapquest.com. 
     Referring now generally to the Figures and particularly to  FIG. 2  and  FIG. 3 ,  FIG. 2  is a process chart of the first method and  FIG. 3  is a block diagram of the evaluator system  110 . In step  2 . 00  of the first method criteria for determining relevance of content of information addressable via a Universal Resource Identifier (hereinafter, “URI”) regarding a product type or product model the computer as a relevance algorithm ALG. 01 -ALG. 04  is stored with the evaluator system  110 . In step  2 . 02  the separate content of a plurality of URI&#39;s, to include websites, of the network  100  or accessible via the network  100  are examined and scored for relevance to the selected product type and/or product model by the evaluator system  110  in accordance with the exemplary first relevance algorithm ALG. 01 , a URI scoring database USCR.DB, wherein the resultant scores are stored in a URI database URI.DB of the evaluator system  110 . In step  2 . 04  a first activity record AREC. 001  of online behavior of an entity associated with a first persistent online identifier ID.NET. 001  (hereinafter, “first online identifier” ID.NET. 001 ) and exhibiting indications of an intent to purchase a specified product or service, product type or service type, or a range of branded products or services is acquired by the evaluator system  110 . It is understood that the first online identifier ID.NET. 001  may be a software cookie, an element of a software cookie, an email address, a legal name of a natural born person, a telephone number, an account name, a government issued identifier or tax system identifier, a name of an organization, a name of a corporation, a name of a limited liability company, a name of an association, other identifier of a distinguishable entity and/or the first online identifier ID.NET. 001  may be or comprise a partial or complete hash of any of these items of digitized information. It is understood that the term “entity” as meant in the present disclosure includes natural born human beings, families, software agents or processes, associations, organizations, partnerships, ventures, enterprises, businesses, companies, corporations, governmental actors and groups of entities. 
     In step  2 . 06  a consumer database CON.DB that includes information separately associated with identifiable entities is acquired by the evaluator system  110 . 
     In step  2 . 08  a correlation between one or more of the entities referenced in the consumer database CON.DB and the first online identifier ID.NET. 001  of the first activity record AREC. 001  is attempted by the evaluator system  110 . If no correlation is found in step  2 . 08 , the evaluator system  110  returns to step  2 . 02 . 
     If a correlation of information and the first online identifier ID.NET. 001  is found by the evaluator system  110  in step  2 . 8 , the evaluator system  110  proceeds onto step  2 . 09  and to update an entity database EN.DB with the contents of the first activity record AREC. 001  wherein correlations between information stored in the consumer database CON.DB and the activity database ACT.DB are stored. 
     The evaluator system  110  proceeds from step  2 . 09  and to step  2 . 10  to apply one or more multivariate intent algorithms MVALG. 001 -MVALG.N of the multivariate database MV.DB of  FIG. 3 ,  FIG. 9A  and  FIG. 9B  to the relevant information of the consumer database CON.DB and the first activity record AREC. 001  to determine if a sufficient level of intention indications is present. When the resultant intention value of one or more multivariate intent algorithms MVALG. 001 -MVALG.N fail to indicate sufficient indications of purchasing intent in step  2 . 10 , the evaluator system  110  proceeds to step  2 . 02 . In the alternative, when a sufficient indication of purchasing intent is found by the evaluator system  110  in step  2 . 10 , the evaluator system  110  proceeds on to determine if an indication of geographic location is either directly or indirectly associated with the first persistent online identifier ID.NET. 001  in step  2 . 12 . If no geographic association is found in step  2 . 12 , the evaluator system  110  returns to step  2 . 02 . In the alternative, when a geographic association is found in step  2 . 12 , the evaluator system  110  proceeds on to step  2 . 14  and determines whether one or more point of sale systems  112 A- 112 N is associated with a point of sale location that is sufficiently proximate to the geographic location discovered in step  2 . 12 . 
     When the evaluator system  110  determines in step  2 . 14  that one or more point of sale systems  112 A- 112 N is associated with a point of sale location that is sufficiently proximate to the geographic location discovered in step  2 . 12 , the evaluator system  110  in step  2 . 16  informs the selected point of sale systems  112 A- 112 N of the finding of a sales prospect exhibiting behavior indicative of a purchasing intent of the selected product or service by product name, product type or brand., Marketing messaging may optionally be communicated to the sales prospect in step  2 . 18 . 
     In the alternative, when the evaluator system  110  fails in step  2 . 14  to identify at least one point of sale system  112 A- 112 N to be associated with a point of sale location that is sufficiently proximate to the geographic location discovered in step  2 . 12 , the evaluator system  110  may optionally proceed on to step  2 . 18 , or in other alternate preferred embodiments of the method of the present invention, the evaluator system  110  would proceed back to again perform step  2 . 02 . 
     The evaluator system  110  determines in step  2 . 20  whether to temporarily halt the process of step  2 . 02  through  2 . 20  or to proceed on to alternate computational operations of step  2 . 22 . Referring now generally to the Figures and particularly to  FIG. 3 ,  FIG. 3  is a block diagram of the evaluator system  110 . The evaluator system  110  includes a central processing unit  110 A and a system memory  110 B that are bi-directionally communicatively coupled by an internal communications bus  110 C. The internal communications bus  110 C additionally bi-directionally couples the central processing unit  110 A and the system memory  110 B with a network interface  110 D, a human operator input module  110 E, a display module  110 F and a telephony interface  110 G. The human operator input module  110 E enables an operator to input commands and data to the central processing unit  110 A and the system memory  110 B via the internal communications bus  110 C. The display module  110 F enables visual rendering of information as directed by the central processing unit  110 A. The network interface  110 D bi-directionally communicatively couples the central processing unit  110 A with the network  100 . 
     The system memory  110 B stores an operating system OP.SYS, a hashing derivation software HASH. SW and a system software SYS.SW. The system software SYS.SW enables the evaluator system  110  to perform and provide all relevant aspects of the invented method. The hash derivation software HASH. SW enables generation of the first hash HASH. 001  and additional hashes HASH. 002 -HASH.N of personally identifying information, such as, but not limited to, generating hashes from one or more entity names NAME. 001 -NAME.N, email addresses EMAIL. 001 -EMAIL.N, cellular phone numbers CELL. 001 -CELL.N, account identifiers ACCT. 001 -ACCT.N, a mobile device identifier MOB. 001 , and/or a government issued identifier GOV. 001 . It is understood that the government issued identifier GOV. 001  may be a passport number, a student identifier, a social insurance account number or identifier, a workman&#39;s compensation account number or identifier, a driver&#39;s license account number or identifier, a social services account number or identifier, and/or a Social Security 
     Account Number of the government of the United States of America 
     It is also understood that the hash derivation software HASH. SW may optionally or alternatively be in conformance with a commonly available hashing software, such as, but not limited to, a hashing software that applies the MD5 algorithm as designed by Ronald Rivest of the Computer Science and Artificial Intelligence Laboratory of the Massachusetts Institute of Technology of Cambridge, Mass., or other suitable hashing or cryptographic software or algorithm known in the art. 
     The system memory  110 C further stores, maintains and makes accessible the entity database EN, the URI database URI.DB, the URI scoring database USCR.DB, the consumer information database CON.DB, an online activity database ACT.DB and a plurality of algorithms ALG. 01 -ALG. 04 . It is understood that in various alternate preferred embodiments of the invented method that one or more of the databases and algorithms applied therein may be alternatively or additionally stored outside of the evaluator system  110  in one or more data storage systems (not shown) that are accessible to the evaluator system  110  via the network  100  and/or an alternate electronic communications network (not shown). 
     The URI database URI.DB preferably maintains a listing of Universal Resource Identifiers, to include domain names of the World Wide Web, Universal Resource Locators, and other network addresses that facilitate locating and exchanging information with informational assets accessible via the network  100 . The URI database URI.DB further maintains a refreshable score of relevancy of an associated URI to a particular product, product type, service type, specific service and/or brand. 
     The consumer database CON.DB includes consumer information separately with identified entities, wherein such consumer information preferably includes geolocational data of each entity and additional consumer information relevant in evaluating an intensity of interest in purchasing at least one product type and/or product model, such as an automobile type or a specific automobile make, model and year. 
     It is understood that in the present disclosure that the scope of meaning of the term automobile includes vehicles powered by an internal combustion engine, an electric motor, a hydrogen fuel cell, and/or a hybrid combination thereof. 
     The online activity database ACT.DB includes separate records, wherein each record is preferably associated with a particular persistent online identifier ID.NET. 001 -ID.NET.N and documenting activity such as searching, accessing and/or browsing activity within the network  100 . It is understood that the numerical designation of “.N” is meant to indicate that the quantity of individual data of a series of a certain type of data, e.g., persistent online identifiers ID.NET. 001 -ID.NET.N, may be arbitrarily large and as required by a particular application of the invented method. It is further understood that the numerical designation of “.N” is not meant to indicate that different series of distinguishable instances of particular systems, servers, data or record types are of a same quantity of occurrences, but rather that each series referred to as having N members or instances may be arbitrarily large and as required by a particular application of the invented method. 
     It is further understood that one or more of the databases EN.DB, ACT.DB, CON.DB, URI.DB, USCR.DB &amp; POS.DB may optionally, alternatively or additionally be or comprise a relational database management system, such as an IBM DB2 Universal Database™ server marketed by IBM Corporation of Armonk, N.Y., or other suitable relational database management system known in the art. It is further understood that one or more of the databases EN.DB, ACT.DB, CON.DB, URI.DB, USCR.DB &amp; POS.DB may optionally, alternatively or additionally be or comprise an object-oriented database management system, such as an Object Oriented DBMS as marketed by Objectivity, Inc. of San Jose, Calif., or other suitable object-oriented database management system known in the art. It is yet further understood that one or more of the databases EN.DB, ACT.DB, CON.DB, URI.DB, USCR.DB &amp; POS.DB may optionally, alternatively or additionally be or comprise a HADOOP™ distributed file system as developed by the Apache Software Foundation of Forest Hills, Md., or other suitable file system known in the art. 
     The evaluator system  110  further comprises a plurality of software programs stored in system memory  110 B, to include a web browser BROWSER.SW, an email client EMAIL.SW, a texting client TEXT.SW, and a network communication software NET.SW. The web browser BROWSER.SW enables the evaluator system  110  to retrieve, present, render and traverse information resources on the World Wide Web via and/or within the network  100 , and may be a SAFARI™ web browser provided by APPLE of Cupertino, Calif., or other suitable web browser known in the art. The email client EMAIL.SW enables the evaluator system  110  to communicate by email transmissions with servers and systems  104 A- 114  of the network  100  via the telephony interface  110 G and/or the network interface  110 D. The texting client TEXT.SW enables the evaluator system  110  to communicate by text messaging with servers and systems  104 A- 114  of the network  100  via the network interface  110 D and/or the telephony network interface  110 .G. The network communication software NET.SW enables the evaluator system  110  to communicate by other suitable messaging protocols known in the art with servers and systems  104 A- 114  of the network  100  via the telephony interface  110 G and/or the network interface  110 D. The evaluator system  110  may optionally store a database hash DBHASH but is a hash generated by applying the hashing software HASH. 001  of a volume of information sourced from or referenced by one or more databases EN.DB, CON.DB, POS.DB &amp; ACT.DB. 
     Referring now generally to the Figures and particularly to  FIG. 4A ,  FIG. 4A  is a first table  400  comprising a subset of information contained within a first exemplary consumer database record CREC. 001  of the consumer database CON.DB of the evaluator system  110 . The first exemplary consumer database record CREC. 001  (hereinafter, “the first consumer record” CREC. 001 ) includes a first consumer record identifier CREC.ID. 001  that uniquely identifies the first consumer record CREC. 001  within the consumer database CON.DB. The first consumer record CREC. 001  preferably additionally includes one or more distinguishable instances of personally identifiable information, such as a first entity name NAME. 001 , a first email address EMAIL. 001 , a first cellular phone number CELL. 001 , a first account identifier ACCT. 001 , a first insurance process identifier INS. 001 , a first mobile device identifier MOB. 001  and/or a first government issued identifier GOV. 001  that identify a first entity. The first consumer record CREC. 001  preferably yet additionally includes a first geographic location identifier LOC. 001 , and a plurality of consumer information data fields CINFO. 001 -CINFO.N that preferably contain information related to an entity that is related to one or more of the distinguishable instances personally identifiable information of the same first record CREC. 001 . One or more of the plurality of consumer information data fields CINFO. 001 -CINFO.N may be associated with an individual consumer data record time-date stamp CTDS. 001 -CTDS.N, wherein each consumer data record time-date stamp CTDS. 001 -CTDS.N preferably indicates when a time and day associated with the generation, occurrence, receipt or observation of the information of one or more of the plurality of consumer information data fields CINFO. 001 -CINFO.N. 
     Referring now generally to the Figures and particularly to  FIG. 4B ,  FIG. 4B  is a first database table  402  comprising a plurality of consumer database records CREC. 001 -CRE.N of which the first consumer record CREC. 001  is an example. 
     Referring now generally to the Figures and particularly to  FIG. 5 ,  FIG. 5  is a flow chart of a generation of an activity record AREC. 001 -AREC.N by one or more servers  104 A- 104 N and systems  108  &amp;  109  of the network  2 . For the purpose of clarity of illustration, the method of  FIG. 5  will be discussed in the disclosure as an instance of the content publisher  108  interacting with the user device  106  as an example of generation of a first exemplary activity record AREC. 001 . It is understood that the method of  FIG. 5  is also applied by the servers  104 A- 104 N and system  109  in generation of other activity records AREC. 001 -AREC.N. 
     In step  5 . 00  the content publisher  108  connects with the network  100 . In step  5 . 02  the content publisher  108  receives a content request message either directly from the user device  106  or from the user device  106  via a web server  104 A- 104 N. In step  5 . 04  the content publisher  108  generates and formats an exemplary first activity record AREC. 001 . The content publisher  108  in step  5 . 06  determines whether the content request message received in step  5 . 02  includes a persistent online identifier ID.NET. 001 -ID.NET.N, such as a software cookie  106 A that had been previously recorded into a user web browser  106 B (as shown in  FIG. 25 ) or other suitable persistent online identifier ID.NET. 001 -ID.NET.N associated with the user device  104 . When the content publisher  108  in step  5 . 06  does not detect a persistent online identifier ID.NET. 001 -ID.NET.N in the content request message received in step  5 . 02 , the content publisher  108  proceeds on to step  5 . 08  and assigns a persistent online identifier ID.NET. 001 -ID.NET.N to a digitized content. The persistent online identifier ID.NET. 001 -ID.NET.N detected in step  5 . 06  or alternatively newly assigned in step  5 . 08  is written into the first exemplary activity record AREC. 001  in step  5 . 10 . 
     The digitized content is communicated to the user device  106  in step  5 . 12 , with either (a.) the persistent online identifier ID.NET. 001 -ID.NET.N detected in step  5 . 06 , or (b.) the persistent online identifier ID.NET. 001 -ID.NET.N assigned in step  5 . 08 . 
     In step  5 . 14  the content publisher  108  determines whether the user device  106  has submitted a personally identifying information, e.g., such as an entity name NAME. 001 -NAME.N, an email address EMAIL. 001 -EMAIL.N, a cellular phone number CELL. 001 -CELL.N, account identifier ACCT. 001 -ACCT.N, an insurance process identifier INS. 001 , a mobile device identifier MOB. 001 , or a government issued identifier GOV. 001 . 
     When the content publisher  108  detects receipt of a personally identifying information in step  5 . 14 , the content publisher  108  applies the hashing algorithm MD5 to the received personally identifying information in step  5 . 16  to derive an exemplary first hash HASH. 001  (hereinafter, “the first hash” HASH. 001 ) and adds the first hash HASH. 001  to the activity record AREC. 001 -AREC.N in step  5 . 18 . 
     In step  5 . 20  the content publisher  108  determines sends the first activity record AREC. 001 -AREC.N of step  5 . 04  with the first has HASH. 001  to the evaluator system  110 . The content publisher  108  next determines in step  5 . 22  whether to perform another instance of the loop of steps  5 . 02  through  5 . 22  to alternatively proceed on to alternate operations in step  5 . 24 . 
     Referring now generally to the Figures and particularly to  FIG. 6A ,  FIG. 6A  is an online activity database record table  600  comprising a subset of information contained within an exemplary first online activity database record AREC. 001  of the online activity database ACT.DB of the evaluator system  110 . The exemplary first online activity database record AREC. 001  (hereinafter, “the first activity record” AREC. 001 , preferably includes a first activity record identifier AREC.ID. 001  that uniquely identifies the first activity record AREC. 001  within the online activity database ACT.DB. The first activity record AREC. 001  preferably further includes a first hash HASH. 001  of a personally identifiable information, e.g., the first network identifier ID.NET. 001 , that is acquired or observed by at least one of the plurality of webservers  104 A- 104 N, one more content publishers  108 , one or more aggregators  109 , and/or one or more of the plurality of point of sales system POS  112 A- 112 N. For the sake of clarity of explanation, the present disclosure shall explicate the case where the first hash HASH. 001  is equal to and represents the first distinguishable instances personally identifiable information. 
     The first activity record AREC. 001  still further preferably includes information that documents activity within the network associated with the first hash HASH. 001 , wherein such information may include domain names visited in browsing sessions, user behavior within websites, search engine tasking and URI&#39;s addressed and applied for access to information. 
     For example, a first activity data field set DFS. 001  of the first activity record AREC. 001  includes a first URI address URI. 001  that indicates a first Universal Resource Indicator that was visited and a most recent activity time and date ATDS. 001  that this first Universal Resource Indicator was accessed; a second activity data field set DFS. 002  of the first activity record AREC. 001  includes a second URI address URI. 002  that is a domain name of a website that was visited and a second date time stamp ATDS. 002  of a most recent activity time and date that this website was accessed; a third activity data field set DFS. 003  of the first activity record AREC. 001  documents user behavior within a website and optionally includes a behavior date time stamp ATDS. 003  of a most recent activity time and date that this website behavior was observed; and a fourth activity data field set DFS. 004  of the first activity record AREC. 001  includes plurality of keyword submitted for search requests to a search engine and a most recent search activity time and date ATDS. 004  that this search was tasked with the indicated keywords. It is understood that one or more online activity database records AREC. 001 -AREC.N may contain other recordations of user interaction with the network associable with one or more persistent online identifiers ID.NET. 001 -ID.NET.N. 
     Referring now generally to the Figures and particularly to  FIG. 6B ,  FIG. 6B  is an online activity database table  602  comprising a plurality of online activity database records AREC. 001 -AREC.N of the online activity database ACT.DB of which the first activity record AREC. 001  is an example. In a plurality of the online activity database records AREC. 001 -AREC.N includes a HASH. 001 -HASH.N that is observed as a persistent online identifiers ID.NET. 001 -ID.NET.N by one or more of the plurality of webservers  104 A- 104 N, one more content publishers  108 , one or more aggregators  109 , and/or one or more of the plurality of point of sales system POS  112 A- 112 N 
     Referring now generally to the Figures and particularly to  FIG. 7A ,  FIG. 7A  is a flowchart of the generation of a first exemplary entity record EREC. 001  by the evaluator system  110 . In the interest of clarity of explanation, the method of  FIG. 7A  will be discussed in reference to a first activity record AREC. 001  and a first consumer record CREC. 001  in generating a first entity record EREC. 001 . It is understood that the method of  FIG. 7A  may be applied to the plurality of activity records AREC. 001 -AREC.N and the plurality of consumer records CREC. 001 -CREC.N to generate the plurality of entity records EREC. 001 -EREC.N. 
     In step  7 . 00  the evaluator system  110  connects to the network  100  and receives the first activity record AREC. 001  containing the first hash HASH. 001  in step  7 . 02 . The evaluator system  110  determines in step  7 . 04  whether the first hash HASH. 001  is already recorded in an existing entity record EREC. 002 -EREC.N. When the evaluator system  110  determines in step  7 . 04  that the first hash HASH. 001  is already recorded in an existing entity record EREC. 002 -EREC.N, the evaluator system  110  proceeds on to step  7 . 06  and adds the first activity record identifier AREC.ID. 001  to the entity record EREC. 002 -EREC.N that already contains the first hash HASH. 001 , and the evaluator system  110  thereupon proceeds on to step  7 . 08  to newly calculate one or more purchasing intensity values from the information associated by the entity record EREC. 002 -EREC.N comprising the first hash HASH. 001 . 
     When the evaluator system  110  determines in step  7 . 04  that the first hash HASH. 001  is not recorded in an existing entity record EREC. 002 -EREC.N, the evaluator system  110  initializes a counter value CTR in step  7 . 10  begins selecting counter records CREC. 001 -CREC.N in succeeding instantiations of steps  7 . 12  and steps  7 . 14 . In step  7 . 12  the evaluator system  110  selects a consumer record CREC.CTR and applies the hash algorithm of step  5 . 16  to each personally identifying information detected in the consumer record CREC.CTR selected in the most recent execution of step  7 . 12 . When the evaluator system  110  in step  7 . 16  finds a match of a hash generated in the most recent execution of step  7 . 14  with the first hash HASH. 001 , the evaluator system  110  proceeds on to step  7 . 18  and generate the first entity record EREC. 001 . Given that in the explanatory example of the generation of the first entity record EREC. 001 , it is understood that a hash of the first email address EMAIL. 001  of the first consumer record CREC. 001  matches the first hash HASH. 001 , in step  7 . 18  the evaluator system  110  populates the first entity record EREC. 001  with the first hash HASH. 001 , the first consumer record identifier CREC.ID. 001 , first activity record identifier AREC.ID. 001 , the first consumer location LOC. 001  harvested from the first consumer record CREC. 001 , and optionally additional information harvested from the first consumer record CREC. 001  and the first activity record AREC. 001 . Optionally, the first entity record EREC. 001  may be populated to include or reference additional information harvested from any other activity records AREC. 001  that reference or include (a.) the first hash HASH. 001 , or (b.) any online identifier ID.NET. 001 -ID.NET.N that included in or is referenced by the first activity record AREC. 001 . 
     When the evaluator system  110  in step  7 . 16  fails to find a match of any hash generated in the most recent execution of step  7 . 14 , the evaluator system  110  proceeds on to step  7 . 20  to determine if the counter value CTR has reached or exceeded a maximum counter value MAX that indicates that all of the plurality of consumer records CREC. 001 -CREC.N have been processed in an instantiation of step  7 . 14 . 
     When the evaluator system  110  determines in step  7 . 20  that the counter value CTR has not reached or exceeded the maximum value MAX, the evaluator system  110  proceeds on to step  7 . 22  and increments the counter value CTR. The evaluator system  110  proceeds from step  7 . 22  to an additional execution of step  7 . 12 . Alternatively, the evaluator system  110  proceeds from step  7 . 20  to step  7 . 24  when the evaluator system  110  determines in step  7 . 20  that the counter value CTR has reached or exceeded the maximum value MAX and to perform alternate operations of step  7 . 24 . 
     Referring now generally to the Figures and particularly to  FIG. 7B ,  FIG. 7B  is a flowchart of an alternate method of correlating hashes of personally identifying information with an information accessible via the entity database EN.DB and the generation of entity record EREC. 001 -EREC.N by the evaluator system of  FIG. 3 . In step  7 . 04  when a previously received hash is not detected in a newly received activity record AREC. 001 -ACRE.N, the evaluator system  110  proceeds onto step  2 . 26  and to apply the hashing software HASW. SW to generate a database hash DBHASH from the some or all of the body of information included in or referenced by the entity database EN.DB, to include but not limited to the information contained in the consumer record database CON.DB, the consumer information records CREC. 001 -CREC.N, the activity database ACT.DB, and/or the activity records AREC. 001 -AREC.N. It is understood that the database hash DBHASH but is rather a hash of a volume of information sourced from one or more databases. It is understood that the database hash DBHASH may have been previously generated before a particular execution of step  7 . 26  whereby the previously stored database hash DBHASH is accessed by the evaluator system  110  an compared for hash matches in instant execution of step  7 . 16   
     When a hash match is found in step  7 . 16  between the hash HASH. 001 -HASH.N received in step  7 . 02  and the database hash DBHASH generated in step  7 . 26 , the evaluator system  110  proceeds on from step  7 . 16  to step  7 . 18  and to generate a new entity record EREC. 001 -EREC.N. In the alternative, when a hash match is not found in step  7 . 16  between the hash HASH. 001 -HASH.N received in step  7 . 02  and the database hash DBHASH generated in step  7 . 26 , the evaluator system  110  proceeds on from step  7 . 16  to step  7 . 18  and to generate a new entity record EREC. 001 -EREC.N. 
     Referring now generally to the Figures and particularly to  FIG. 8A ,  FIG. 8A  is an entity database table  800  comprising a subset of information contained within a first exemplary entity database record EREC. 001  of the entity database EN.DB. The first exemplary entity database record EREC. 001  (hereinafter, “the first entity record” EREC. 001 ″) includes a first entity record identifier EREC.ID. 001 , the first hash HASH. 001 , the first consumer record identifier CREC.ID. 001  and the first activity record identifier AREC.ID. 001  and thereby indicates that the information of the first consumer record CREC. 001  and the first activity record AREC. 001  are associated with the same first entity. The entity identified by first email address EMAIL. 001  the first consumer record CREC. 001  is further associated with the first geographic location identifier 
     LOC. 001 , wherein the first geographic location identifier LOC. 001  indicates a primary locus of presence of the first entity, such as a domicile of the entity is a natural born person, or a leading operations station if the first entity is a venture or business operation. It is further understood that one or more of the plurality of entity database records EREC. 001 -EREC.N may associate one or more consumer records CREC. 001 -CREC. 001  with one or more activity records AREC. 001 -AREC.N whereby a same entity may be associated with one or more hashes HASH. 001 -HASH.N of personally identifiable information. 
     Referring now generally to the Figures and particularly to  FIG. 8B ,  FIG. 8B  is an entity database table  802  comprising a plurality of entity database records EREC. 001 -EREC.N of the entity database EN.DB of which the first entity record EREC. 001  is an example. Each entity database record EREC. 001 -EREC.N includes a unique entity record identifier EREC.ID. 001 -EREC.ID.N and preferably associates at least one consumer record CREC. 001 -CREC.N with at least one activity record AREC. 001 -AREC.N, and with a unique hash HASH. 001 -HASH.N, wherein each unique hash HASH. 001 -HASH.N is observed by at least one of the plurality of webservers  104 A- 104 N, one more content publishers  108 , one or more aggregators  109 , and/or one or more of the plurality of point of sales system POS  112 A- 112 N to be a persistent online identifier ID.NET. 001 -ID.NET.N. 
     Referring now generally to the Figures and particularly to  FIG. 9A  through  FIG. 9E ,  FIG. 9A  through  FIG. 9E  each formula database record tables comprising a subset of information contained within a particular formula database record of the multivariate formula database MVF.DB of the evaluator system  110 . 
       FIG. 9A  is a first formula database record table  900  comprising a subset of information contained within a first exemplary formula database record FREC. 001  of the multivariate formula database MVF.DB. The first exemplary formula database record FREC. 001  includes a first formula record identifier FREC.ID. 001 , a first formula identifier IFRM.ID. 001 , a first product identifier PROD.ID. 001 , and the first multivariate formula FORM. 001 . The first multivariate formula FORM. 001  is adapted to derive from data associated with an entity record EREC. 001 -EREC.N an intensity value that indicates an intensity and urgency of an intent by an entity identified in the selected entity record EREC. 001 -EREC.N to purchase the first product identified by the first product identifier PROD.ID. 001 . The first exemplary formula database record FREC. 001  further includes individual criteria CRIT. 001 , CRIT. 843 , &amp; CRIT. 967  of information that may be contained in consumer records CREC. 001 -CREC.N or activity records AREC. 001 -AREC.N. Each individual criteria CRIT. 001 , CRIT. 843 , &amp; CRIT. 967  is associated with a mathematical function operator OP. 001 , OP. 843  &amp; OP. 967 , wherein each paired mathematical function operator OP. 001 , OP. 843  &amp; OP. 967  is separately applied to information associated with a selected EREC. 001 -EREC.N and matching a criteria CRIT. 001 , CRIT. 843 , &amp; CRIT. 967 , and the results of these operations may be summed to generate an intensity value that indicates an intensity and urgency of an intent by an entity identified in the selected entity record EREC. 001 -EREC.N to purchase the first product identified by the first product identifier PROD.ID. 001 . 
       FIG. 9B  is a second formula database record table  902  comprising a subset of information contained within a second exemplary formula database record FREC. 002  of the multivariate formula database MVF.DB. The second exemplary formula database record FREC. 002  includes a second formula record identifier FREC.ID. 002 , a second formula identifier IFRM.ID. 002  and a second product type identifier PRODT.ID. 001 , and a second multivariate formula FORM. 002 . The second multivariate formula FORM. 002  is adapted to derive from data associated with an entity record EREC. 001 -EREC.N an intensity value that indicates an intensity and urgency of an intent by an entity identified in the selected entity record EREC. 001 -EREC.N to purchase the second product type identified by the second product identifier PROD.ID. 001 . The second exemplary formula database record FREC. 002  further includes individual criteria CRIT. 589 , CRIT. 826 , &amp; CRIT. 594  of information that may be contained in consumer records CREC. 001 -CREC.N or activity records AREC. 001 -AREC.N. Each individual criteria CRIT. 589 , CRIT. 826 , &amp; CRIT. 594  is associated with a mathematical function operator OP. 589 , OP. 826  &amp; OP. 594 , wherein each paired mathematical function operator OP. 589 , OP. 826  &amp; OP. 594  is separately applied to information associated with a selected entity record EREC. 001 -EREC.N and matching a criteria CRIT. 589 , CRIT. 826 , &amp; CRIT. 594 , and the results of these operations may be summed to generate an intensity value that indicates an intensity and urgency of an intent by an entity identified in the selected entity record EREC. 001 -EREC.N to purchase the second product identified by the second product type identifier PROD.ID. 002 . 
       FIG. 9C  is a third formula database record table  904  comprising a subset of information contained within a third exemplary formula database record FREC. 003  of the multivariate formula database MVF.DB. The third exemplary formula database record FREC. 003  includes a third formula record identifier FREC.ID. 003 , a third formula identifier IFRM.ID. 003  and a third service identifier SERV.ID. 003 , and the third multivariate formula FORM. 003 . The third multivariate formula FORM. 003  is adapted to derive from data associated with an entity record EREC. 001 -EREC.N an intensity value that indicates an intensity and urgency of an intent by an entity identified in the selected entity record EREC. 001 -EREC.N to purchase the third service identified by the third service identifier SERV.ID. 003 . The third exemplary formula database record FREC. 003  further includes individual criteria CRIT. 583 , CRIT. 921 , &amp; CRIT. 563  of information that may be contained in consumer records CREC. 001 -CREC.N or activity records AREC. 001 -AREC.N. Each individual criteria CRIT. 583 , CRIT. 921 , &amp; CRIT. 563  is associated with a mathematical function operator OP. 583 , OP. 921  &amp; OP. 563 , wherein each paired mathematical function operator OP. 589 , OP. 921  &amp; OP. 563  is separately applied to information associated with a selected EREC. 001 -EREC.N and matching a criteria CRIT. 583 , CRIT. 921 , &amp; CRIT. 563 , and the results of these operations may be summed to generate an intensity value that indicates an intensity and urgency of an intent by an entity identified in the selected entity record EREC. 001 -EREC.N to purchase the third service identified by the third service identifier SERV.ID. 003 . 
       FIG. 9D  is a fourth formula database record table  906  comprising a subset of information contained within a fourth exemplary formula database record FREC. 004  of the multivariate formula database MVF.DB. The fourth exemplary formula database record FREC. 004  includes a fourth formula record identifier FREC.ID. 004 , a fourth formula identifier IFRM.ID. 004  and a fourth service type identifier SERVT.ID. 004 , and the fourth multivariate formula FORM. 004 . The fourth multivariate formula FORM. 004  is adapted to derive from data associated with an entity record EREC. 001 -EREC.N an intensity value that indicates an intensity and urgency of an intent by an entity identified in the selected entity record EREC. 001 -EREC.N to purchase the fourth service type identified by the fourth service type identifier SERVT.ID. 004 . The fourth exemplary formula database record FREC. 004  further includes individual criteria CRIT. 615 , CRIT. 358  &amp; CRIT. 227  of information that may be contained in consumer records CREC. 001 -CREC.N or activity records AREC. 001 -AREC.N. Each individual criteria CRIT. 615 , CRIT. 358  &amp; CRIT. 227  is associated with a mathematical function operator OP. 615 , OP. 358  &amp; OP. 227 , wherein each paired mathematical function operator OP. 615 , OP. 358  &amp; OP. 227  is separately applied to information associated with a selected EREC. 001 -EREC.N and matching a criteria CRIT. 615 , CRIT. 358  &amp; CRIT. 227 , and the results of these operations may be summed to generate an intensity value that indicates an intensity and urgency of an intent by an entity identified in the selected entity record EREC. 001 -EREC.N to purchase the fourth service type identified by the fourth service type identifier SERVT.ID. 004 . 
       FIG. 9E  is a fifth formula database record table  908  comprising a subset of information contained within a fifth exemplary formula database record FREC. 005  of the multivariate formula database MVF.DB. The fifth exemplary formula database record FREC. 005  includes a fifth formula record identifier FREC.ID. 005 , a fifth formula identifier IFRM.ID. 005  and a fifth brand identifier BRND.ID. 005 , and the fifth multivariate formula FORM. 005 . The fifth multivariate formula FORM. 005  is adapted to derive from data associated with an entity record EREC. 001 -EREC.N an intensity value that indicates an intensity and urgency of an intent by an entity identified in the selected entity record EREC. 001 -EREC.N to purchase the fifth brand identified by the fifth brand identifier BRND.ID. 005 . The fifth exemplary formula database record FREC. 005  further includes individual criteria CRIT. 593 , CRIT. 696  &amp; CRIT. 178  of information that may be contained in consumer records CREC. 001 -CREC.N or activity records AREC. 001 -AREC.N. Each individual criteria CRIT. 593 , CRIT. 696  &amp; CRIT. 178  is associated with a mathematical function operator OP. 593 , OP. 696  &amp; OP. 178 , wherein each paired mathematical function operator OP. 593 , OP. 696  &amp; OP. 178  is separately applied to information associated with a selected EREC. 001 -EREC.N and matching a criteria CRIT. 593 , CRIT. 696  &amp; CRIT. 178 , and the results of these operations may be summed to generate an intensity value that indicates an intensity and urgency of an intent by an entity identified in the selected entity record EREC. 001 -EREC.N to purchase the fifth brand identified by the fifth brand identifier BRND.ID. 005 . 
       FIG. 9F  is a formula database table  910  comprising a plurality of formula database records FREC. 001 -FREC.N of the multivariate formula database MVF.DB. 
     Referring now generally to the Figures and particularly  FIG. 10 ,  FIG. 10  is a flowchart of a generation of a purchasing intensity value by the evaluator system  110  by application of a formula FORM. 001 -FORM.N selected from the multivariate formula database MVF.DB in view of an entity record EREC. 001 -EREC.N selected from the entity record database EN.DB. In step  10 . 00  the system software SYS.SW directs the evaluator system  110  to access the multivariate formula database MVF.DB and in step  10 . 02  an individual formula FORM. 001 -FORM.N is selected from the multivariate formula database MVF.DB and in step  10 . 04  a loop counter CTR is initialized to a null value. The selected individual formula FORM. 001 -FORM.N is separately applied to each entity record EREC. 001 -EREC.N in multiple executions of step  10 . 06  in the loop of step  10 . 06  through step  10 . 14 . In step  10 . 08  each resultant purchasing intention value of each application of the selected individual formula FORM. 001 -FORM.N to a unique entity record EREC. 001 -EREC.N of step  10 . 06  is evaluated with a threshold purchasing intensity value. When the resultant purchasing intention value of an application of the selected individual formula FORM. 001 -FORM.N to a unique entity record EREC. 001 -EREC.N of step  10 . 06  is evaluated to be greater than or equal to a threshold purchasing intensity value in step  10 . 08 , the evaluator system  110  proceeds to step  10 . 10 . 
     The evaluator system  110  determines in step  10 . 10  if an indication of geographic location is either directly or indirectly associated with the currently examined entity record EREC. 001 -EREC.N. If no geographic association is found in step  10 . 10 , the evaluator system  110  optionally performs step  10 . 11  proceeds to distribute marketing information to one or more postal or electronic addresses referenced by or included in the instant entity record EREC.CTER. Alternatively or additionally the evaluator system proceeds from step  10 . 10  or step  10 . 11  and returns to step  10 . 12 . 
     In the alternative, when a geographic association is found in step  10 . 10  the evaluator system  110  proceeds on to step  10 . 14  and determines whether one or more point of sale systems  112 A- 112 N is associated with a point of sale location that is sufficiently proximate to the geographic location discovered in step  10 . 10 . When the evaluator system  110  determines in step  10 . 14  that one or more point of sale systems  112 A- 112 N is associated with a point of sale location that is sufficiently proximate to the geographic location discovered in step  10 . 10 , the evaluator system  110  in step  10 . 16  informs the selected point of sale systems  112 A- 112 N of the finding of a sales prospect exhibiting behavior indicative of a purchasing intent of the selected product or service by product name, product type or brand, and optionally provides one or more selected point of sale systems  112 A- 112 N with one or more personally identifying information associated with the currently examined entity record EREC. 001 -EREC.N. 
     The evaluator system  110  proceeds from either step  10 . 10  or step  10 . 16  and to execute step  10 . 12  and to determine if the counter value CTR has achieved a maximum value count of entity records EREC. 001 -EREC.N. When the evaluator system  110  determines in step  10 . 12  that the counter value CTR has not achieved a maximum value count of entity records EREC. 001 -EREC.N, the evaluator system  110  proceeds from step  10 . 12  to step  10 . 18  and increments the counter value CTR. The evaluator system  110  proceeds from  10 . 18  to another instantiation of step  10 . 06 . 
     In the alternative, when the evaluator system  110  determines in step  10 . 12  that the counter value CTR has achieved a maximum value count of entity records EREC. 001 - EREC.N, the evaluator system  110  proceeds from step  10 . 12  to step  10 . 20  and determine whether to select and apply an alternate multivariate formula database MVF.DB in an additional instantiation the loop of steps  10 . 02  through step  10 . 18 . In the alternative, the evaluator system  110  may determine to proceed in step  10 . 20  to step  10 . 22  and to perform alternate computational operations. 
       FIG. 11A  is a first exemplary point of sale database record table  1100  comprising a subset of information contained within a first exemplary point of sale agent record PREC. 001  of the point of sale database POS.DB (hereinafter, “the POS database” POS.DB). The first exemplary point of sale agent record PREC. 001  includes a first point of sale record identifier PREC.ID. 001 , a first POS location data PLOC. 001 , an alternate POS location data PLOC. 001 A, and a POS network address POS.ADDR. 001 . The POS network address POS.ADDR. 001  is a network address at which the first POS system  112 A may be accessed. The first POS location data PLOC. 001  identifies a first geographic point of sales location and the alternate POS location data PLOC. 001 A identifies a second geographic point of sales location. The first point of sale record identifier PREC.ID. 001  uniquely identifies the first exemplary point of sale agent record PREC. 001  within the POS database POS.DB. The first exemplary point of sale agent record PREC. 001  optionally further includes one or more identifiers of products, types of products, services, types of services and brands that are available for sale at the geographic location identified by the first POS location data PLOC. 001  and/or at the alternate geographic location identified by the alternate POS location data PLOC. 001 A. The first exemplary point of sale agent record PREC. 001  further includes the first product identifier PROD.ID. 001 , a fourth product type identifier PRODT.ID. 004  that identifies a fourth product type, an  855   th  service identifier SERV.ID. 855  that identifies an 855 th  service, a 433 rd  service type identifier SERVT.ID. 433  that identifies a 433 rd  service type, and a 233 rd  brand identifier BRND.ID. 233  that identifies a 233 rd  brand. 
       FIG. 11B  is a point of sale database table  1102  comprising a plurality of point of sale database records PREC. 001 -PREC.N of the POS database POS.DB of the evaluator system  110 . 
       FIG. 12  is a flowchart of the evaluator system  110  in generating purchasing intensity values without necessity of receipt of a query message from a POS system POS  112 A- 112 N. In step  12 . 00  the evaluator system operating system OPSYS launches the system software SYS.SW and the system software SYS.SW directs the evaluator system  110  in step  12 . 02  to select an item identifier, e.g., product identifier PROD.ID. 001 -PROD.ID.N, product type identifier PRODT.ID. 001 -PRODT.ID.N, a service identifier SERV.ID. 001 -SERV.ID.N, a service type identifier SERVT.ID. 001 -SERVT.ID.N, or a brand identifier BRND.ID. 001 -BRND.ID.N. The evaluator system  110  selects a multivariate formula FORM. 001 -FORM.N corresponding to the item identifier selected in step  12 . 02 . In step  12 . 06  the evaluator system  110  receives, determines or selects a threshold intensity value to be applied in step  12 . 12 . In optional step  12 . 08  a time length value ΔT is received or set by the evaluator system  110  that may be applied to by the evaluator system  110  in the method of  FIG. 12  to disregard information associated with an entity record EREC. 001 -EREC.N that is associated with a time date stamp TDS. 001 -TDS.N that indicates a time less recent than a current time date value than the time length value ΔT. 
     In step  12 . 10  the evaluator system  110  searches the entity records EREC. 001 -EREC.N of the entity data base EN.DB and applies the multivariate formula FORM. 001 -FORM.N selected in step  12 . 04  to each entity records EREC. 001 -EREC.N. When no purchasing intensity value is generated in step  12 . 12  that exceeds the threshold intensity value of step  12 . 06 , the evaluator system  110  proceeds from step  12 . 12  and to step  12 . 14 . In step  12 . 14  the evaluator system determines whether it is directed by user command or the system software SYS.SW to return to another execution of step  12 . 02  or to proceed on to alternate operations of step  12 . 16 . 
     When at least one purchasing intensity value is generated in step  12 . 12  that exceeds the threshold intensity value of step  12 . 06 , the evaluator system  110  proceeds from step  12 . 12  to step  12 . 18  and to search the POS database POS.DB for point of sale records PREC. 001 -PREC.N that include a POS location data PLOC. 001 -PLOC.N that is determined to indicate a geographic location that is closer than a maximum displacement value ΔD from a geographic location indicated by a geographic location identifier LOC. 001 -LOC.N of an entity record EREC. 001 -EREC.N selected in step  12 . 12  from which a purchasing intensity value is derived that is greater than the threshold value of step  12 . 06 . 
     It is understood that the displacement value ΔD may be generated by application of the mapping web service of the mapping system  114 . It is further understood that the displacement value ΔD may be expressed as estimated travel distance by known roads and common travel routes, as estimated travel time by known roads and common travel routes, as an average travel time by known roads and common travel routes, or other suitable parameters of travel time or transportation convenience known in the art and estimated to be found between a geographic location identifier LOC. 001 -LOC.N of an entity record EREC. 001 -EREC.N examined in step  12 . 18  and a POS location value PLOC. 001 -PLOC.N of a POS record PREC. 001 -PREC.N selected in step  12 . 18 . 
     If no POS record PREC. 001 -PREC.N is found in step  12 . 20  that contains a POS location value PLOC. 001 -PLOC.N that meets the distance variance criteria of step  12 . 20 , the evaluator  110  proceeds from step  12 . 20  to step  12 . 14 . In step  12 . 22  the evaluator system  110  formats and populates one or more messages PMSG. 001 -PMSG.N individually addressed to POS systems  112 A- 112 N. In step  12 . 24  the one or more messages PMSG. 001 -PMSG.N generated in step  12 . 22  are communicated via the network  100  to the POS systems  112 A- 112 N noted as addressees in the one or more messages PMSG. 001 -PMSG.N. 
       FIG. 13  is a message table  1300  showing aspects of a first exemplary purchasing intensity value message PMSG. 001  as sent from the evaluator system  110  to a POS system  112 A- 112 N. The first exemplary purchasing intensity value message PMSG. 001  includes a first POS network address POS.ADDR. 001  of the first POS system  112 A as the destination address; an evaluator network address EVAL.ADDR of the evaluator system as the sender address, the first product identifier PROD.ID. 001 , optionally the intensity threshold level of step  12 . 06 , and a plurality of personally identifying information of potential customers as extracted from information associated with an entity record EREC. 001 -EREC.N. The personally identifying information of the first exemplary purchasing intensity value message PMSG. 001  includes email addresses EMAIL. 001  &amp; EMIL. 002 , cellular telephone numbers CELL. 001 , CELL. 020  &amp; CELL. 734 , a 990 th  consumer record identifier CREC.ID. 990 , a 866 th  legal name NAME. 886 , and a 487 th  account identifier ACCT. 487 . 
     Referring now generally to the Figures and particularly to  FIG. 14 ,  FIG. 14  is a flowchart of the first point of sale system  112 A in generating a first exemplary query message QMSG. 001  and sending the first exemplary query message QMSG. 001  to the evaluator system  110 . 
     In the interest of clarity of explanation, the method of  FIG. 14  will be discussed in reference to the first POS system  112 A generating a first query message QMSG. 001 . It is understood that the method of  FIG. 14  may be applied to the generation of a plurality of query messages QMSG. 001 -QMSG.N by one of the POS systems  112 A- 112 N. 
     In step  14 . 00  the first POS system  112 A connects with the network  100  and formats the first query message QMSG. 001  in step  14 . 02 . The first POS system  112 A enters its own first POS network address POS.ADDR. 001  into the first query message QMSG. 001  in step  14 . 04  as a sender address and enters the evaluator network address EVAL.ADDR as a destination address of the first query message QMSG. 001  in step  14 . 06 . 
     In step  14 . 08  the first POS system  112 A enters the first product identifier PROD.ID. 001  into the first query message QMSG. 001 . It is understood that in alternate and modified applications of the method of  FIG. 14 , a POS system  112 A- 112 N may insert into a query message QMSG. 001 -Q.MSG.N one or more item identifiers, e.g., one or more product identifiers PROD.ID. 001 -PROD.ID.N, one or more product type identifiers PRODT.ID. 001 -PRODT.ID.N, one or more service identifiers SERV.ID. 001 -SERV.ID.N, one or more service type identifiers SERVT.ID. 001 -SERVT.ID.N and/or one or more brand identifiers BRND.ID. 001 -BRND.N. 
     In step  14 . 10  the first POS system  112 A enters an optional distance variance value ΔD into the first query message QMSG. 001 . The first POS system  112 A thereby provides instruction parameters for the evaluator system  110  to search for entity records EREC. 001 -EREC.N that refer to information indicating that one or more identifiable entities that are associated with both (a.) a geographic location sufficiently close to a selected point of sale location, and (b.) information indicating a sufficiently high and current purchasing intensity level of specified goods and/or services to be of interest to an operator of the first POS system  112 A. 
     In optional step  14 . 12  the first POS system  112 A enters into the first query message QMSG. 001  a time displacement value ΔT, whereby the first POS system  112 A specifies a time window limitation of data to be considered in the derivation of purchasing intensity values. In optional step  14 . 14  the first POS system  112 A enters into the first query message QMSG. 001  a first purchasing intensity level value ILEVL. 001  into the first query message QMSG. 001 . 
     The first POS system  112 A transmits the first query message QMSG. 001  via the network  100  in step  14 . 16  and therefrom proceeds to step  14 . 18  and to perform alternate computational operations. 
     Referring now generally to the Figures and particularly to  FIG. 15 ,  FIG. 15  is a query message table  1500  presenting aspects of the first exemplary query message QMSG. 001  as sent to the evaluator system  110 . The first query message QMSG. 001  includes the evaluator system network address EVAL.ADDR as the destination address, the first POS network address POS.ADDR. 001 , the second product identifier PROD.ID. 02 , an optional time displacement value ΔT, an optional distance variance value ΔD, and an optional first purchasing intensity level value IVEVL. 001 . 
     Referring now generally to the Figures and particularly to  FIG. 16 ,  FIG. 16  is a flowchart of the evaluator system  110  in generating a first exemplary query response message RMSG. 001  and sending the first exemplary query message RMSG. 001  to the sender of a query message QMSG. 001 -Q.MSG.N received by the evaluator system  110 . 
     In the interest of clarity of explanation, the method of  FIG. 16  will be discussed in reference to the evaluator system  110  receiving the first query message QMSG. 001  and generating a first query response message RMSG. 001  received from the first POS system  112 A. It is understood that the method of  FIG. 14  may be applied to the generation of a plurality of query response messages RMSG. 001 -RMSG.N in response to receipt of each of a plurality of query messages QMSG. 001 -QMSG.N by one of the POS systems  112 A- 112 N. It is further understood that the method of  FIG. 16  may be modified and applied by one of ordinary skill in the art to query messages QMSG. 001 -QMSG.N that specify two or more item identifiers, e.g., one or more product identifiers PROD.ID. 001 -PROD.ID.N, one or more product type identifiers PRODT.ID. 001 -PRODT.ID.N, one or more service identifiers SERV.ID. 001 -SERV.ID.N, one or more service type identifiers SERVT.ID. 001 -SERVT.ID.N and/or one or more brand identifiers BRND.ID. 001 -BRND.N, in a search of the entity database EN.DB in harvesting relevant personally identifying information of entities, e.g., entity name NAME. 001 -NAME.N, an email address EMAIL. 001 -EMAIL.N, a cellular phone number CELL. 001 -CELL.N, account identifier ACCT. 001 -ACCT.N an insurance process identifier INS. 001 , a mobile device identifier MOB. 001 , or a government issued identifier GOV. 001 ., as described in steps  16 . 16  through  16 . 30 . 
     In step  16 . 00  the evaluator system  110  connects with the network  100  and in step  16 . 02  receives the first query message QMSG. 001  and in step  16 . 04  the evaluator system  110  extracts the second product identifier PROD.ID. 002  from the first query message QMSG. 001 . In step  16 . 06  the evaluator system  110  selects a formula FORM. 001 -FORM.N from the multivariate formula database MVF.DB associated with the first product identifier, i.e., the second multivariate formula FORM. 002 . 
     In optional step  16 . 08  the evaluator system  110  extracts the first purchasing intensity level value IVEVL. 001  from the first query message QMSG. 001 . It is understood that in alternate applications of the method of  FIG. 16  that a default intensity level value may be applied by the evaluator system  110  in evaluating the significance of a purchasing intensity value as generated in the method of  FIG. 16 . 
     In optional step  16 . 10  the evaluator system  110  extracts the optional time displacement value ΔT from the first query message QMSG. 001 , and in optional step  16 . 12  the evaluator system  110  extracts the optional distance variance value ΔD from the first query message QMSG. 001 . It is understood that in alternate applications of the method of  FIG. 16  that a default time displacement value ΔT may be applied by the evaluator system  110  in the method of  FIG. 16  rather than a time displacement value ΔT as read from the first query message QMSG. 001 . It is also understood that in alternate applications of the method of  FIG. 16  that a default distance variance value ΔD may be applied by the evaluator system  110  in the method of  FIG. 16  rather than a distance variance value ΔD as read from the first query message QMSG. 001 . 
     The evaluator system  110  initializes a second counter value CTR 2  in step  16 . 14  begins selecting counter records CREC. 001 -CREC.N in succeeding instantiations of step  16 . 16 . In step  16 . 8  the evaluator system  110  selects a single entity record EREC.CTR 2  for examination in the following steps  16 . 18  through  16 . 28 . In step  16 . 18  the evaluator system  110  determines if the first product identifier PROD.ID. 001  is associated with the selected EREC.CTR 2 . When the evaluator system  110  determines that entity record EREC.CTR 2  is associated with the first product identifier PROD.ID. 001 , the evaluator system  110  proceeds on to step  16 . 20 . In step  16 . 20  the evaluator system  110  calculates a derived distance value in view of a location value LOC. 001 -LOC.N associated with the selected entity record EREC. 001  and the first POS location PLOC. 001  associated with the first POS server  112 A in the POS database POS.DB, wherein if the derived distance value is less than or equal to distance variance value ΔD harvested from the first query message QMSG. 001 , or in an alternative less than or equal to a default distance variance value ΔD, the evaluator system  110  proceeds on to execute step  16 . 22 . 
     In step  16 . 22  the evaluator system  110  applies the multivariate formula FORM. 001 -FORM.N, i.e., the first formula FORM. 001  in the instant example, as selected in step  16 . 06  to information associated by the entity record EREC.CTR 2  as selected in the most recent instantiation of step  16 . 16  to calculate a purchasing intensity value in view of the instant selected entity record EREC.CTR 2 . The evaluator system  110  in step  16 . 24  then compares the calculated purchasing intensity value of step  16 . 22  with the first intensity level value ILVL. 001  as harvested from the first query message QMSG. 001  to determine if the instant calculated purchasing intensity value is greater than or equal to the first intensity level value ILVL. 001 . When the value comparison of step  16 . 24  indicates that the calculated purchasing intensity value is greater than or equal to the first intensity level value ILVL. 001 , the evaluator system  110  proceeds on to step  16 . 26  and to write into the first query response message RMSG. 001  one or more personally identifying information, e.g., such as an entity name NAME. 001 -NAME.N, an email address EMAIL. 001 -EMAIL.N, a cellular phone number CELL. 001 -CELL.N, an account identifier ACCT. 001 -ACCT.N, an insurance process identifier INS. 001 , a mobile device identifier MOB. 001 , or a government issued identifier GOV. 001 , as read from information associated by the selected entity record EREC.CTR 2 . Optionally or additionally, the evaluator system  110  may write additional information associated by the selected entity record EREC.CTR 2 , such as, but not limited to location data LOC. 001 -LOC.N and other associated information DATA. 001 -DATA.N. 
     The evaluator system  110  proceeds from step  16 . 26  and to step  16 . 28  to determine if the second counter value CTR 2  has reached or exceeded the maximum counter value MAX that indicates that all of the plurality of consumer records CREC. 001 -CREC.N have been processed in an instantiation of steps  16 . 16  through  16 . 26 . 
     When the evaluator system  110  determines in step  16 . 28  that the second counter value CTR 2  has not reached or exceeded the maximum value MAX, the evaluator system  110  proceeds on to step  16 . 30  and increments the second counter value CTR 2 . The evaluator system  110  proceeds from step  16 . 30  to an additional execution of step  16 . 16 . Alternatively, the evaluator system  110  proceeds from step  16 . 28  to step  16 . 32  when the evaluator system  110  determines in step  16 . 28  that the second counter value CTR 2  has reached or exceeded the maximum value MAX, wherein the evaluator system  110  proceeds from step  16 . 32  to step  16 . 34  and to perform alternate computational operations. 
     Alternatively, evaluator system  110  in step  16 . 24  may compare the calculated purchasing intensity value of step  16 . 22  with a default intensity level value as provided to or by the evaluator system  110  and to proceed from step  16 . 22  and on to step  16 . 24  if the instant calculated purchasing intensity value of step  16 . 22  is determined to be greater than or equal to the default intensity level. 
     Referring now generally to the Figures and particularly to  FIG. 17 ,  FIG. 17  is a response table  1700  of aspects of the first exemplary query response message 
     RMSG. 001  as sent from the evaluator system  110 . The first exemplary query response message RMSG. 001  includes the first POS system network address POS.ADDR- 001  as the destination address, the evaluator system network address EVAL.ADDR as the sender address, and a plurality of instances of personally identifying information EMAIL, EMAIL. 900 , CELL. 447 , NAME.N, EMAIL. 045 , CELL. 792  &amp; ACCT. 422  and a first consumer record identifier CREC.ID. 001 . It is understood that the first consumer record identifier CREC.ID. 001  may be used to access personally identifying information included in, associated with or referenced by first consumer record CREC. 001 . 
     The first exemplary query response message RMSG. 001  may further additionally or alternatively include the first product identifier PROD.ID. 001  as extracted by the evaluator system  110  in step  16 . 04  of the method of  FIG. 16 , the distance variance value ΔD as applied by the evaluator system  110  in step  16 . 20  of the method of  FIG. 16 , and the time displacement value ΔT applied by the evaluator system  110  in step  16 . 22  of the method of  FIG. 16 . 
     The first exemplary query response message RMSG. 001  may further additionally or alternatively include location data LOC, LOC. 900 , LOC. 447 , LOC. 045 , LOC. 792 , LOC. 492  &amp; LOC.N that are each individually and uniquely associated with separate instances of personally identifying information comprised within the first exemplary query response message RMSG. 001 , and/or additional information DATA, DATA. 900 , DATA. 447 , DATA. 045 , DATA. 792 , DATA. 492  &amp; DATA.N that is also individually uniquely associated with separate instances of personally identifying information comprised within the first exemplary query response message RMSG. 001 . It is noted that a 500th consumer record identifier CREC.ID. 001  may optionally be associated within the with a 500 th  entity location data LOC. 500  and/or a 500 th  consumer record information DATA. 500 . 
     Referring now generally to the Figures and particularly to  FIG. 18 ,  FIG. 18  is a flowchart of a point of sale system  112 A- 112 N visually rendering an exemplary first map image MAP.IMG. 001 , wherein the map image indicates locations selected from the consumer database records CREC. 001 -CREC.N and optionally a point of sale location PLOC. 001  associated with retail sales of one or more of items identified by a product identifier PROD.ID. 001 -PROD.ID.N, a product type identifier PRODT.ID. 001 -PRODT.ID.N, a service identifier SERVID. 001 -SERVID.N, a service type identifier SERVT.ID. 001 -SERVT.ID.N, and/or a brand identifier BRND.ID. 001 -BRND.ID.N. It is understood the invented method enables, and that the aspects of the method of  FIG. 18  may be performed by, other suitable servers and computers system known in the art, including but not limited to, the plurality of web servers  104 A- 104 N, the user device  106 , the content publisher  108 , the aggregator  109 , the evaluator system  110 , other point of sale systems  112 B- 112 N and the mapping web service server  114 , to render the first map image MAP.IMG. 001  and other suitable images of geographically related data known in the art. 
     For the purpose of clarity of illustration, the method of  FIG. 18  will be discussed in the disclosure as an instance of the first POS system  112 A generating the first map image MAP.IMG. 001  in view of the first query response message RMSG. 001 . It is understood that the method of  FIG. 18  may also be applied in whole or in part by one or more other servers  104 A- 104 N &amp;  114  and systems  112 A- 112 N,  109 ,  108  &amp;  110  in rendering information harvested from one or more entity records EREC. 001 -EREC.N, consumer records CREC. 001 -CREC.N and/or activity records AREC. 001 -AREC.N. 
     The first POS system  112 A in step  18 . 00  connects with the network  100  and receives the first query response message RMSG. 001  in step  1802 . In step  18 . 04  the first POS system  112 A launches a map application software MAP.SW and in step  18 . 06  renders a first map image MAP.IMG. 001  via a first POS system display screen  112 A.A. It is understood that the map application software MAP.SW may in step  18 . 06  rely upon and render information received via the network  100 , to include rendering data requested from the mapping system  114  and received by the map application software MAP.SW. In step  18 . 08  the first POS system  112 A selects location data LOC, LOC. 900 , LOC. 447 , LOC. 045 , LOC. 792 , LOC. 492  &amp; LOC.N from the first query response message RMSG. 001 . In step  18 . 10  the first POS system  112 A renders visual avatars AVT. 001 -AVT.N, wherein each avatar AVT. 001 -AVT.N is separately representative of one particular location data LOC, LOC. 900 , LOC. 447 , LOC. 045 , LOC. 792 , LOC. 492  &amp; LOC.N. In step  18 . 12  the first POS system  112 A determines whether to highlight one or more avatars AVT. 001 -ACT.N, wherein each highlight expresses information read from a data DATA. 001 -DATA.N of the first query response message RMSG. 001  that is separately associated with a particular location data LOC, LOC. 900 , LOC. 447 , LOC. 045 , LOC. 792 , LOC. 492  &amp; LOC.N that a selected AVT. 001 -AVT.N shares an association. 
     In step  18 . 14  the first POS system  112 A optionally renders visual highlights of one or more avatar AVT. 001 -AVT.N as representing information interpreted from a data DATA, 001  DATA. 900 , DATA. 447 , DATA. 045 , DATA. 792 , DATA. 492  &amp; DATA.N associated with a same location data LOC. 001 -LOC.N as the highlighted avatar AVT. 001 -AVT.N. 
     The first POS system  112 A proceeds from either step  18 . 12  or step  18 . 14  to step  18 . 16  as directed by either a POS system user or the POS system software POS. SW, wherein the first POS system  112 A in step  18 . 16  whether to proceed to an additional execution of step  18 . 02  and continue rendering and visually modifying the avatars AVT. 001 -AVT.N. In the alternative, the first POS system  112 A may proceed from step  18 . 16  to step  18 . 18  as directed by either the POS system user or the POS system software POS.SW and therefrom to perform alternate computational operations. 
     Referring now generally to the Figures and particularly to  FIG. 19 ,  FIG. 19  presents the first map image MAP.IMG. 001  visually rendered by the first POS screen  112 A.A and including a plurality of representative avatars AVT. 001 -AVT.N &amp; AVT.POSA. The first POS avatar AVT.POSA represents the geographic position represented by the first POS location data PLOC. 001 . The circular shape of the first POS avatar AVT.POSA indicates the nature of the first POS avatar AVT.POSA as representing the geographic location of the first geographic point of sales location. The three triangular shape avatars AVT. 001 , AVT. 045  &amp; AVT. 900  respectively represent entity location data LOC. 001 , LOC. 045  &amp; LOC. 900  stored in associated consumer records CREC. 001 -CREC.N, wherein the triangular avatar shapes indicate an association with an identified email address EMAI. 001 , EMAIL. 045  &amp; EMAIL. 900 . A diamond shape of the 447 th  avatar AVT. 447  indicates the nature of the 447 th  avatar AVT. 447  as representing a geographic location of an entity associated with a 447 th  cellular phone number CELL. 447 . A pentagonal shape of the N th  avatar AVT.N indicates the nature of the N th  avatar AVT.N as representing a geographic location of an entity associated with a legal entity name NAME.N. A hexagonal shape of the 422 nd  avatar AVT. 422  indicates the nature of the 422 nd  avatar AVT. 422  as representing a geographic location of an entity associated with a 422 nd  account identifier ACC. 422 . A relieved orthogonal shape of the 500 th  avatar AVT. 500  indicates the nature of the 500 th  avatar AVT. 500  as representing a geographic location of an entity associated with a 500 th  consumer record CREC. 500 . 
     It is understood that additional information associated by one or more identity records EREC. 001 -EREC.N and the location data represented in the first map image MAP.IMG. 001  may optionally be visually indicated by addition to, or association with, the avatars AVT. 001 -AVT.N of coloring, shading, sizing, and other suitable visual indicators known in the art. 
     It is further understood the invented method enables other suitable servers and computers system known in the art, including but not limited to, the plurality of web servers  104 A- 104 N, the user device  106 , the content publisher  108 , the aggregator  109 , the evaluator system  110 , other point of sale systems  112 B- 112 N and the mapping web service server  114 , to partially or completely render the first map image MAP.IMG. 001  of  FIG. 19  and other suitable images of geographically related data known in the art. 
     Referring now generally to the Figures and particularly to  FIG. 20 ,  FIG. 20  is a target message table  2000  of aspects of a first targeted marketing message TMSG. 001  as sent from the evaluator system  110  and addressed to a first email address EMAIL. 001  selected from a first consumer record CREC. 001  of the consumer database CON.DB. The first email address EMAIL. 001  is entered as a destination address and the evaluation system email address EVAL.ADDR is entered as a sender address. The first target message TMSG. 001  further includes the product identifier PROD.ID. 001 , a first product information payload PROD.INFO. 001 , a first product pricing data PRICING. 001 , and the first POS system identifier. 
       FIG. 21  is a flowchart of the evaluator system  110  in scoring a relevance factor of universal resource identifiers URI. 001 -URIN in relation to specific product models PROD. 001 -PROD.N, product types PRODT. 001 -PRODT.N, services SERV. 001 -SERVT.N, service types SERVT. 001 -SERV.N and brands BRND. 001 -BRND.N, and in view of content accessible via a particular universal resource locator URI. 001 -URI.N. 
     In step  21 . 00  the evaluator system  110  connects with the network  100  and initializes the URI database URI.DB. The evaluator system  110  populates the URI.DB with URI records URI.REC. 001 -URI.REC.N in step  21 . 02  with separate universal resource locator identifiers URLID. 001 -URLID.N, to include domain names of the World Wide Web, Universal Resource Locators of the World Wide Web, and Internet Protocol Addresses of the Internet  102 . The evaluator system  110  counts the quantity of URI records URI.REC. 001 -URI.REC.N in step  21 . 06  and sets a maximum URI database record count value VAL.MAX to be equal to that quantity of URI records URIREC. 001 -URIREC.N. 
     The evaluator system  110  initializes a third loop counter CTR 3  in step  21 . 08  and proceeds to score an informational resource accessible via the network  100  and referenced in a selected URI record URI.REC.CTR 3  in step  21 . 10 , wherein the scoring is performed according to the URI scoring algorithm ALGO. 001  and the scoring information of the URI scoring database USCR.DB. The resultant of the scoring of the selected URI record URI.REC.CTR 3  of step  21 . 10  is recorded in the URI database URI.DB in step  21 . 12 . 
     The evaluator system  110  determines in step  21 . 14  whether one or more new universal resource identifiers have been received via the network  100 , and if so, proceeds to update the URI database URI.DB with new URI database records URI. 001 -URI.N in step  21 . 16 . The evaluator system  110  resets the maximum URI database record count value VAL.MAX in step  21 . 18  in view of the additional count of universal resource locators accepted in step  21 . 14  and thereupon proceeds on to step  21 . 20 . 
     In the alternative outcome to step  21 . 14 , when the evaluator system  110  determines in step  21 . 14  that no new universal resource indicators are to be accepted, the evaluator system  110  proceeds on to step  21 . 20 . In step  21 . 20  the evaluator system  110  determines whether the current value of the third counter CTR 3  is greater than or equal to the maximum URI database record count value VAL.MAX and proceeds on to step  21 . 22  if the current value of the third counter CTR 3  is less than the maximum URI database record count value VAL.MAX. In step  21 . 22  the evaluator system  110  increments the value of the third counter CTR 3 . In the alternative, when the evaluator system  110  determines in step  21 . 20  that the current value of the third counter CTR 3  is greater than or equal to the maximum URI database record count value VAL.MAX, the evaluator system  110  proceeds from step  21 . 20  to an additional execution of step  21 . 08 . 
       FIG. 22  is URI score record table  2200  of selected contents of an exemplary first URI score record USCR.REC. 001  as applied by the method of  FIG. 21 . A plurality of URI score records USCR.REC. 001 -USCR.REC.N, wherein each URI score records USCR.REC. 001 -USCR.REC.N includes a unique URI score record identifier USCR.ID. 001 -USCR.ID.N, a single item identifier, e.g., one of the first product identifier PROD.ID. 001 , the second product type identifier PRODT. 002 , the third service identifier SERV.ID. 003 , the fourth service type identifier SERVT. 004 , the fifth brand BRND. 005 , and the Nth product identifier PROD.ID.N. 
     Each URI score record USCR.REC. 001 -USCR.REC.N further includes one or more character strings STR. 001  and image files IMG. 001 -IMG.N. The evaluator system  110  attempts to sequentially match character strings STR. 001 -STR.N and image files IMG. 001 -IMG.N of each URI score record USCR.REC. 001 -USCR.REC.N with information accessed at an address of URI identifier URI.ID. 001 -URI.ID.N of a URI record URI.REC. 001 -URI.N, and when a match is found between a URI identifier and one or more character strings STR. 001  and image files IMG. 001 -IMG.N of a specific URI score record USCR.REC. 001 , the item identifier associated with the matching URI score record USCR.REC. 001  is written into the URI record URI.REC. 001 -URIREC.N, whereby positive finding of a relatedness of the URI identifier URLID. 001 -URLID.N is recorded in the URI database URLDB. 
       FIG. 23  is a URI database table  2300  of selected contents of a plurality of URI score records URI.REC. 001 -URI.SCR.N. A first URI record UREC. 001  includes a first URI record identifier UREC.ID. 001 , the first URI identifier URI.ID. 001 , the first product identifier and the second product type identifier PRODT.ID. 002 . The presence of the first product identifier PROD.ID. 001  in the first URI record UREC. 001  will cause the first multivariate formula FORM. 001  when applied in step  2 . 10 , step  10 . 06 , step  12 . 10 , or step  16 . 22  to an activity record AREC. 001 -AREC.N that includes a recordation of a recent visit to the information accessible at the first URI identifier UREC.ID. 001  to increase the resulting purchasing intensity score. 
     The presence of the second product type identifier PRODT.ID. 002  in the first URI record UREC. 001  will also cause the second multivariate formula FORM. 002  when applied in step  2 . 10 , step  10 . 06 , step  12 . 10 , or step  16 . 22  to an activity record AREC. 001 -AREC.N that includes a recordation of a recent visit to the information accessible at the second URI identifier UREC.ID. 002  to increase the resulting purchasing intensity score. 
     A second URI record UREC. 002  includes a second URI record identifier UREC.ID. 002 , a second URI identifier URI.ID. 002  and the third service identifier SERV.ID. 003 . The presence of the third service identifier SERV.ID. 003  in the second URI record UREC. 001  will also cause the third multivariate formula FORM. 003  when applied in step  2 . 10 , step  10 . 06 , step  12 . 10 , or step  16 . 22  to an activity record AREC. 001 -AREC.N that includes a recordation of a recent visit to the information accessible at the second URI identifier UREC.ID. 002  to increase the resulting purchasing intensity score. 
     A third URI record UREC. 003  includes a third URI record identifier UREC.ID. 003  and a null value for matches with item identifiers. 
     A fourth URI record UREC. 004  includes a fourth URI record identifier UREC.ID. 004 , a fourth URI identifier URI.ID. 004  and a 625 th  service type identifier SERVT.ID. 625 . 
     A fifth URI record UREC. 005  includes a fifth URI record identifier UREC.ID. 005 , a fifth URI identifier URI.ID. 005  and the fifth brand identifier BRND.ID. 005 . The presence of the fifth brand identifier BRND.ID. 005  in the fifth URI record UREC. 005  will also cause the fifth multivariate formula FORM. 005  when applied in step  2 . 10 , step  10 . 06 , step  12 . 10 , or step  16 . 22  to an activity record AREC. 001 -AREC.N that includes a recordation of a recent visit to the information accessible at the fifth URI identifier UREC.ID. 005  to increase the resulting purchasing intensity score. 
     Referring now generally to the Figures and particularly to  FIG. 24 ,  FIG. 24  is a block diagram of the exemplary first web server  104 A. It is understood that one or more other webservers  104 B- 104 N may include some or all of the aspects and elements of the exemplary first web server  104 A as disclosed herein. 
     The first web server system  104 A includes a WS central processing unit  104 A.A and a WS system memory  104 B that are bi-directionally communicatively coupled by a WS internal communications bus  104 C. The WS internal communications bus  104 C additionally bi-directionally couples the WS central processing unit  104 A.A and the WS system memory  104 B with a WS network interface  104 D, a WS human operator input module  104 E, a display module  104 F that includes the WS display screen  104 G and a 
     WS telephony interface  104 H. The WS human operator input module  104 E enables an operator to input commands and data to the WS central processing unit  104 A.A and the WS system memory  104 B via the WS internal communications bus  104 C. The WS display module  104 F enables visual rendering of information at the WS display screen  104 A.A as directed by the WS central processing unit  104 A.A. The WS network interface  104 D bi-directionally communicatively couples the WS central processing unit  104 A.A with the WS network  100 . 
     The WS system memory  104 B stores a WS operating system WS.OP.SYS, a WS system software WS.SYS.SW, and a WS database management system WS.DBMS. The WS system software WS.SYS.SW enables the first web server system  104 A to perform and provide all aspects of the invented method relevant to operations of the first web serve  104 A, to include web page publishing and hash generation. 
     The WS database management system WS.DBMS stores, updates and manages digitized information, databases and database records as record to implement the aspects of the invention as disclosed herein and required of the first web server  104 A. The WS database management system WS.DBMS may optionally, alternatively or additionally be or comprise a relational database management system, such as an IBM DB2 Universal Database™ server marketed by IBM Corporation of Armonk, N.Y., or other suitable relational database management systems known in the art. It is further understood that one or more of the databases EN.DB, ACT.DB, CON.DB, URLDB, USCR.DB &amp; POS.DB optionally, alternatively or additionally be or comprise an object-oriented database management system, such as an Object Oriented DBMS as marketed by Objectivity, Inc. of San Jose, Calif., or other suitable object-oriented database management system known in the art. 
     A web page publishing software WS.PUB.SW enables the first web server  104 A to generate and transmit information suitable for rendering by the user web browser  106 B. A WS hash derivation software WS.HASH.SW enables generation of the first hash HASH. 001  and additional hashes HASH. 002 -HASH.N of personally identifying information. It is understood that the WS hash derivation software WS.HASH.SW may optionally or alternatively be in conformance with a commonly available hashing software, such as, but not limited to, a hashing software that applies the MD5 algorithm as designed by Ronald Rivest of the Computer Science and Artificial intelligence Laboratory of the Massachusetts Institute of Technology of Cambridge, MA or other suitable hashing or cryptographic software or algorithms known in the art. 
     It is understood that in various alternate preferred embodiments of the invented method that one or more of the databases and algorithms applied therein may be alternatively or additionally stored outside of the first web server system  104 A in one or more data storage systems (not shown) that are accessible to the first web server system  104 A via the network  100  and/or an alternate electronic communications network (not shown). 
     The first web server  104 A further comprises a plurality of software programs stored in system memory  104 B, to include a WS web browser BROWSER.SW, a WS email client WS.EMAIL.SW, a WS texting client WS.TEXT.SW, and a WS network communication software WS.NET.SW. The WS email client WS.EMAIL.SW enables the first web server  104 A to communicate by email transmissions with servers and systems  104 B- 114  of the network  100  via the WS telephony interface  104 H and/or the WS network interface  104 D. The WS texting client WS.TEXT.SW enables the first web server  104 A to communicate by text messaging with servers and systems  104 B- 114  of the network  100  via the WS network interface  104 D and/or the WS telephony network interface  104 H. The WS network communication software WS.NET.SW enables the first web server  104 A to communicate by other suitable messaging protocols known in the art with servers and systems  104 A- 114  of the network  100  via the telephony interface  104 H and/or the network interface  104 D. 
     Referring now generally to the Figures and particularly to  FIG. 25 ,  FIG. 25  is a block diagram of the user device  106 . 
     The user device  106  includes a UD central processing unit  106 C and a UD system memory  106 D that are bi-directionally communicatively coupled by a UD internal communications bus  106 E. The UD internal communications bus  106 E additionally bi-directionally couples the UD central processing unit  106 C and the UD system memory  106 D with a UD network interface  106 F, a UD human operator input module  106 G, a display module  106 H that includes a UD display screen  1061 , and a UD telephony interface  106 J. The UD human operator input module  106 G enables an operator to input commands and data to the UD central processing unit  106 C and the UD system memory  106 D via the UD internal communications bus  106 E. The UD display module  106 H enables visual rendering of information at the UD display screen  1061  as directed by the UD central processing unit  106 C. The UD network interface  106 F bi-directionally communicatively couples the UD central processing unit  106 C with the UD network  100 . 
     The UD system memory  106 D stores a UD operating system UD.OP.SYS, a UD system software UD. SYS. SW, and a UD database management system UD.DBMS. The UD system software UD.SYS.SW enables the user device  106  to perform and provide all aspects of the invented method relevant to operations of the user device  106 , to include web browsing and electronic messaging. 
     The UD database management system UD.DBMS stores, updates and manages digitized information, values, counters, databases and database records as record to implement the aspects of the invention as disclosed herein and required of the user device  106 . The UD database management system UD.DBMS may optionally, alternatively or additionally be or comprise a relational database management system, such as an IBM DB2 Universal Database™ server marketed by IBM Corporation of Armonk, N.Y., or other suitable relational database management system known in the art. It is further understood that one or more of the databases EN.DB, ACT.DB, CON.DB, URI.DB, USCR.DB &amp; POS.DB optionally, alternatively or additionally be or comprise an object-oriented database management system, such as an Object Oriented DBMS as marketed by Objectivity, Inc. of San Jose, Calif., or other suitable object-oriented database management system known in the art. 
     It is understood that in various alternate preferred embodiments of the invented method that one or more of the databases and algorithms applied therein may be alternatively or additionally stored outside of the user device  106  in one or more data storage systems (not shown) that are accessible to the user device  106  via the network  100  and/or an alternate electronic communications network (not shown). 
     The user device  106  further comprises a plurality of software programs stored in the UD system memory  106 D, to include the web browser  106 B that may include the cookie  106 A, a UD email client EMAIL. SW, a UD texting client TEXT. SW, and a UD network communication software UD.NET.SW. The user web browser  106 B enables the user device  106  to retrieve, present, render and traverse information resources on the World Wide Web via and/or within the network  100 . It is understood that the user web browser  106 B may be or comprise a SAFARI™ web browser provided by APPLE of Cupertino, Calif., or other suitable web browser known in the art. 
     The UD email client UD.EMAIL.SW enables the user device  106  to communicate by email transmissions with servers and systems  104 A- 106  of the network  100  via the UD telephony interface  1061  and/or the UD network interface  106 F. The UD texting client UD.TEXT.SW enables the user device  106  to communicate by text messaging with servers and systems  104 A- 106  of the network  100  via the UD network interface  106 F and/or the UD telephony network interface  1061 . The UD network communication software UD.NET.SW enables the user device  106  to communicate by other suitable messaging protocols known in the art with servers and systems  106 - 106  of the network  100  via the UD telephony interface  1061  and/or the UD network interface  106 F. 
     Referring now generally to the Figures and particularly to  FIG. 26 ,  FIG. 26  is a block diagram of the content publisher  108 . 
     The content publisher  108  includes a PUB central processing unit  108 A and a PUB system memory  108 B that are bi-directionally communicatively coupled by a PUB internal communications bus  108 C. The PUB internal communications bus  108 C additionally bi-directionally couples the PUB central processing unit  108 A and the PUB system memory  108 B with a PUB network interface  108 D, a PUB human operator input module  108 E, a display module  108 F that includes a PUB display screen  108 G, and a 
     PUB telephony interface  108 H. The PUB human operator input module  108 E enables an operator to input commands and data to the PUB central processing unit  108 A and the PUB system memory  108 B via the PUB internal communications bus  108 C. The PUB display module  108 F enables visual rendering of information at the PUB display screen  108 A.A as directed by the PUB central processing unit  108 A. The PUB network interface  108 D bi-directionally communicatively couples the PUB central processing unit  108 A with the PUB network  100 . 
     The PUB system memory  108 B stores a PUB operating system PUB.OP.SYS, a PUB system software PUB.SYS.SW, and a PUB database management system PUB.DBMS. The PUB system software PUB.SYS.SW enables the content publisher  108  to perform and provide all aspects of the invented method relevant to operations of the content publisher  108 , to include web page publishing and hash generation. 
     The PUB database management system PUB.DBMS stores, updates and manages digitized information, values, variables, counters, databases and database records as record to implement the aspects of the invention as disclosed herein and required of the content publisher  108 . The PUB database management system PUB.DBMS may optionally, alternatively or additionally be or comprise a relational database management system, such as an IBM DB2 Universal Database™ server marketed by IBM Corporation of Armonk, N.Y., or other suitable relational database management system known in the art. It is further understood that one or more of the databases EN.DB, ACT.DB, CON.DB, URI.DB, USCR.DB &amp; POS.DB optionally, alternatively or additionally be or comprise an object-oriented database management system, such as an Object Oriented DBMS as marketed by Objectivity, Inc. of San Jose, Calif., or other suitable object-oriented database management system known in the art. A PUB web page publishing software PUB.AG.SW enables the content publisher  108  to generate and transmit information suitable for rendering by the user web browser  106 B. A PUB hash derivation software PUB.HASH.SW enables generation of the first hash HASH. 001  and additional hashes HASH. 002 -HASH.N of personally identifying information. It is understood that the PUB hash derivation software PUB.HASH.SW may optionally or alternatively be in conformance with a commonly available hashing software, such as, but not limited to, a hashing software that applies the MD5 algorithm as designed by Ronald Rivest of the Computer Science and Artificial Intelligence Laboratory of the Massachusetts Institute of Technology of Cambridge, Mass., or other suitable hashing or cryptographic software or algorithm known in the art. 
     It is understood that in various alternate preferred embodiments of the invented method that one or more of the databases and algorithms applied therein may be alternatively or additionally stored outside of the content publisher  108  in one or more data storage systems (not shown) that are accessible to the content publisher  108  via the network  100  and/or an alternate electronic communications network (not shown). 
     The content publisher  108  further comprises a plurality of software programs stored in the PUB system memory  108 B, to include a PUB web browser PUB.BROWSER.SW, a PUB email client EMAIL.SW, a PUB texting client TEXT.SW, and a PUB network communication software PUB.NET.SW. The PUB web browser PUB.BROWSER.SW enables the aggregator  109  to retrieve, present, render and traverse information resources on the World Wide Web via and/or within the network  100 . It is understood that the PUB web browser PUB.BROWSER.SW may be or comprise a SAFARI™ web browser provided by APPLE of Cupertino, Calif., or other suitable web browser known in the art. 
     The PUB email client PUB.EMAIL.SW enables the content publisher  108  to communicate by email transmissions with servers and systems  104 A- 114  of the network  100  via the PUB telephony interface  108 H and/or the PUB network interface  108 D. The PUB texting client PUB.TEXT.SW enables the content publisher  108  to communicate by text messaging with servers and systems  104 A- 114  of the network  100  via the PUB network interface  108 D and/or the PUB telephony network interface  108 H. The PUB network communication software PUB.NET.SW enables the content publisher  108  to communicate by other suitable messaging protocols known in the art with servers and systems  108 - 114  of the network  100  via the PUB telephony interface  108 H and/or the PUB network interface  108 D. 
     Referring now generally to the Figures and particularly to  FIG. 27 ,  FIG. 27  is a block diagram of the aggregator  109 . 
     The aggregator  109  includes an AG central processing unit  109 A and an AG system memory  109 B that are bi-directionally communicatively coupled by an AG internal communications bus  109 C. The AG internal communications bus  109 C additionally bi-directionally couples the AG central processing unit  109 A and the AG system memory  109 B with an AG network interface  109 D, an AG human operator input module  109 E, a display module  109 F that includes an AG display screen  109 G, and an AG telephony interface  109 H. The AG human operator input module  109 E enables an operator to input commands and data to the AG central processing unit  109 A and the AG system memory  109 B via the AG internal communications bus  109 C. The AG display module  109 F enables visual rendering of information at the AG display screen  109 A.A as directed by the AG central processing unit  109 A. The AG network interface  109 D bi-directionally communicatively couples the AG central processing unit  109 A with the AG network  100 . 
     The AG system memory  109 B stores an AG operating system AG.OP.SYS, an AG system software AG.SYS.SW, and an AG database management system AG.DBMS. The AG system software AG.SYS.SW enables the aggregator  109  to perform and provide all aspects of the invented method relevant to operations of the aggregator  109 , to include web page publishing and hash generation. It is understood that the AG hash derivation software AG.HASH.SW may optionally or alternatively be in conformance with a commonly available hashing software, such as, but not limited to, a hashing software that applies the MD5 algorithm as designed by Ronald Rivest of the Computer Science and Artificial Intelligence Laboratory of the Massachusetts Institute of TechnologyOf Cambridge, Mass., or other suitable hashing or cryptographic software or algorithm known in the art. 
     The AG database management system AG.DBMS stores, updates and manages digitized information, variables, values, counters, databases and database records as record to implement the aspects of the invention as disclosed herein and required of the aggregator  109 . The AG database management system AG.DBMS may optionally, alternatively or additionally be or comprise a relational database management system, such as an IBM DB2 Universal Database™ server marketed by IBM Corporation of Armonk, N.Y., or other suitable relational database management system known in the art. It is further understood that one or more of the databases EN.DB, ACT.DB, CON.DB, URI.DB, USCR.DB &amp; POS.DB optionally, alternatively or additionally be or comprise an object-oriented database management system, such as an Object Oriented DBMS as marketed by Objectivity, Inc. of San Jose, Calif., or other suitable object-oriented database management system known in the art. 
     An AG web page publishing software AG.PUB.SW enables the aggregator  109  to generate and transmit information suitable for rendering by the user web browser  10 B. A hash derivation software AG.HASH. SW enables generation of the first hash HASH. 001  and additional hashes HASH. 002 -HASH.N of personally identifying information. It is understood that the hash derivation software AG.HASH. SW may optionally or alternatively be in conformance with a commonly available hashing software, such as, but not limited to, a hashing software that applies the MD5 algorithm as designed by Ronald Rivest of the Computer Science and Artificial Intelligence Laboratory of the Massachusetts Institute of Technology of Cambridge, Mass., or other suitable hashing or cryptographic software or algorithm known in the art. 
     It is understood that in various alternate preferred embodiments of the invented method that one or more of the databases and algorithms applied therein may be alternatively or additionally stored outside of the aggregator  109  in one or more data storage systems (not shown) that are accessible to the aggregator  109  via the network  100  and/or an alternate electronic communications network (not shown). 
     The aggregator  109  further comprises a plurality of software programs stored in the AG system memory  109 B, to include an AG web browser AG.BROWSER.SW, an AG email client EMAIL. SW, an AG texting client TEXT. SW, and an AG network communication software AG.NET.SW. The AG web browser AG.BROWSER.SW enables the aggregator  109  to retrieve, present, render and traverse information resources on the World Wide Web via and/or within the network  100 , and may be a SAFARI™ web browser provided by APPLE of Cupertino, Calif., or other suitable web browser known in the art. 
     The AG email client AG.EMAIL.SW enables the aggregator  109  to communicate by email transmissions with servers and systems  104 A- 114  of the network  100  via the AG telephony interface  109 H and/or the AG network interface  109 D. The AG texting client AG.TEXT.SW enables the aggregator  109  to communicate by text messaging with servers and systems  104 A- 114  of the network  100  via the AG network interface  109 D and/or the AG telephony network interface  109 H. The AG network communication software AG.NET.SW enables the aggregator  109  to communicate by other suitable messaging protocols known in the art with servers and systems  109 - 114  of the network  100  via the AG telephony interface  109 H and/or the AG network interface  109 D. 
     Referring now generally to the Figures and particularly to  FIG. 28 ,  FIG. 28  is a block diagram of the exemplary first POS system  112 A. It is understood that one or more other POS systems  112 B- 112 N may include some or all of the aspects and elements of the exemplary first POS system  112 A as disclosed herein. 
     The first POS system  112 A includes a POS central processing unit  112 A.A and a POS system memory  112 B that are bi-directionally communicatively coupled by a POS internal communications bus  112 C. The POS internal communications bus  112 C additionally bi-directionally couples the POS central processing unit  112 A.A and the POS system memory  112 B with a POS network interface  112 D, a POS human operator input module  112 E, a display module  112 F that includes the POS display screen  112 G and a POS telephony interface  112 H. The POS human operator input module  112 E enables an operator to input commands and data to the POS central processing unit  112 A.A and the POS system memory  112 B via the POS internal communications bus  112 C. The POS display module  112 F enables visual rendering of information at the POS display screen  112 A.A as directed by the POS central processing unit  112 A.A. The POS network interface  112 D bi-directionally communicatively couples the POS central processing unit  112 A.A with the POS network  100 . 
     The POS system memory  112 B stores a POS operating system POS.OP.SYS, a POS system software POS.SYS.SW, and a POS database management system POS.DBMS. The POS system software POS.SYS.SW enables the first POS system  112 A to perform and provide all relevant aspects of the invented method, to include web page publishing and hash generation. 
     The POS database management system POS.DBMS stores, updates and manages digitized information, values, variable, counters, databases and database records as record to implement the aspects of the invention as disclosed herein and required of the first POS system  112 A. The POS database management system POS.DBMS may optionally, alternatively or additionally be or comprise a relational database management system, such as an IBM DB2 Universal Database™ server marketed by IBM Corporation of Armonk, N.Y., or other suitable relational database management system known in the art. It is further understood that one or more of the databases EN.DB, ACT.DB, CON.DB, URI.DB, USCR.DB &amp; POS.DB optionally, alternatively or additionally be or comprise an object-oriented database management system, such as an Object Oriented DBMS as marketed by Objectivity, Inc. of San Jose, Calif., or other suitable object-oriented database management system known in the art. 
     A web page publishing software POS.PUB.SW enables the first POS system  112 A to generate and transmit information suitable for rendering by the user web browser  106 B. A POS hash derivation software POS.HASH.SW enables generation of the first hash HASH. 001  and additional hashes HASH. 002 -HASH.N of personally identifying information. It is understood that the POS hash derivation software POS.HASH.SW may optionally or alternatively be in conformance with a commonly available hashing software, such as, but not limited to, a hashing software that applies the MD5 algorithm as designed by Ronald Rivest of the Computer Science and Artificial Intelligence Laboratory of the Massachusetts Institute of Technology of Cambridge, Mass., or other suitable hashing or cryptographic software or algorithm known in the art. 
     It is understood that in various alternate preferred embodiments of the invented method that one or more of the databases and algorithms applied therein may be alternatively or additionally stored outside of the first POS system  112 A in one or more data storage systems (not shown) that are accessible to the first POS system  112 A via the network  100  and/or an alternate electronic communications network (not shown). 
     The first the POS A further comprises a plurality of software programs stored in system memory  112 B, to include a POS web browser BROWSER.SW, a POS email client EMAIL.SW, a POS texting client TEXT.SW, and a POS network communication software POS.NET.SW. The POS email client EMAIL.SW enables the first POS system  112 A to communicate by email transmissions with servers and systems  112 A- 114  of the network  100  via the POS telephony interface  112 H and/or the POS network interface  112 D. The POS texting client POS.TEXT.SW enables the first POS system  112 A to communicate by text messaging with servers and systems  112 A- 114  of the network  100  via the POS network interface  112 D and/or the POS telephony network interface  112 H. The POS network communication software POS.NET.SW enables the first POS system  112 A to communicate by other suitable messaging protocols known in the art with servers and systems  112 - 114  of the network  100  via the telephony interface  112 H and/or the POS network interface  112 D. 
     Referring now generally to the Figures and particularly to  FIG. 29 ,  FIG. 29  is a block diagram of the mapping system  114 . 
     The mapping system  114  includes an MS central processing unit  114 A and an MS system memory  114 B that are bi-directionally communicatively coupled by an MS internal communications bus  114 C. The MS internal communications bus  114 C additionally bi-directionally couples the MS central processing unit  114 A and the MS system memory  114 B with an MS network interface  114 D, an MS human operator input module  114 E, a display module  114 F that includes an MS display screen  114 G, and an MS telephony interface  114 H. The MS human operator input module  114 E enables an operator to input commands and data to the MS central processing unit  114 A and the MS system memory  114 B via the MS internal communications bus  114 C. The MS display module  114 F enables visual rendering of information at the MS display screen  114 G as directed by the MS central processing unit  114 A. The MS network interface  114 D bi-directionally communicatively couples the MS central processing unit  114 A with the MS network  100 . 
     The MS system memory  114 B stores an MS operating system MS.OP.SYS, an MS system software MS.SYS.SW, and an MS database management system MS.DBMS. The MS system software MS.SYS.SW enables the mapping system  114  to perform and provide all aspects of the invented method relevant to operations of the mapping system  114 , to include web page publishing and hash generation. 
     The MS database management system MS.DBMS stores, updates and manages digitized information, values, counters, databases and database records as record to implement the aspects of the invention as disclosed herein and required of the mapping system  114 . The MS database management system MS.DBMS may optionally, alternatively or additionally be or comprise a relational database management system, such as an IBM DB2 Universal Database™ server marketed by IBM Corporation of Armonk, N.Y., or other suitable relational database management system known in the art. It is further understood that one or more of the databases EN.DB, ACT.DB, CON.DB, URI.DB, USCR.DB &amp; POS.DB optionally, alternatively or additionally be or comprise an object-oriented database management system, such as an Object Oriented DBMS as marketed by Objectivity, Inc. of San Jose, Calif., or other suitable object-oriented database management system known in the art. 
     An MS web page publishing software MS.PUB.SW enables the mapping system  114  to generate and transmit information suitable for rendering by the user web browser  106 B. An MS hash derivation software MS.HASH.SW enables generation of the first hash HASH. 001  and additional hashes HASH. 002 -HASH.N of personally identifying information. It is understood that the MS hash derivation software MS.HASH.SW may optionally or alternatively be in conformance with a commonly available hashing software, such as, but not limited to, a hashing software that applies the MD5 algorithm as designed by Ronald Rivest of the Computer Science and Artificial Intelligence Laboratory of the Massachusetts Institute of Technology of Cambridge, Mass., or other suitable hashing or cryptographic software or algorithm known in the art. 
     It is understood that in various alternate preferred embodiments of the invented method that one or more of the databases and algorithms applied therein may be alternatively or additionally stored outside of the mapping system  114  in one or more data storage systems (not shown) that are accessible to the mapping system  114  via the network  100  and/or an alternate electronic communications network (not shown). 
     The mapping system  114  further comprises a plurality of software programs stored in the MS system memory  114 B, to include an MS web browser 
     MS.BROWSER.SW, an MS email client MS.EMAIL.SW, an MS texting client MS.TEXT.SW, and an MS network communication software MS.NET.SW. The MS web browser MS.BROWSER.SW enables the mapping system  114  to retrieve, present, render and traverse information resources on the World Wide Web via and/or within the network  100 . It is understood that the MS web browser MS.BROWSER.SW may be or comprise a SAFARI™ web browser provided by APPLE of Cupertino, Calif., or other suitable web browser known in the art. 
     The MS email client MS.EMAIL.SW enables the mapping system  114  to communicate by email transmissions with servers and systems  104 A- 114  of the network  100  via the MS telephony interface  114 H and/or the MS network interface  114 D. The 
     MS texting client MS.TEXT.SW enables the mapping system  114  to communicate by text messaging with servers and systems  104 A- 114  of the network  100  via the MS network interface  114 D and/or the MS telephony network interface  114 H. The MS network communication software MS.NET.SW enables the mapping system  114  to communicate by other suitable messaging protocols known in the art with servers and systems  114 - 114  of the network  100  via the MS telephony interface  114 H and/or the MS network interface  114 D. 
     The foregoing description of the embodiments of the invention has been presented for the purpose of illustration; it is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Persons skilled in the relevant art can appreciate that many modifications and variations are possible in light of the above disclosure. 
     Some portions of this description describe the embodiments of the invention in terms of algorithms and symbolic representations of operations on information. These algorithmic descriptions and representations are commonly used by those skilled in the data processing arts to convey the substance of their work effectively to others skilled in the art. These operations, while described functionally, computationally, or logically, are understood to be implemented by computer programs or equivalent electrical circuits, microcode, or the like. Furthermore, it has also proven convenient at times, to refer to these arrangements of operations as modules without loss of generality. The described operations and their associated modules may be embodied in software, firmware, hardware, or any combinations thereof. 
     Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product comprising a non-transitory computer-readable medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described. 
     Embodiments of the invention may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, and/or it may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a non-transitory, tangible computer-readable storage medium, or any type of media suitable for storing electronic instructions, which may be coupled to a computer system bus. Furthermore, any computing systems referred to in the specification may include a single processor or may be architectures employing multiple processor designs for increased computing capability. 
     Embodiments of the invention may also relate to a product that is produced by a computing process described herein. Such a product may comprise information resulting from a computing process, where the information is stored on a non-transitory, tangible, computer readable storage medium and may include any embodiment of a computer program product or other data combination described herein. 
     Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based herein. Accordingly, the disclosure of the embodiments of the invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.