U.S. patent application number 16/101272 was filed with the patent office on 2020-09-03 for method and system for combining offline and online identities with associated purchasing intention indicators in view of a geogr.
This patent application is currently assigned to SUMMIT RESOURCE, LLC. The applicant listed for this patent is SUMMIT RESOURCES, LLC. Invention is credited to Natalie M. BORN, William Dale BROEN, Ryan Andrew FOSS, Grigoriy S. GEODAKYAN, Jason HARDY, JONATHAN LUCENAY, Yong-jong Shawn YEN.
Application Number | 20200279278 16/101272 |
Document ID | / |
Family ID | 1000004827868 |
Filed Date | 2020-09-03 |
![](/patent/app/20200279278/US20200279278A1-20200903-D00000.png)
![](/patent/app/20200279278/US20200279278A1-20200903-D00001.png)
![](/patent/app/20200279278/US20200279278A1-20200903-D00002.png)
![](/patent/app/20200279278/US20200279278A1-20200903-D00003.png)
![](/patent/app/20200279278/US20200279278A1-20200903-D00004.png)
![](/patent/app/20200279278/US20200279278A1-20200903-D00005.png)
![](/patent/app/20200279278/US20200279278A1-20200903-D00006.png)
![](/patent/app/20200279278/US20200279278A1-20200903-D00007.png)
![](/patent/app/20200279278/US20200279278A1-20200903-D00008.png)
![](/patent/app/20200279278/US20200279278A1-20200903-D00009.png)
![](/patent/app/20200279278/US20200279278A1-20200903-D00010.png)
View All Diagrams
United States Patent
Application |
20200279278 |
Kind Code |
A1 |
GEODAKYAN; Grigoriy S. ; et
al. |
September 3, 2020 |
METHOD AND SYSTEM FOR COMBINING OFFLINE AND ONLINE IDENTITIES WITH
ASSOCIATED PURCHASING INTENTION INDICATORS IN VIEW OF A GEOGRAPHIC
LOCATION
Abstract
A system and method for generating purchasing interest values in
relation to purchasing a product or service, by category, brand,
make, or model. The method includes associating a user identity
with recordations of activity in requesting information from assets
accessible by addressing universal resource locators, such as
applying a web browser to render web pages addressable by
registered domain names of the World Wide Web; additional
information such as purchasing history, residence address and
income level; and 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. The product or service may be
related to or comprise an automobile. A map is rendered that
separately associates geographic locations with individual user
identities. Marketing communications are sent to electronic and/or
postal addresses associated with user identities.
Inventors: |
GEODAKYAN; Grigoriy S.;
(ATLANTA, GA) ; YEN; Yong-jong Shawn; (Johns
Creek, GA) ; FOSS; Ryan Andrew; (Cumming, GA)
; HARDY; Jason; (Ball Ground, GA) ; BROEN; William
Dale; (Atlanta, GA) ; BORN; Natalie M.;
(BUFORD, GA) ; LUCENAY; JONATHAN; (CUMMINGS,
GA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
SUMMIT RESOURCES, LLC |
CUMMING |
GA |
US |
|
|
Assignee: |
SUMMIT RESOURCE, LLC
CUMMING
GA
|
Family ID: |
1000004827868 |
Appl. No.: |
16/101272 |
Filed: |
August 10, 2018 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
15712036 |
Sep 21, 2017 |
10078844 |
|
|
16101272 |
|
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 67/02 20130101;
G01C 21/3676 20130101; G06F 16/955 20190101; G09B 29/10 20130101;
G01C 21/32 20130101; G06F 16/9535 20190101; G06Q 30/0201
20130101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02; G01C 21/32 20060101 G01C021/32; G01C 21/36 20060101
G01C021/36; G06F 16/955 20060101 G06F016/955; G06F 16/9535 20060101
G06F016/9535 |
Claims
1. A method comprising: associating a hash of a unique user
identifier with an entity identifiable by the unique user
identifier; acquiring a plurality of records of instances of
website browser activity associated with the hash of the unique
user identifier; associating at least one record of offline
behavior associated with the entity; and deriving a purchasing
intention intensity value from the plurality of records of
instances of website browser activity in combination with the at
least one record of offline behavior.
2. The method of claim 1, wherein the unique user identifier is an
email address.
3. The method of claim 1, wherein the unique user identifier is a
telephone number address.
4. The method of claim 1, wherein the unique user identifier is an
account identifier.
5. The method of claim 1, wherein the entity is a natural born
person.
6. The method of claim 1, further comprising: assigning a
purchasing intent weighting value to at least one universal
resource locator; determining that a connection with the at least
one universal resource locator is recorded in the plurality of
records; and deriving the purchasing intention intensity value in
view of the purchasing intent weighting value.
7. The method of claim 6, wherein the at least one universal
resource locator comprises a domain name of the World Wide Web.
8. The method of claim 6, wherein the purchasing intent weighting
value is at least partially derived in view of relevance to
information describing an automobile quality.
9. The method of claim 1, further comprising: generating a
plurality of individually determined purchasing intent weighting
values separately in view of each of a plurality of universal
resource locators; assigning each of the plurality of the
individually determined purchasing intent weighting values to one
of the plurality of universal resource locators in a one-to-one
correspondence; determining that a connection with the at least one
of the plurality of universal resource locator is recorded in the
plurality of records; and deriving the purchasing intention
intensity value in view of the purchasing intent weighting
value.
10. The method of claim 1, wherein the at least one record of
offline behavior comprises information relevant to a likelihood of
an intent to purchase an automobile.
11. The method of claim 1, wherein the purchasing intention
intensity value quantifies a likelihood of the entity purchasing an
automobile.
12. The method of claim 11, wherein the purchasing intention
intensity value quantifies a likelihood of the entity purchasing a
specific make and model of the automobile.
13. The method of claim 1, wherein the purchasing intention
intensity value quantifies a likelihood of the entity purchasing an
automobile product or service.
14. The method of claim 1, wherein at least one record of the
plurality of records of instances of website browser activity is
acquired from a web browser.
15. The method of claim 1, wherein at least one record of the
plurality of records of instances of website browser activity is
acquired from a cellular telephone.
16. The method of claim 1, wherein the entity is associated with a
postal address.
17. The method of claim 1, wherein the entity is associated with a
residence address.
18. The method of claim 1 further comprising: calculating a travel
path extending between a geolocational position associated with the
entity and a point of sale location; and calculating an
accessibility value of the travel path.
19. The method of claim 1, further comprising rendering a
geographic map image, the geographic map image including a visual
indication of a geographic location associated with an entity.
20. The method of claim 1, comprising further rendering a
distinguishing visual indication of a geographic location
associated with a point of sale operation.
Description
CO-PENDING APPLICATION
[0001] 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
[0002] 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
[0003] 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.
[0004] 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.
[0005] 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
[0006] 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.
[0007] 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.
[0008] 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.
[0009] 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.
[0010] 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.
[0011] 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.
[0012] 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.
[0013] 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.
[0014] 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.
[0015] 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
[0016] 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:
[0017] 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;
[0018] FIG. 2 is process chart of a first preferred embodiment of
the invented method;
[0019] FIG. 3 is a detailed block diagram of the evaluator system
of FIG. 1;
[0020] 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;
[0021] 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;
[0022] 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;
[0023] 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;
[0024] 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;
[0025] FIG. 7A is a flowchart of the generation of a first
exemplary entity record by the evaluator system of FIG. 3;
[0026] 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;
[0027] 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;
[0028] 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;
[0029] 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;
[0030] 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;
[0031] 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;
[0032] 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;
[0033] 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;
[0034] 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;
[0035] 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;
[0036] 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;
[0037] 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;
[0038] 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;
[0039] FIG. 13 is a representation of aspects of a first exemplary
purchasing intensity value message as sent from the evaluator
system of FIG. 3;
[0040] 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;
[0041] FIG. 15 is a query table of aspects of the first exemplary
query message as sent to the evaluator system of FIG. 3;
[0042] 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;
[0043] FIG. 17 is a response table of aspects of the first
exemplary query response message as sent from the evaluator system
of FIG. 3;
[0044] 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;
[0045] 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;
[0046] 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;
[0047] 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;
[0048] FIG. 22 is URI score record table of selected contents of an
exemplary URI scoring record as applied by the method of FIG.
21;
[0049] FIG. 23 is a URI database table of selected contents of a
plurality of URI score records;
[0050] FIG. 24 is a block diagram of an exemplary webserver of the
electronic communications network of FIG. 1;
[0051] FIG. 25 is a block diagram of the user device of the
electronic communications network of FIG. 1;
[0052] FIG. 26 is a block diagram of content publishing system of
the electronic communications network of FIG. 1;
[0053] FIG. 27 is a block diagram of aggregator system of the
electronic communications network of FIG. 1;
[0054] FIG. 28 is a block diagram of an exemplary point of sale
system of the electronic communications network of FIG. 1; and
[0055] FIG. 29 is a block diagram of the mapping web service server
of the electronic communications network of FIG. 1.
DETAILED DESCRIPTION
[0056] 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.
[0057] 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.
[0058] 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 104A-104N, 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 112A-112N 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.
[0059] The user device 106 may be or comprise a bundled hardware
and software product such as (a.) an IPHONE.TM. cellular telephone
as marketed by Apple, Inc. of Cupertino, Calif.; (b.) an HTC TITAN
II.TM. cellular telephone as marketed by AT&T, Inc. of Dallas,
Tex. and running a WINDOWS 7.TM. operating system as marketed by
Microsoft Corporation of Redmond, Wash.; (c.) a GALAXY NEXUS.TM.
smart phone as marketed by Samsung Group of Seoul, Republic of
Korea and/or running an ANDROID.TM.; (d.) a network-communications
enabled THINKSTATION WORKSTATION.TM. 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.TM. operating system or a UNIX.TM. operating
system; (f) a network-communications enabled personal computer
configured for running WINDOWS XP.TM., VISTA.TM. or WINDOWS 7.TM.
operating system marketed by Microsoft Corporation of Redmond,
Wash.; (g.) a MACBOOK PRO.TM. personal computer as marketed by
Apple, Inc. of Cupertino, Calif.; (h.) an IPAD.TM. tablet computer
as marketed by Apple, Inc. of Cupertino, Calif.; (i.) a
TOUGHPAD.TM. tablet computer as marketed by Panasonic Corporation
of Kadoma, Osaka, Japan and running an ANDROID.TM. 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.
[0060] One or more of the plurality of web servers 104A-104N, the
content publishers 108, the aggregators 109, the evaluator system
110, the plurality of point of sale systems 112A-112N 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.TM. 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.TM. operating system or a UNIX.TM. operating
system; (c.) a network-communications enabled personal computer
configured for running WINDOWS XP.TM., VISTA.TM. or WINDOWS 7.TM.
operating system marketed by Microsoft Corporation of Redmond,
Wash.; (d.) a MACBOOK PRO.TM. personal computer as marketed by
Apple, Inc. of Cupertino, Calif.; (e.) an IPAD.TM. tablet computer
as marketed by Apple, Inc. of Cupertino, Calif.; or (f.) other
suitable computational system or electronic communications device
known in the art.
[0061] 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.TM. 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.TM.
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.
[0062] 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'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.
[0063] In step 2.06 a consumer database CON.DB that includes
information separately associated with identifiable entities is
acquired by the evaluator system 110.
[0064] 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.
[0065] 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.
[0066] 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 112A-112N is associated
with a point of sale location that is sufficiently proximate to the
geographic location discovered in step 2.12.
[0067] When the evaluator system 110 determines in step 2.14 that
one or more point of sale systems 112A-112N 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
112A-112N 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.
[0068] In the alternative, when the evaluator system 110 fails in
step 2.14 to identify at least one point of sale system 112A-112N
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.
[0069] 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 110A and a
system memory 110B that are bi-directionally communicatively
coupled by an internal communications bus 110C. The internal
communications bus 110C additionally bi-directionally couples the
central processing unit 110A and the system memory 110B with a
network interface 110D, a human operator input module 110E, a
display module 110F and a telephony interface 110G. The human
operator input module 110E enables an operator to input commands
and data to the central processing unit 110A and the system memory
110B via the internal communications bus 110C. The display module
110F enables visual rendering of information as directed by the
central processing unit 110A. The network interface 110D
bi-directionally communicatively couples the central processing
unit 110A with the network 100.
[0070] The system memory 110B 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's compensation
account number or identifier, a driver's license account number or
identifier, a social services account number or identifier, and/or
a Social Security
[0071] Account Number of the government of the United States of
America
[0072] 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.
[0073] The system memory 110C 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).
[0074] 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.
[0075] 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.
[0076] 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.
[0077] 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.
[0078] It is further understood that one or more of the databases
EN.DB, ACT.DB, CON.DB, URI.DB, USCR.DB & POS.DB may optionally,
alternatively or additionally be or comprise a relational database
management system, such as an IBM DB2 Universal Database.TM. 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 & 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 & POS.DB may optionally,
alternatively or additionally be or comprise a HADOOP.TM.
distributed file system as developed by the Apache Software
Foundation of Forest Hills, Md., or other suitable file system
known in the art.
[0079] The evaluator system 110 further comprises a plurality of
software programs stored in system memory 110B, 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.TM.
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 104A-114 of the network 100
via the telephony interface 110G and/or the network interface 110D.
The texting client TEXT.SW enables the evaluator system 110 to
communicate by text messaging with servers and systems 104A-114 of
the network 100 via the network interface 110D 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
104A-114 of the network 100 via the telephony interface 110G and/or
the network interface 110D. 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
& ACT.DB.
[0080] 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.
[0081] 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.
[0082] 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 104A-104N and systems
108 & 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 104A-104N and
system 109 in generation of other activity records
AREC.001-AREC.N.
[0083] 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 104A-104N. 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 106A that had been previously recorded
into a user web browser 106B (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.
[0084] 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.
[0085] 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.
[0086] 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.
[0087] 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.
[0088] 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
104A-104N, one more content publishers 108, one or more aggregators
109, and/or one or more of the plurality of point of sales system
POS 112A-112N. 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.
[0089] 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's addressed
and applied for access to information.
[0090] 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.
[0091] 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
104A-104N, one more content publishers 108, one or more aggregators
109, and/or one or more of the plurality of point of sales system
POS 112A-112N
[0092] 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.
[0093] 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.
[0094] 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.
[0095] 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.
[0096] 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.
[0097] 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
[0098] 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.
[0099] 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
[0100] 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.
[0101] 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 104A-104N, one more content publishers 108, one or more
aggregators 109, and/or one or more of the plurality of point of
sales system POS 112A-112N to be a persistent online identifier
ID.NET.001-ID.NET.N.
[0102] 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.
[0103] 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, & 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, &
CRIT.967 is associated with a mathematical function operator
OP.001, OP.843 & OP.967, wherein each paired mathematical
function operator OP.001, OP.843 & OP.967 is separately applied
to information associated with a selected EREC.001-EREC.N and
matching a criteria CRIT.001, CRIT.843, & 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.
[0104] 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, & 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, & CRIT.594 is
associated with a mathematical function operator OP.589, OP.826
& OP.594, wherein each paired mathematical function operator
OP.589, OP.826 & 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, & 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.
[0105] 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, & 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, &
CRIT.563 is associated with a mathematical function operator
OP.583, OP.921 & OP.563, wherein each paired mathematical
function operator OP.589, OP.921 & OP.563 is separately applied
to information associated with a selected EREC.001-EREC.N and
matching a criteria CRIT.583, CRIT.921, & 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.
[0106] 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 & 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 & CRIT.227 is associated
with a mathematical function operator OP.615, OP.358 & OP.227,
wherein each paired mathematical function operator OP.615, OP.358
& OP.227 is separately applied to information associated with a
selected EREC.001-EREC.N and matching a criteria CRIT.615, CRIT.358
& 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.
[0107] 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 & 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 &
CRIT.178 is associated with a mathematical function operator
OP.593, OP.696 & OP.178, wherein each paired mathematical
function operator OP.593, OP.696 & OP.178 is separately applied
to information associated with a selected EREC.001-EREC.N and
matching a criteria CRIT.593, CRIT.696 & 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.
[0108] 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.
[0109] 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.
[0110] 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.
[0111] 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 112A-112N
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 112A-112N 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 112A-112N 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 112A-112N with one or more personally identifying
information associated with the currently examined entity record
EREC.001-EREC.N.
[0112] 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.
[0113] 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.
[0114] 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.001A,
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
112A 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.001A 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.001A. 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.sup.th service identifier SERV.ID.855 that identifies an
855.sup.th service, a 433.sup.rd service type identifier
SERVT.ID.433 that identifies a 433.sup.rd service type, and a
233.sup.rd brand identifier BRND.ID.233 that identifies a
233.sup.rd brand.
[0115] 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.
[0116] 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 112A-112N. 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 .DELTA.T 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 .DELTA.T.
[0117] 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.
[0118] 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 .DELTA.D
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.
[0119] It is understood that the displacement value .DELTA.D may be
generated by application of the mapping web service of the mapping
system 114. It is further understood that the displacement value
.DELTA.D 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.
[0120] 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 112A-112N. 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
112A-112N noted as addressees in the one or more messages
PMSG.001-PMSG.N.
[0121] 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 112A-112N. The first
exemplary purchasing intensity value message PMSG.001 includes a
first POS network address POS.ADDR.001 of the first POS system 112A
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 & EMIL.002,
cellular telephone numbers CELL.001, CELL.020 & CELL.734, a
990.sup.th consumer record identifier CREC.ID.990, a 866.sup.th
legal name NAME.886, and a 487.sup.th account identifier
ACCT.487.
[0122] Referring now generally to the Figures and particularly to
FIG. 14, FIG. 14 is a flowchart of the first point of sale system
112A in generating a first exemplary query message QMSG.001 and
sending the first exemplary query message QMSG.001 to the evaluator
system 110.
[0123] In the interest of clarity of explanation, the method of
FIG. 14 will be discussed in reference to the first POS system 112A
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 112A-112N.
[0124] In step 14.00 the first POS system 112A connects with the
network 100 and formats the first query message QMSG.001 in step
14.02. The first POS system 112A 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.
[0125] In step 14.08 the first POS system 112A 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 112A-112N 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.
[0126] In step 14.10 the first POS system 112A enters an optional
distance variance value .DELTA.D into the first query message
QMSG.001. The first POS system 112A 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 112A.
[0127] In optional step 14.12 the first POS system 112A enters into
the first query message QMSG.001 a time displacement value
.DELTA.T, whereby the first POS system 112A 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 112A
enters into the first query message QMSG.001 a first purchasing
intensity level value ILEVL.001 into the first query message
QMSG.001.
[0128] The first POS system 112A 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.
[0129] 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
.DELTA.T, an optional distance variance value .DELTA.D, and an
optional first purchasing intensity level value IVEVL.001.
[0130] 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.
[0131] 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
112A. 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 112A-112N.
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.
[0132] 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.
[0133] 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.
[0134] In optional step 16.10 the evaluator system 110 extracts the
optional time displacement value .DELTA.T from the first query
message QMSG.001, and in optional step 16.12 the evaluator system
110 extracts the optional distance variance value .DELTA.D 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 .DELTA.T may be applied by the evaluator system
110 in the method of FIG. 16 rather than a time displacement value
.DELTA.T 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 .DELTA.D may be applied by
the evaluator system 110 in the method of FIG. 16 rather than a
distance variance value .DELTA.D as read from the first query
message QMSG.001.
[0135] The evaluator system 110 initializes a second counter value
CTR2 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.CTR2 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.CTR2.
When the evaluator system 110 determines that entity record
EREC.CTR2 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 112A in the POS database
POS.DB, wherein if the derived distance value is less than or equal
to distance variance value .DELTA.D harvested from the first query
message QMSG.001, or in an alternative less than or equal to a
default distance variance value .DELTA.D, the evaluator system 110
proceeds on to execute step 16.22.
[0136] 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.CTR2 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.CTR2. 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.CTR2. Optionally or additionally, the evaluator system
110 may write additional information associated by the selected
entity record EREC.CTR2, such as, but not limited to location data
LOC.001-LOC.N and other associated information DATA.001-DATA.N.
[0137] The evaluator system 110 proceeds from step 16.26 and to
step 16.28 to determine if the second counter value CTR2 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.
[0138] When the evaluator system 110 determines in step 16.28 that
the second counter value CTR2 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 CTR2. 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 CTR2 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.
[0139] 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.
[0140] 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
[0141] 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 &
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.
[0142] 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
.DELTA.D as applied by the evaluator system 110 in step 16.20 of
the method of FIG. 16, and the time displacement value .DELTA.T
applied by the evaluator system 110 in step 16.22 of the method of
FIG. 16.
[0143] 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 & 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
& 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.sup.th entity
location data LOC.500 and/or a 500.sup.th consumer record
information DATA.500.
[0144] Referring now generally to the Figures and particularly to
FIG. 18, FIG. 18 is a flowchart of a point of sale system 112A-112N
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
104A-104N, the user device 106, the content publisher 108, the
aggregator 109, the evaluator system 110, other point of sale
systems 112B-112N 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.
[0145] 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 112A 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 104A-104N & 114 and systems
112A-112N, 109, 108 & 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.
[0146] The first POS system 112A 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 112A 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 112A.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 112A selects location data LOC,
LOC.900, LOC.447, LOC.045, LOC.792, LOC.492 & LOC.N from the
first query response message RMSG.001. In step 18.10 the first POS
system 112A 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
& LOC.N. In step 18.12 the first POS system 112A 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 & LOC.N that a
selected AVT.001-AVT.N shares an association.
[0147] In step 18.14 the first POS system 112A 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 & DATA.N associated with
a same location data LOC.001-LOC.N as the highlighted avatar
AVT.001-AVT.N.
[0148] The first POS system 112A 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 112A
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 112A 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.
[0149] 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 112A.A and including a plurality
of representative avatars AVT.001-AVT.N & 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 & AVT.900 respectively represent
entity location data LOC.001, LOC.045 & 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 & EMAIL.900. A diamond shape of the
447.sup.th avatar AVT.447 indicates the nature of the 447.sup.th
avatar AVT.447 as representing a geographic location of an entity
associated with a 447.sup.th cellular phone number CELL.447. A
pentagonal shape of the N.sup.th avatar AVT.N indicates the nature
of the N.sup.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.sup.nd avatar AVT.422 indicates the
nature of the 422.sup.nd avatar AVT.422 as representing a
geographic location of an entity associated with a 422.sup.nd
account identifier ACC.422. A relieved orthogonal shape of the
500.sup.th avatar AVT.500 indicates the nature of the 500.sup.th
avatar AVT.500 as representing a geographic location of an entity
associated with a 500.sup.th consumer record CREC.500.
[0150] 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.
[0151] 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 104A-104N, the
user device 106, the content publisher 108, the aggregator 109, the
evaluator system 110, other point of sale systems 112B-112N 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.
[0152] 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.
[0153] 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.
[0154] 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.
[0155] The evaluator system 110 initializes a third loop counter
CTR3 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.CTR3 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.CTR3 of step 21.10
is recorded in the URI database URI.DB in step 21.12.
[0156] 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.
[0157] 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 CTR3 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
CTR3 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 CTR3. In the alternative, when the
evaluator system 110 determines in step 21.20 that the current
value of the third counter CTR3 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.
[0158] 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.
[0159] 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.
[0160] 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.
[0161] 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.
[0162] 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.
[0163] A third URI record UREC.003 includes a third URI record
identifier UREC.ID.003 and a null value for matches with item
identifiers.
[0164] 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.sup.th service type identifier SERVT.ID.625.
[0165] 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.
[0166] Referring now generally to the Figures and particularly to
FIG. 24, FIG. 24 is a block diagram of the exemplary first web
server 104A. It is understood that one or more other webservers
104B-104N may include some or all of the aspects and elements of
the exemplary first web server 104A as disclosed herein.
[0167] The first web server system 104A includes a WS central
processing unit 104A.A and a WS system memory 104B that are
bi-directionally communicatively coupled by a WS internal
communications bus 104C. The WS internal communications bus 104C
additionally bi-directionally couples the WS central processing
unit 104A.A and the WS system memory 104B with a WS network
interface 104D, a WS human operator input module 104E, a display
module 104F that includes the WS display screen 104G and a
[0168] WS telephony interface 104H. The WS human operator input
module 104E enables an operator to input commands and data to the
WS central processing unit 104A.A and the WS system memory 104B via
the WS internal communications bus 104C. The WS display module 104F
enables visual rendering of information at the WS display screen
104A.A as directed by the WS central processing unit 104A.A. The WS
network interface 104D bi-directionally communicatively couples the
WS central processing unit 104A.A with the WS network 100.
[0169] The WS system memory 104B 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 104A to perform and provide all aspects
of the invented method relevant to operations of the first web
serve 104A, to include web page publishing and hash generation.
[0170] 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 104A. 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.TM. 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 & 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.
[0171] A web page publishing software WS.PUB.SW enables the first
web server 104A to generate and transmit information suitable for
rendering by the user web browser 106B. 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.
[0172] 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 104A in
one or more data storage systems (not shown) that are accessible to
the first web server system 104A via the network 100 and/or an
alternate electronic communications network (not shown).
[0173] The first web server 104A further comprises a plurality of
software programs stored in system memory 104B, 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 104A to communicate by email transmissions with servers and
systems 104B-114 of the network 100 via the WS telephony interface
104H and/or the WS network interface 104D. The WS texting client
WS.TEXT.SW enables the first web server 104A to communicate by text
messaging with servers and systems 104B-114 of the network 100 via
the WS network interface 104D and/or the WS telephony network
interface 104H. The WS network communication software WS.NET.SW
enables the first web server 104A to communicate by other suitable
messaging protocols known in the art with servers and systems
104A-114 of the network 100 via the telephony interface 104H and/or
the network interface 104D.
[0174] Referring now generally to the Figures and particularly to
FIG. 25, FIG. 25 is a block diagram of the user device 106.
[0175] The user device 106 includes a UD central processing unit
106C and a UD system memory 106D that are bi-directionally
communicatively coupled by a UD internal communications bus 106E.
The UD internal communications bus 106E additionally
bi-directionally couples the UD central processing unit 106C and
the UD system memory 106D with a UD network interface 106F, a UD
human operator input module 106G, a display module 106H that
includes a UD display screen 1061, and a UD telephony interface
106J. The UD human operator input module 106G enables an operator
to input commands and data to the UD central processing unit 106C
and the UD system memory 106D via the UD internal communications
bus 106E. The UD display module 106H enables visual rendering of
information at the UD display screen 1061 as directed by the UD
central processing unit 106C. The UD network interface 106F
bi-directionally communicatively couples the UD central processing
unit 106C with the UD network 100.
[0176] The UD system memory 106D 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.
[0177] 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.TM. 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 & 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.
[0178] 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).
[0179] The user device 106 further comprises a plurality of
software programs stored in the UD system memory 106D, to include
the web browser 106B that may include the cookie 106A, 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 106B 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 106B may be
or comprise a SAFARI.TM. web browser provided by APPLE of
Cupertino, Calif., or other suitable web browser known in the
art.
[0180] The UD email client UD.EMAIL.SW enables the user device 106
to communicate by email transmissions with servers and systems
104A-106 of the network 100 via the UD telephony interface 1061
and/or the UD network interface 106F. The UD texting client
UD.TEXT.SW enables the user device 106 to communicate by text
messaging with servers and systems 104A-106 of the network 100 via
the UD network interface 106F 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 106F.
[0181] Referring now generally to the Figures and particularly to
FIG. 26, FIG. 26 is a block diagram of the content publisher
108.
[0182] The content publisher 108 includes a PUB central processing
unit 108A and a PUB system memory 108B that are bi-directionally
communicatively coupled by a PUB internal communications bus 108C.
The PUB internal communications bus 108C additionally
bi-directionally couples the PUB central processing unit 108A and
the PUB system memory 108B with a PUB network interface 108D, a PUB
human operator input module 108E, a display module 108F that
includes a PUB display screen 108G, and a
[0183] PUB telephony interface 108H. The PUB human operator input
module 108E enables an operator to input commands and data to the
PUB central processing unit 108A and the PUB system memory 108B via
the PUB internal communications bus 108C. The PUB display module
108F enables visual rendering of information at the PUB display
screen 108A.A as directed by the PUB central processing unit 108A.
The PUB network interface 108D bi-directionally communicatively
couples the PUB central processing unit 108A with the PUB network
100.
[0184] The PUB system memory 108B 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.
[0185] 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.TM. 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 & 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 106B. 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.
[0186] 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).
[0187] The content publisher 108 further comprises a plurality of
software programs stored in the PUB system memory 108B, 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.TM. web browser provided
by APPLE of Cupertino, Calif., or other suitable web browser known
in the art.
[0188] The PUB email client PUB.EMAIL.SW enables the content
publisher 108 to communicate by email transmissions with servers
and systems 104A-114 of the network 100 via the PUB telephony
interface 108H and/or the PUB network interface 108D. The PUB
texting client PUB.TEXT.SW enables the content publisher 108 to
communicate by text messaging with servers and systems 104A-114 of
the network 100 via the PUB network interface 108D and/or the PUB
telephony network interface 108H. 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 108H and/or the PUB network interface 108D.
[0189] Referring now generally to the Figures and particularly to
FIG. 27, FIG. 27 is a block diagram of the aggregator 109.
[0190] The aggregator 109 includes an AG central processing unit
109A and an AG system memory 109B that are bi-directionally
communicatively coupled by an AG internal communications bus 109C.
The AG internal communications bus 109C additionally
bi-directionally couples the AG central processing unit 109A and
the AG system memory 109B with an AG network interface 109D, an AG
human operator input module 109E, a display module 109F that
includes an AG display screen 109G, and an AG telephony interface
109H. The AG human operator input module 109E enables an operator
to input commands and data to the AG central processing unit 109A
and the AG system memory 109B via the AG internal communications
bus 109C. The AG display module 109F enables visual rendering of
information at the AG display screen 109A.A as directed by the AG
central processing unit 109A. The AG network interface 109D
bi-directionally communicatively couples the AG central processing
unit 109A with the AG network 100.
[0191] The AG system memory 109B 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.
[0192] 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.TM. 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 & 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.
[0193] 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 10B. 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.
[0194] 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).
[0195] The aggregator 109 further comprises a plurality of software
programs stored in the AG system memory 109B, 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.TM. web browser provided by APPLE of Cupertino, Calif., or
other suitable web browser known in the art.
[0196] The AG email client AG.EMAIL.SW enables the aggregator 109
to communicate by email transmissions with servers and systems
104A-114 of the network 100 via the AG telephony interface 109H
and/or the AG network interface 109D. The AG texting client
AG.TEXT.SW enables the aggregator 109 to communicate by text
messaging with servers and systems 104A-114 of the network 100 via
the AG network interface 109D and/or the AG telephony network
interface 109H. 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 109H
and/or the AG network interface 109D.
[0197] Referring now generally to the Figures and particularly to
FIG. 28, FIG. 28 is a block diagram of the exemplary first POS
system 112A. It is understood that one or more other POS systems
112B-112N may include some or all of the aspects and elements of
the exemplary first POS system 112A as disclosed herein.
[0198] The first POS system 112A includes a POS central processing
unit 112A.A and a POS system memory 112B that are bi-directionally
communicatively coupled by a POS internal communications bus 112C.
The POS internal communications bus 112C additionally
bi-directionally couples the POS central processing unit 112A.A and
the POS system memory 112B with a POS network interface 112D, a POS
human operator input module 112E, a display module 112F that
includes the POS display screen 112G and a POS telephony interface
112H. The POS human operator input module 112E enables an operator
to input commands and data to the POS central processing unit
112A.A and the POS system memory 112B via the POS internal
communications bus 112C. The POS display module 112F enables visual
rendering of information at the POS display screen 112A.A as
directed by the POS central processing unit 112A.A. The POS network
interface 112D bi-directionally communicatively couples the POS
central processing unit 112A.A with the POS network 100.
[0199] The POS system memory 112B 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 112A to perform and provide all
relevant aspects of the invented method, to include web page
publishing and hash generation.
[0200] 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 112A. 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.TM. 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 & 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.
[0201] A web page publishing software POS.PUB.SW enables the first
POS system 112A to generate and transmit information suitable for
rendering by the user web browser 106B. 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.
[0202] 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 112A in one or
more data storage systems (not shown) that are accessible to the
first POS system 112A via the network 100 and/or an alternate
electronic communications network (not shown).
[0203] The first the POS A further comprises a plurality of
software programs stored in system memory 112B, 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 112A to communicate by email transmissions with servers and
systems 112A-114 of the network 100 via the POS telephony interface
112H and/or the POS network interface 112D. The POS texting client
POS.TEXT.SW enables the first POS system 112A to communicate by
text messaging with servers and systems 112A-114 of the network 100
via the POS network interface 112D and/or the POS telephony network
interface 112H. The POS network communication software POS.NET.SW
enables the first POS system 112A 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 112H and/or
the POS network interface 112D.
[0204] Referring now generally to the Figures and particularly to
FIG. 29, FIG. 29 is a block diagram of the mapping system 114.
[0205] The mapping system 114 includes an MS central processing
unit 114A and an MS system memory 114B that are bi-directionally
communicatively coupled by an MS internal communications bus 114C.
The MS internal communications bus 114C additionally
bi-directionally couples the MS central processing unit 114A and
the MS system memory 114B with an MS network interface 114D, an MS
human operator input module 114E, a display module 114F that
includes an MS display screen 114G, and an MS telephony interface
114H. The MS human operator input module 114E enables an operator
to input commands and data to the MS central processing unit 114A
and the MS system memory 114B via the MS internal communications
bus 114C. The MS display module 114F enables visual rendering of
information at the MS display screen 114G as directed by the MS
central processing unit 114A. The MS network interface 114D
bi-directionally communicatively couples the MS central processing
unit 114A with the MS network 100.
[0206] The MS system memory 114B 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.
[0207] 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.TM. 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 & 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.
[0208] 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 106B. 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.
[0209] 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).
[0210] The mapping system 114 further comprises a plurality of
software programs stored in the MS system memory 114B, to include
an MS web browser
[0211] 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.TM. web browser provided by APPLE of Cupertino,
Calif., or other suitable web browser known in the art.
[0212] The MS email client MS.EMAIL.SW enables the mapping system
114 to communicate by email transmissions with servers and systems
104A-114 of the network 100 via the MS telephony interface 114H
and/or the MS network interface 114D. The
[0213] MS texting client MS.TEXT.SW enables the mapping system 114
to communicate by text messaging with servers and systems 104A-114
of the network 100 via the MS network interface 114D and/or the MS
telephony network interface 114H. 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 114H and/or the MS network interface 114D.
[0214] 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.
[0215] 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.
[0216] 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.
[0217] 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.
[0218] 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.
[0219] 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.
* * * * *
References