U.S. patent application number 09/928956 was filed with the patent office on 2002-05-09 for systems and methods for virtual population mutual relationship management using electronic computer driven networks.
Invention is credited to Sterling, Deborah.
Application Number | 20020055833 09/928956 |
Document ID | / |
Family ID | 26847588 |
Filed Date | 2002-05-09 |
United States Patent
Application |
20020055833 |
Kind Code |
A1 |
Sterling, Deborah |
May 9, 2002 |
Systems and methods for virtual population mutual relationship
management using electronic computer driven networks
Abstract
A method and system for creating and managing Virtual Population
mutual relationships is disclosed. The method uses a Rich Semantic
Model component, expert system components, and various interface
components and other components to dynamically alter the visitation
experience as received by the Visitor at a computer and to allow
the Visitor control over their Virtual Representative that controls
this personal experience.
Inventors: |
Sterling, Deborah; (Ottawa,
CA) |
Correspondence
Address: |
BAKER & BOTTS
30 ROCKEFELLER PLAZA
NEW YORK
NY
10112
|
Family ID: |
26847588 |
Appl. No.: |
09/928956 |
Filed: |
August 13, 2001 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
09928956 |
Aug 13, 2001 |
|
|
|
09477168 |
Jan 4, 2000 |
|
|
|
60150380 |
Aug 23, 1999 |
|
|
|
Current U.S.
Class: |
703/22 |
Current CPC
Class: |
G06Q 30/02 20130101 |
Class at
Publication: |
703/22 |
International
Class: |
G06F 009/45 |
Claims
What is claimed is:
1. A method for dynamically creating and managing mutual
relationships between a virtual visitor and a virtual enterprise
expert on an electronic network, comprising the steps of: (i)
Providing a virtual population, the semantic model of which is
rendered specific to one or more real world populations, said
virtual population comprising instances of said model; (ii)
Providing expert system software which effects a virtual enterprise
expert, said software tailored to a particular virtual population;
and (iii) Applying said expert system software to an instance of
said semantic model to create a unique virtual visitation
experience.
2. The method of claim 1, wherein said expert system software which
effects a virtual enterprise expert is capable of learning about
said virtual population.
3. The method of claim 1, wherein said expert system software which
effects a virtual enterprise expert intelligently optimizes
enterprise goals.
4. The method of claim 1, wherein said expert system software which
effects a virtual enterprise expert is further capable of
generating new rules.
5. The method of claim 1, wherein said expert system software which
effects a virtual enterprise expert comprises an inference or a
reasoning engine.
6. The method of claim 1, wherein said expert system software which
effects a virtual enterprise expert further comprises a set of
rules representative of enterprise expert knowledge.
7. The method of claim 1, wherein said expert system software which
effects a virtual enterprise expert contains reasoning engine log
information, wherein said reasoning engine log information may
store self-observational expert events and learned information.
8. The method of claim 1, wherein said expert system software which
effects a virtual enterprise expert is capable of making rule
suggestions.
9. The method of claim 1, wherein said expert system software which
effects a virtual enterprise expert is programmatically accessible
to outside systems.
10. The method of claim 1, wherein said expert system software
which effects a virtual enterprise expert is capable of additional
functions such as rule ordering to control execution.
11. The method of claim 1, wherein said semantic model comprises
fact information selected from classes comprising simple facts,
behavioral facts, preference facts, and combinations thereof.
12. The method of claim 1, wherein said semantic model includes
universal facts.
13. The method of claim 1, wherein said semantic model further
includes enterprise-specific facts.
14. The method of claim 1, wherein said semantic model further
includes custom enterprise-specific facts.
15. The method of claim 1, wherein said semantic model further
includes facts which are restricted for use by the real world
user.
16. The method of claim 1, further including the step of providing
a report generator for reporting on one or more of the following
activities: (i) expert activity; (ii) rules used; (iii) rule
success, (iv) rules suggested; and (v) learnings.
17. The method of claim 1, wherein said virtual enterprise expert
is an e-commerce expert.
18. The method of claim 1, wherein said semantic model is a model
of consumers.
19. The method of claim 1, wherein said semantic model includes
facts which reflect whether the real world visitor is a known or
anonymous visitor.
20. The method of claim 1, wherein said method employs one or more
computer systems, said computer systems including one or more user
site computers, one or more visit site computers, and one or more
populations site computers.
21. The method of claim 1, wherein said virtual population is
stored, retrievable, and updateable through a machine-readable
database.
22. The method of claim 1, wherein said virtual population is
stored, retrievable, and updateable at a site where said virtual
population resides.
23. The method of claim 1, wherein said expert system software
which effects said virtual enterprise expert includes expert
knowledge.
24. The method of claim 1, wherein said virtual enterprise expert
is an expert in the marketing and/or selling of goods and
services.
25. The method of claim 1, wherein said enterprise expert is
deployed on a customer-hosted computer.
26. The method of claim 1, wherein said electronic communication
network is a private IP network.
27. The method of claim 1, wherein said electronic communication
network is a public IP network or the World Wide Web.
28. The method of claim 1, wherein said electronic communication
network is a combination of public and private IP networks.
29. The method of claim 1, wherein there are multiple enterprise
sites and one population site.
30. The method of claim 1, further including the step of providing
a rule editor.
31. The method of claim 30, wherein said rule editor is capable of
a point-and-click style interface.
32. The method of claim 1, further including the step of providing
a content management system, wherein said content management system
is capable of performing one or more of the following: storing,
viewing, labeling, and annotating arbitrary content available for
personalization decisions.
33. The method of claim 32, further including the step of
making-labels resulting from said labeling of content available for
rule decisions.
34. The method of claim 32, wherein said content management system
manages pointers to said content.
35. The method of claim 1, further including the step of providing
a billing system.
36. The method of claim 35, wherein said billing system is capable
of billing based on rule success.
37. The method of claim 35, wherein said billing system is capable
of billing based on successful use of real world visitor
identification, said real world visitor identification including
one or more identification of population instances, identification
with specific facts, identification with custom facts, and
identification with some number of facts.
38. The method of claim 1, wherein access to said expert system
software is provided as a software plugin with an Application
Programmer Interface.
39. The method of claim 1, further including the step of providing
expert system software which effects a virtual population expert,
said virtual population expert tailored to a particular virtual
population.
40. The method of claim 39, wherein said expert system software
which effects a virtual population expert is further capable of
generating new rules.
41. The method of claim 39, where said expert system software which
effects a virtual population expert comprises an inference or a
reasoning engine.
42. The method of claim 39, wherein said expert system software
which effects a virtual population expert further comprises a set
of rules representative of population expert knowledge.
43. The method of claim 39, wherein said expert system software
which effects a virtual population expert contains reasoning engine
log information, wherein said reasoning engine log information may
store self-observational expert events and learned information.
44. The method of claim 39, wherein said expert system software
which effects a virtual population expert is capable of making rule
suggestions.
45. The method of claim 39, wherein said expert system software
which effects a virtual population expert is programmatically
accessible to outside systems.
46. The method of claim 39, wherein said expert system software
which effects a virtual population expert is capable of additional
functions such as rule ordering to control execution.
47. The method of claim 39, wherein said expert system software
which effects a virtual population expert is capable of learning
new information about said virtual population.
48. The method of claim 39, wherein said expert systems software
which effects a virtual population expert is capable of predicting
new information about said virtual population.
49. The method of claim 39, wherein said virtual population expert
is a consumer population expert.
50. The method of claim 1, further including the step of providing
expert system software which effects a virtual visitor expert.
51. The method of claim 50, further including the step of providing
a visitor tool, wherein said virtual visitor expert assists a real
world visitor in managing an instance of said virtual population
that corresponds to a real world visitor through said visitor
tool.
52. The method of claim 50, wherein said expert system software
which effects a virtual visitor expert is further capable of
generating new rules.
53. The method of claim 50, where said expert system software which
effects a virtual visitor expert comprises an inference or a
reasoning engine.
54. The method of claim 50, wherein said expert system software
which effects a virtual visitor expert further comprises a set of
rules representative of visitor expert knowledge.
55. The method of claim 50, wherein said expert system software
which effects a virtual visitor expert contains reasoning engine
log information, wherein said reasoning engine log information may
store self-observational expert events and learned information.
56. The method of claim 50, wherein said expert system software
which effects a virtual visitor expert is capable of making rule
suggestions.
57. The method of claim 50, wherein said expert system software
which effects a virtual visitor expert is programmatically
accessible to outside systems.
58. The method of claim 50, wherein said expert system software
which effects a virtual visitor expert is capable of additional
functions such as rule ordering to control execution.
59. The method of claim 50, wherein said virtual visitor expert
interacts with said real world visitor through said visitor tool to
gain self-consistent knowledge of said real world visitor.
60. The method of claim 50, wherein said virtual visitor expert
interacts with said real world visitor through said-visitor tool to
learn new knowledge of said real world visitor based on expert
knowledge of said virtual population.
61. The method of claim 50, wherein said virtual visitor expert
interacts with said real world visitor through said visitor tool to
restrict access to at least some information in said instance under
the control of said real world user.
62. The method of claim 50, wherein said virtual visitor expert is
a consumer visitor expert.
63. A method for dynamically creating and managing mutual
relationships between a virtual visitor and a virtual enterprise
expert on an electronic network, comprising the steps of: (i)
Providing a virtual population, the semantic model of which is
rendered specific to one or more real world populations, said
virtual population comprising instances of said model; (ii)
Providing software accessible to a real world enterprise expert,
said software permitting said real world expert to create one or
more expert rules which can be applied to instances of said
semantic model; (iii) Providing expert system software which
effects a virtual enterprise expert, said software tailored to a
particular virtual population; and (iv) Applying said expert system
software to an instance of said semantic model to create a unique
virtual visitation experience in accordance with the real world
expert rules, the interests and/or desires of the visitor, and the
expert knowledge of the expert system software.
64. The method of claim 63, wherein access to said expert system
software is provided as a software plugin.
65. The method of claim 64, wherein said plugin is provided with an
Application Programmer Interface as an extension of a web
programming environment.
66. The method of claim 65, further including an Application
Programmer Interface call, wherein said call provides access to
said virtual enterprise expert whose judgements decide upon web
page real estate to be shown to a virtual visitor.
67. The method of claim 66, wherein multiple Application Programmer
Interface calls are used on multiple web pages to manage web page
content.
68. The method of claim 65, further including an Application
Programmer Interface call which provides access to a virtual
enterprise expert, wherein said virtual enterprise expert will
intelligently add new information to a virtual representative.
69. The method of claim 68, wherein said new information is sent
over said electronic communication network to intelligently update
said virtual population instance corresponding to said visitor.
70. The method of claim 63, wherein said virtual population
instance is automatically retrieved over said electronic
network.
71. The method of claim 70, wherein a real world visitor
corresponding to said virtual population instance is identified
electronically.
72. The method of claim 71, wherein said real world visitor
corresponding to said virtual population instance is identified
electronically using cookies, e-wallet technology, or other
electronic identification mechanisms.
73. The method of claim 65, wherein said Application Programmer
Interface further comprises a parameter which allows for a default
piece of content to be displayed.
74. The method of claim 63, further including web-based versions of
one or more of the following: (i) rule editor; (ii) report
generator; and (iii) content management system.
75. A system for dynamically creating and managing mutual
relationships between a virtual visitor and a virtual enterprise
expert on an electronic network, comprising: (i) A virtual
population, the semantic model of which is rendered specific to one
or more real world populations, said virtual population comprising
instances of said model; and (ii) Software accessible to a real
world enterprise expert, said software permitting said real world
expert to create one or more expert rules which can be applied to
instances of said semantic model; and (iii) Expert system software
which effects a virtual enterprise expert, said software tailored
to a particular virtual population; wherein the application of said
expert system software to an instance of said semantic model
creates a unique virtual visitation experience in accordance with
the real world rules, the interests and/or desires of the visitor,
and the expert knowledge of the expert system software.
76. A system for conducting real-time dynamic marketing on the
Internet comprising: (i) A web site which contains information
which can be dynamically altered and made available to a web site
visitor; (ii) Expert system software which effects a virtual
enterprise expert, said software tailored to a particular virtual
population; and (iv) A stored, retrievable, and updateable virtual
population accessible through a machine-readable database, the
semantic model of which is rendered specific to one or more real
world populations, said virtual population comprising instances of
said model.
77. A system for creating and maintaining a virtual mutual
relationship, said system being accessed by a user through a
network by one or more networked computers and comprising: (i) A
database comprising a richly semantically modeled virtual
population; (ii) One or more expert systems in communication with
said database and said software associated with a network site,
said expert systems(s) being capable of performing at least one or
more of the following tasks: (a.) applying expert rules to
instances of said semantically modeled population to produce a
reasoned result; (b.) applying expert system knowledge to instances
of said semantically modeled population to produce a reasoned
result; (c.) observing and understanding its own activity; (d.)
learning new information as such information is generated; (e.)
creating new expert rules automatically; and (f.) reporting on
(a)-(e) above.
78. The system of claim 74, wherein said expert system is operating
in response to a virtual visit at said network site and resulting
in the receipt by the user, in real time, of unique digitally
managed sensory content at the local computer.
Description
CROSS-REFERENCE TO RELATED PRIOR APPLICATIONS
[0001] This application claims priority to U.S. Provisional
Application No. 60/150,380, filed Aug. 23, 1999, the subject matter
of which is incorporated by reference herein.
FIELD OF THE INVENTION
[0002] The present invention relates to systems and methods for
dynamically exchanging information electronically using an
Electronic Communication Network. More particularly, the present
invention relates to creating and managing personalized experiences
for Visitors to a network Enterprise Site to create and maintain a
dynamic virtual mutual relationship between an electronic expert
system emulating a real world Enterprise Expert and the Visitor.
Most particularly, the present invention relates to such systems
applied to electronic commerce and specifically to electronic
marketing applications.
BACKGROUND OF RELATED TECHNOLOGY
[0003] Traditional mediums of marketing and advertising have all
been inherently broadcast. A message delivered on radio,
television, billboards, magazines and the like is delivered to a
broad base of individuals. The information received in this manner
is identical to all individuals. For example, every person who
opens a particular issue of a magazine or views a television
advertisement sees exactly the same information. Using this
traditional broadcast media, the marketer has only been able to
deliver personalized messages to a broad base. For example, the
marketer targets readers of a science magazine based on the facts
that the readership is largely university educated with an interest
in science and has a particular range of income.
[0004] With the advent of computer-driven electronic spaces, such
as computer networks, and in particular the Internet, the
opportunity now exists for delivering visitor information in a
completely "soft" medium, i.e., where all components of information
are delivered under software control. This is in contrast to
delivering information in a "hard" format, such as through
traditional print advertisements. A medium such as the Internet is
inherently non-broadcast. For the first time in history, an
inherently non-broadcast medium is available to fulfill the
marketer's dream of true "one-to-one" personalization. Although
banner spaces used on the Internet for advertising and the like are
essentially broadcast in nature, electronic spaces offer, by way of
software control, the possibility of targeting individuals
uniquely, thereby permitting the delivery of personalized
information to the individual.
[0005] Marketing on the Internet has thus far consisted primarily
of providing advertisements to web site visitors in an essentially
broadcast manner. As illustrated in FIG. 14, typical electronic
advertising systems include an advertisement server 1 which
contains advertisements 3 provided by content providers 5.
Individuals visiting web sites 7, 7', and 7" are shown
advertisements 3 which are transmitted from the advertisement
server 1 to the web sites 7, 7', and 7" over the Internet 8. New
advertisements 3 are typically selected at established intervals to
replace those being shown to the visitor. While these
advertisements 3 are often shown randomly to the visitor, they may
be selected based on information which is known about the visitor,
such as when facts about the visitor have been stored in a profile
database 9.
[0006] Typically, when a visitor selects an advertisement 3, such
as by clicking on an advertisement banner using a mouse, the
visitor is sent to the advertiser's web site. Generally, the web
site owner is compensated for displaying the advertisements 3 and
is additionally compensated each time a visitor clicks on an
advertisement 3. To the extent that advertisements 3 on an owner's
web site 7, 7', or 7" are not inconsistent with the interests of
the owner, the owner is not generally concerned with the content of
the advertisements 3. This is similar to traditional broadcast
advertising in magazines.
[0007] As noted, some companies offer advertisement-server
technology which utilize systems involving profiles in order to
personalize electronic marketing. Profiles, which can be edited and
are often updated by tracking the behavior of individuals on the
World Wide Web, can be used to determine the particular
advertisements, including their background, coloration, etc. that
an individual will experience when visiting a web site. As an
individual navigates a web site, provides information, and makes
purchases, her profile is updated accordingly, thereby allowing for
a more customized visitation experience. However, such systems are
very limited in their ability to provide the visitor with a unique
visitation experience as they are limited to comparing and matching
information contained in various databases (e.g., a profile
database and an advertisement database). These systems are also
limited to advertising. Their purpose is not to manage the
relationship or personalize the experience directly with the Ad
hosting sites 7, 7', and 7".
[0008] For instance, U.S. Pat. No. 5,933,811 (Angles et al.)
describes a system for delivering customized electronic
advertisements in an interactive communication system. The
customized advertisements are selected based on consumer profiles
and are then integrated at different web sites. The consumer
provides data which is used to establish her profile. When the
consumer selects content on a web site, an advertising request is
sent to an advertisement provider computer which then generates a
custom advertisement based on the consumer's profile.
[0009] Customized advertising is also described in U.S. Pat. No.
5,948,061, assigned to DoubleClick.sup.SM, Inc. This patent
essentially uses data from a user profile to select an appropriate
advertisement from a data bank of advertisements most appropriate
to display to the user. This system requires an advertising server,
a content provider, a user mode, an affiliate web site, and an
advertising web site. When a user visits a web page which is
affiliated with the advertising server, the affiliated page
includes an embedded reference to an object provided by the
advertising server which causes the advertising server to provide
the advertising image which will appear on the web page displayed
by the user's browser. The server uses information about the user
which is passed on by the browser to select an appropriate
advertisement for the particular user. Data is compiled about the
user, such as the user's name, Internet Protocol address, domain
type, time zone, location, particular advertisements seen, and the
number of times each are seen. This data is then compared with
various ads to select an appropriate match.
[0010] Further, U.S. Pat. No. 5,717,923, assigned to Intel.RTM.
Corporation, describes another method of Ad serving. This method
includes customizing electronic information, such as
advertisements, to the preferences of an individual user. This
method compares user preference data in a user profile database to
a unit of electronic information to generate a customized unit of
electronic advertising information. The user profile is updated
using a client activity monitor which allows for further
customization of electronic information.
[0011] The aforementioned advertising systems do not allow for
particularly effective personalized marketing. They are very
limited in their ability to present the user with a customized web
site visitation experience, relying primarily on correlating
particular advertisements with certain information known about the
visitor. Customization of the visitation experience is limited to
utilizing information contained in a profile database, which may be
updated as the visitor navigates the web site. However, advertising
is but a small part, and even not the most important part, of a
visitation experience. As an example, what is more important when
visiting a web store is the web store itself, not the advertising
therein. The goal of one-to-one marketing on the web is not to
focus on the advertisements of a web site, but on the visitor
experience at the site itself.
[0012] To date, efforts to create computer-driven network systems
to manage personalized relationships in electronic space have
suffered from the failure to apply the right technology to the
problem. Such systems are unable to provide personalized visitation
experiences to web site visitors which mirror the experiences those
visitors would have in the real world. Human interaction as it
occurs naturally between individuals is extraordinarily complex and
involves aspects of human intelligence such as reasoning, memory,
behavior, and perception. Indeed, successful and mutually rewarding
relationships often involve exploiting these aspects. Translating
features of a successful mutual relationship to a virtual place,
such as over a computer network, to create and foster virtual
mutual relationships is a very complex task. The technology that
allows this problem to be realistically tackled is artificial
intelligence (AI) technology.
[0013] Artificial intelligence systems have been employed in
attempting to provide personalized marketing to visitors of web
sites. However, these attempts have been largely inadequate. For
example, neural network systems, a form of artificial intelligence,
have been used for personalized electronic marketing. Such systems
attempt to emulate a thinking brain and must be trained in order to
process the information with which they are presented. While they
are artificial intelligence systems, they are not expert or
rules-based systems, i.e., they do not use inferencing engines to
apply a set of rules to sets of facts or represented semantically
modeled information to obtain reasoned results. For instance, U.S.
Pat. Nos. 5,774,868 and 5,504,675 (both to Cragun et al.), both
assigned to International Business Machines.RTM., describe sales
promotion systems which utilize neural networks. In the '868
patent, a trained neural network is used to recommend product
purchases to consumers by grouping products into categories and
then analyzing which products a consumer has purchased from that
category. The '675 patent describes a system wherein data is
collected relating to the success of various sales programs. The
collected data is applied to a trained neural network so that the
neural network can select the most appropriate sales program to
run.
[0014] Additionally, there are companies which offer systems which
tailor the visitation experiences of customers by applying business
rules to customer profiles. While these companies may claim to
offer systems which use reasoning capabilities similar to that of a
salesperson in order to better understand a visitor and provide a
unique visitation experience, these systems lack sufficient
artificial intelligence components to effectively establish virtual
mutual relationships that are based on aspects of human
interaction. These systems do not use expert systems technology and
lack the sophistication of this technology.
[0015] Companies such as Net Perceptions.RTM.
(www.netperceptions.com) and Athenium.TM. offer electronic
marketing technology which uses a form of an artificial
intelligence technique known as collaborative filtering. This is a
statistical inferencing technique that attempts to infer the
preferences of individuals by associating them with like
individuals, for example. U.S. Pat. No. 5,918,014 (Robinson)
describes an automated collaborative filtering system for use in
World Wide Web advertising. In this system, statistical inferencing
is employed to group of persons displaying similar likes and
dislikes into communities. If the members of a subject's community
tend to click on a particular web advertisement, then it is
inferred that it is likely that the subject will tend to click on
that advertisement. Using this information, selected advertisements
are presented to the individual. One of the features of this system
is determining the communities that individuals belong to based on
statistical analysis.
[0016] Another use of statistical inferencing techniques is found
in the back-end statistical inferencing used in data mining.
Companies offering data mining systems gather and analyze data on
individuals in order to group individuals according to
similarities. Once individuals have been grouped, inferences are
drawn regarding the products, services, etc. that will appeal to an
individual based on choices made by other individuals in the same
group.
[0017] While collaborative filtering, data mining, and the use of
neural networks provide useful and sophisticated tools for
segmentation, they are limited to but a small aspect of the
problems associated with one-to-one marketing in a non-broadcast
media such as the World Wide Web. As we have seen, advertisement
serving technology is limited to the "foreign context" aspect of a
visitation experience and is very much like traditional broadcast
advertising, simply applied on-line. What is desired is the
application of technology that will achieve the effect of an
on-line cyber-salesperson in all aspects of its complexity. The
"brain emulation" techniques offered by neural networks and
collaborative filtering are technologies that are too immature to
emulate the type of sophisticated human behavior required in such a
salesperson.
[0018] Another weakness of these technologies is that the
relationship with the user is completely one-sided. The user is
watched, data is collected, and users are compared and then
broadcast to, etc. However, there is absolutely no involvement from
the user.
[0019] Thus, until now, personalized marketing using the Internet
has not been particularly successful. The system approaches taken
have been either too limited in their outlook, have used
inappropriate underlying technology, i.e., no AI, or have applied
an ineffective or inefficient Al technology.
[0020] Accordingly, there is a need for a system which can be used
on a computer-driven network which provides for the creation and
maintenance of personalized experiences and which establishes and
augments virtual mutual relationships. There is further a need for
such a system which is intelligent, yet is user friendly and
relatively inexpensive. Still further, there is a need for a system
which can approach the level of sophistication of a
cyber-salesperson through the correct application of available
artificial intelligence technology. The present invention is
directed towards meeting these and other needs.
SUMMARY OF THE INVENTION
[0021] The present invention includes the application of
rules-based and expert systems artificial intelligence technology
to computer-driven network systems which permit the User, i.e., a
Visitor, to experience a personalized Virtual Visit to a Virtual
Place. In contrast to neural networks and collaborative filtering
systems, expert systems technology focuses on emulating captured
expert knowledge and reasoning rather than emulating the brain. The
expert systems of the present invention emulate Real World Experts.
The personalized experience of such a visit permits the creation,
management and fostering of a virtual mutual relationship between
the Visitor and an electronic Enterprise Expert whereby the
satisfaction of needs or interests can be fulfilled, much in the
same manner ordinary human interaction occurs. In a typical visit,
various components of the system interact to: (i) target the
visitor; (ii) dynamically alter the content displayed to the
visitor; and (iii) dynamically optimize the overall experience of
the visitation, among other things.
[0022] A primary goal of such a personalized experience over an
Electronic Communication Network is to establish a mutual
relationship between an Enterprise Expert, for example a virtual
salesperson, a virtual librarian, a virtual physician and the like,
and the Virtual Visitor, i.e. the Virtual Representative of the
actual User. For example, the Virtual Visitor might be the Virtual
Representative of an actual User who is using a personal computer
connected to the Internet to access an electronic site which
deploys the methods and systems of the present invention. By
establishing a virtual mutual relationship, meaningful interaction
which is intended to emulate that of one-on-one human interaction
transpires. The ability to develop the loyalty, trust, and
satisfaction of the customer, which in the above example is the
personal computer User, is a skill which most good sales and
marketing people possess. Balanced against obtaining customer
satisfaction and promoting goodwill is accomplishing the goals of
the Enterprise, which in the context of e-commerce or e-marketing
is the selling of goods or services. The present invention seeks to
develop and maintain a mutual relationship based on these
relationship attributes through the personalization of the
visitation experience as well as by allowing a User control over
her Virtual Representative.
[0023] In one aspect of the present invention is provided a method
for dynamically creating and managing mutual relationships between
a virtual visitor and a virtual enterprise expert on an electronic
network which includes the steps of: (i) Providing a virtual
population, the semantic model of which is rendered specific to one
or more real world populations, the virtual population comprising
instances of said model; (ii) Providing expert system software
which effects a virtual enterprise expert, where the software is
tailored to a particular virtual population; and (iii) Applying the
expert system software to an instance of the semantic model to
create a unique virtual visitation experience. This method may
employ one or more computer systems which include one or more user
site computers, one or more visit site computers, and one or more
population site computers.
[0024] This method may also include the step of providing a billing
system which is capable of billing based on rule success and/or
successful use of real world visitor identification, the real world
visitor identification including identification of population
instances, identification with specific facts, identification with
custom facts, and identification with some number of facts.
[0025] In another aspect of the present invention is provided a
method for dynamically creating and managing mutual relationships
between a virtual visitor and a virtual enterprise expert on an
electronic network which includes the steps of: (i) Providing a
virtual population, the semantic model of which is rendered
specific to one or more real world populations, the virtual
population comprising instances of the model; (ii) Providing
software accessible to a real world enterprise expert, the software
permitting the real world expert to create one or more expert rules
which can be applied to instances of the semantic model; (iii)
Providing expert system software which effects a virtual enterprise
expert, the software tailored to a particular virtual population;
and (iv) Applying the expert system software to an instance of the
semantic model to create a unique virtual visitation experience in
accordance with the real world expert rules, the interests and/or
desires of the visitor, and the expert knowledge of the expert
system software. This method may further include web-based versions
of a rule editor, a report generator, and/or a content management
system.
[0026] In a further aspect of the present invention is provided a
system for dynamically creating and managing mutual relationships
between a virtual visitor and a virtual enterprise expert on an
electronic network which includes: (i) A virtual population, the
semantic model of which is rendered specific to one or more real
world populations, the virtual population including instances of
the model; (ii) Software accessible to a real world enterprise
expert, the software permitting the real world expert to create one
or more expert rules which can be applied to instances of the
semantic model; and (iii) Expert system software which effects a
virtual enterprise expert, the software tailored to a particular
virtual population, wherein the application of the expert system
software to an instance of the semantic model creates a unique
virtual visitation experience in accordance with the real world
rules, the interests and/or desires of the visitor, and the expert
knowledge of the expert system software.
[0027] In a further aspect of the present invention is provided a
system for conducting real-time dynamic marketing on the Internet
which includes: (i) A web site which contains information which can
be dynamically altered and made available to a web site visitor;
(ii) Expert system software which effects a virtual enterprise
expert, the software tailored to a particular virtual population;
and (iii) A stored, retrievable, and updateable virtual population
accessible through a machine-readable database, the semantic model
of which is rendered specific to one or more real world
populations, the virtual population comprising instances of said
model.
[0028] In a still further aspect of the present invention is
provided a system for creating and maintaining a virtual mutual
relationship, the system being accessed by a user through a network
by one or more networked computers which includes: (i) A database
comprising a richly semantically modeled virtual population; (ii)
One or more expert systems in communication with the database and
the software associated with a network site, the expert systems(s)
being capable of performing at least one or more of the following
tasks: (a) applying expert rules to instances of the semantically
modeled population to produce a reasoned result; (b) applying
expert system knowledge to instances of the semantically modeled
population to produce a reasoned result; (c) observing and
understanding its own activity; (d) learning new information as
such information is generated; (e) creating new expert rules
automatically; and (f) reporting on (a)-(e) above.
[0029] For purposes of this invention the following definitions
apply:
[0030] Application Program Interface (API)
[0031] This is a standard term used to describe a programmer access
mechanism to supplied software.
[0032] Electronic Communication Network
[0033] Any electronic environment that allows communications
between computing devices, and/or computing access devices of any
sort. Examples are an Internet Protocol network, a cable network, a
kiosk network, a telephone network, a satellite network, or the
World Wide Web. Computing devices and computing access devices
include personal computers, touch sensitive screens, web TV,
touch-tone telephones, personal digital systems, Virtual Reality
equipment such as gloves, head gear, clothing, sensory devices and
the like, dumb terminals, Java virtual machines, or any other
electronic communication device. Networks include, but are not
limited to, Internets, Intranets, Extranets, Local Area Networks,
Wide Area Networks, and combinations thereof.
[0034] Desirable, the Electronic Communication Network used in the
present invention may be a private IP network, a public IP network,
the World Wide Web, or a combination of public and private IP
networks.
[0035] Enabled Site
[0036] An Enterprise Site which has deployed the technology as
described herein on a computing device in an electronic network.
The computing device can be distributed and made available to
multiple Users. The system of the current invention allows for
maximum flexibility in terms of networked deployment of its
components.
[0037] Enterprise and Customer
[0038] The organizational entity which uses its Site to reach
Visitors for the purpose of furthering its goals. The Enterprise
owner is the Customer or User of most aspects of the present
invention. For example, the Enterprise mentioned in numerous
examples herein are electronic library, e-commerce store, and
virtual town X.
[0039] Enterprise Expert
[0040] A Virtual Expert which has expertise in the goals and/or
business of any particular Enterprise. For example, a
cyber-librarian is an expert about books, libraries, library
searches, etc. As another example, the enterprise expert may be an
e-commerce expert. For instance, a cyber-salesperson is an expert
about selling the goods and services of a business Enterprise or
market area. This expert is effected by an expert system using
expert system and related AI and other software technologies. The
Enterprise Expert may be deployed on a third-party hosted
computer.
[0041] Population Expert
[0042] A Virtual Expert which applies expert knowledge of a
population to achieve additional knowledge about the population for
future reasoning thereon. Such knowledge is represented and
augmented in the Virtual Population. A Virtual Population might be
a population of library users, professionals, consumers, travelers,
etc. On the other hand, the ultimately rendered Semantic Model
would simply be "person" and include views of different facets of a
person. For example, one can be both a consumer, a professional,
and a traveler.
[0043] Real Estate
[0044] User interface areas which can vary under software control.
These areas are components of the Virtual Place of a particular
Enterprise. An example would be the varying areas on web pages
representing the sections of an on-line department store. These
areas will vary under intelligent software control, responding to
the individuality of the virtual consumer entering the store.
Another example would be the rooms in a 3-Dimensional Virtual
Reality library, the shelves on display there, or versions of them
in dark and light wood.
[0045] Real World Expert
[0046] The real world equivalent of a particular expert system. For
example, a salesman is the Real World Expert that corresponds to a
cyber-salesperson.
[0047] Semantic Model
[0048] A model rich in semantics rendered using artificial
intelligence (AI) techniques, referred to herein as a "Rich
Semantic Model". Such models are used to solve problems typically
associated with those requiring human intelligence. Semantic
Modeling is a part of the toolset of AI technology. In the present
invention, Semantic Models may vary to suit the goals of the
Enterprise. For example, income may be less important in the model
of a library Visitor than it would be in the model of a consumer.
The Semantic Model may, for example, be a model of consumers. Also,
the Semantic Model may include facts which reflect whether the real
world visitor is a known or anonymous visitor.
[0049] The Semantic Model may contain facts such as universal
facts, enterprise-specific facts, custom enterprise-specific facts,
and facts which are restricted for use by the real world user.
Universal facts are facts which are potentially available to all
enterprises or customers, e.g., "name", "gender". There is no
reason to restrict this type of fact to a particular enterprise or
enterprise class (e.g., retail or travel enterprise classes).
Enterprise-specific facts are facts which are useful to any
particular enterprise but which differ for each enterprise. For
example, "number of visits" for each of multiple e-Stores may have
a different value. Therefore, such facts are enterprise or customer
specific, although all enterprises use it. Custom
enterprise-specific facts are facts which are of particular use to
a particular customer. For example, "air miles" is a fact which
contains air mile rewards, but only a particular airline has "air
miles".
[0050] Site
[0051] The physical location where components of the Electronic
Communications Network reside. The place where a Virtual Place,
Virtual Population, or Visitor is located. For example, a Virtual
Population may exist at a data center at one Site, and a Visitor
may be at home using a personal computer attached to the World Wide
Web at another. A Virtual Reality holiday cruise ship may reside on
the computers at yet another Site.
[0052] Storage Medium
[0053] Any medium capable of having electronic information stored
thereon and retrieved therefrom. Examples include hard disks,
tapes, compact discs, and the like.
[0054] Virtual Expert or Cyber-Expert
[0055] A virtual electronic expert which has been created by the
use of AI expert systems technology.
[0056] Virtual Place
[0057] A Virtual Visitation location which is representative of a
place in the real world. Examples include a Virtual Reality game
space, the on-line Library of Congress, Jane Smith's home page, or
Amazon.com on the World Wide Web. Any web site is a Virtual
Place.
[0058] Virtual Population
[0059] A group of Virtual Representatives who fit the Semantic
Model which suits the goals of a particular Enterprise, such as an
electronic store, library, etc. For example, consumers, members of
a library, members of an association, etc. constitute Virtual
Populations. They are desirably stored, retrievable, and updateable
through a machine-readable database and are richly semantically
modeled. The Virtual Population may be stored, retrievable, and
updateable at a site where the Virtual Population resides.
[0060] Virtual Reality
[0061] The technology of today and the future that attempts to
create as complete an experience of computer-rendered spaces as
possible, such as allowing 3-Dimensional, sound, and tactile
interfaces. The World Wide Web will become more sophisticated as
known Virtual Reality technology is deployed on networks of
increasing bandwidth. The Web, as it exists for the most part for
most people today, is essentially Virtual Reality technology in its
infancy.
[0062] Virtual Visit
[0063] The experience of the real world Visitor visiting a Virtual
Place. The set of electronic events that comprise the visit.
[0064] Virtual Visitor, Virtual Representative
[0065] An instance of the Virtual Population which functions as a
Virtual Representative for the Visitor. The Virtual Visitor
functions as a Virtual Representative for the real world User at an
Enabled Site.
[0066] Visitor, User
[0067] The real world User of a Visitor Access Device. The real
world person represented by a Virtual Representative.
[0068] Visitor Access Device
[0069] A computer or computing access device.
[0070] Visitor Expert
[0071] An expert that manages the relationship between the Visitor
and the Visitor's Virtual Representative. This expert is an expert
on the Virtual Population Semantic Model and interacts on a
one-to-one basis with Visitors to effect modifications of the
Virtual Representative of the Visitor.
BRIEF DESCRIPTION OF THE DRAWINGS
[0072] FIG. 1 is a schematic presentation of a system of the
present invention showing the Electronic Communications Network,
the Virtual Population Site and database, the Virtual Place Site
with its program interface, and the Visitor at the Visitor Site. It
also shows a Reasoning Component.sub.1, which effects the
Enterprise Expert, and a Reasoning Component.sub.2, which effects a
Population Expert. Reasoning Component.sub.1 has the expert task of
altering the visitation experience of the Visitor. Reasoning
Component.sub.2 has the expert task of learning new information
about the Virtual Population, and thereby enhancing the knowledge
about the Virtual Representatives therein. Reasoning
Component.sub.2 also manages the Virtual Population. The Virtual
Population contains the Virtual Representative of the Visitor and
supplies this information to the Enterprise Expert at the Virtual
Place Site over the Electronic Communications Network.
[0073] FIG. 2 is a continuation of FIG. 1. It repeats the
Electronic Communications Network, the Visitor Site and Virtual
Population Site of FIG. 1. An additional component, Reasoning
Component.sub.3, effects a Visitor Expert. This expert understands
individual instances in the Virtual Population and is accessed by a
program interface that provides the Visitor access to the
corresponding Virtual Representative for the purposes of allowing
the Visitor to access, view, and change the correspondent Virtual
Representative in the Virtual Population. This is done in such a
way as to maximize the value of learned information, maintain the
integrity of the Population, understand and act upon any
ramifications of learned information to the Virtual Population at
large, and present the Virtual Representative to the User in an
easy to understand manner.
[0074] FIG. 3 is a schematic of the system where multiple Sites use
the same Virtual Population. Visitors moving from Virtual Place to
Virtual Place will have their Virtual Representative available for
use automatically by Sites enabled with the system of the present
invention. Further, due to automatic updates to their Virtual
Representative, the most accurate representative is always
available.
[0075] FIG. 4 shows a Reasoning Component in more detail. What is
shown is an expert system technology implementation with a
Reasoning Engine which applies rules to facts to produce judgements
or results. The log files shown are rich in information about all
expert activity and knowledge. In each case, i.e., for Reasoning
Engines 1, 2, or 3, an Inference Engine or Reasoning Engine applies
rules to facts to produce a reasoned result. The engine is
specialized to effect a Site, population, or Visitor Expert in
order to achieve personalization decisions, optimization decisions,
and population knowledge, as well as to update the Semantic Model,
manage Virtual Representative changes under User control, or
suggest new rules.
[0076] FIG. 5 is a schematic emphasizing the dynamic
personalization aspect of one use of the present invention.
Reasoning Component.sub.1, as expanded in FIG. 4, applies expert
knowledge to effect a personalization experience at a Virtual Place
for a particular type of population, such as a consumer population.
As such, Reasoning Component.sub.1 is shown passing judgements to
the Virtual Place Program Interface, thus dynamically altering the
visitation experience of the Visitor. API calls to manage Real
Estate are used within the code of the program interface shown. The
additional arrow connecting the Virtual Place Program Interface
code and the set of Virtual Representatives currently visiting the
Site indicates calls to the Visitor learning API call. Newly
learned Visitor information is added to the resident Virtual
Representatives in real time at the Site and is sent back to the
Virtual Population. This is done through the Virtual Visitor
Manager 42 shown in FIG. 9.
[0077] FIG. 6 is a schematic emphasizing Virtual Population
learning. The Population Expert, tailored to the type of
populations represented, reasons to produce new information about
the population. For example, population purchasing behavior
analysis may produce new observations on consumer preferences.
Another example would be to scan the Virtual Population to learn
likely cross-sell items based on aggregate consumer purchase
behavior information.
[0078] FIG. 7 is a schematic emphasizing the Visitor management end
of the relationship between the Visitor and Virtual Reality
Cyber-Experts. Providing the Visitor access and control over their
Virtual Representative respects the individual, involves the
individual, and gives the individual control over how they are
perceived, what they do and do not want to be known about them, and
how they wish information about them to be shared amongst Enabled
Sites.
[0079] FIG. 8 is a schematic showing the personalization of any
amount of web page Real Estate on any number of pages. Virtual
Visitors are transported over the Internet as they enter a Virtual
Place for the duration of the visit. Plugin software provides web
programmers access to the Enterprise Expert that decides which Real
Estate to retrieve. This Real Estate is completely arbitrary. For
example, it can be an offer to sell, a picture that the Visitor is
likely to enjoy, or a colored background reflecting the Visitor's
known preferences. The Plugin also provides programmer access to
add newly learned information about the Visitor at the Site. This
is explained in more detail in FIG. 9. This new Virtual Visitor
information will be made available in real time to dynamically
alter the visitation experience and is also made available over the
Internet at the Virtual Population Site. A configuration allowing
for access over an Intranet is supplied. In such a case, the
Virtual Population Site and the web site are on the same Enterprise
Network.
[0080] FIG. 9 emphasizes the management of dynamically learned
information about Virtual Visitors. As such, it shows a component
not previously shown, the Virtual Visitor Manager. The Plugin API
provides programmer access to add newly learned information about a
Visitor at the Site via the Virtual Visitor Manager. This new
Virtual Visitor information is added to the Virtual Representative
instantly and is also passed back to the Virtual Population Site.
The Virtual Visitor Manager has a subset of the knowledge of the
Visitor Expert, as it must intelligently add new information to a
Virtual Representative. The Enterprise Expert has instant access to
newly learned information. Any number of tools and methods, such as
click stream analyzers and data mining tools, may be interfaced to
the technology of the present invention by accessing the Virtual
Visitor Manager. The Visitor learning API call, accessible through
the Plugin, provides a software access mechanism that can be used
to update the Virtual Visitor with information learned through
other software tools.
[0081] FIG. 10 shows part of a Tool set provided with the present
invention. A Real World Expert is provided a network-enabled
program and program interface that allows rule editing. This is
desirably a "point-and-click" interface in that allows the expert
to compose rules. The interface can be point-and-click because the
Semantic Model, while changeable, is fixed.
[0082] FIG. 11 shows another part of the Toolset provided with the
present invention. A Real World Expert is provided a
network-enabled program and program interface that allows
reporting. The Enterprise Expert has recorded a vast amount of
information about all of its decisions in the Reasoning Engine log
files, as shown. Examples of such information are all Visitors who
have been at the Site, all Virtual Visitor update requests, and the
like. These log files are accessible to produce reports.
[0083] FIG. 12 shows another part of the Toolset provided with the
present invention. A Real World Expert is provided a program
interface that allows for the editing of the Customer Real Estate
Library of a particular Virtual Place. Real Estate can be added,
re-labeled, annotated, viewed, removed, etc. in a simple fashion.
Labels for the Real Estate are stored by the system and
automatically made available to the Rule Editor.
[0084] FIG. 13 is a schematic representing a different
configuration of the system of FIG. 8. The technology of the
present invention allows for deployment of the components in such a
way as to allow for remote management of most aspects of the
system. In this model, only the requests to the Plugin remain
locally at the Enabled Sites. They are relayed to the remote Site
via a proxy agent, not shown. The system is functionally equivalent
to the system in FIG. 8, the only difference being in distributed
computation.
[0085] FIG. 14 is a schematic of the salient features of
commercially available Ad Serving technology currently used by
others. Web sites control foreign space with Ad Serving technology.
Ad Serving on the Internet or World Wide Web is the equivalent of
traditional advertising in magazines. On the Internet, space is
rented and managed by a third party that manages the foreign ads.
Content providers supply advertisements of many types to be managed
by a central Ad server. Targeting may make use of User profiles in
order to more successfully place the right Ad. This is in sharp
contrast to the present invention, in which web sites are managed
with online cyber-experts.
DETAILED DESCRIPTION OF THE INVENTION
[0086] The following is a brief description of various components
of the present invention:
[0087] Cold Fusion or Other Plugin
[0088] The Plugin provides the interface to the technology that
enables the Virtual Place Site. Cold Fusion or other web
development tools are supported. A particular API call enables the
Reasoning Component to function and return intelligent Real Estate
selection results based on the Real Estate location, knowledge of
the Virtual Visitor, and expert knowledge. Calls to this API are
made in real time as pages load. Another call allows newly learned
knowledge about the Virtual Visitor to be added in real time to the
Virtual Representative at the Virtual Place Site, and in the
Virtual Population at the Virtual Population Site. Newly learned
information is available for instant reasoning thereon. The Plugin
also provides access to the Virtual Population. Plugin components
include a Reasoning Component, the Virtual Visitor Manager, and
Customer Data Store access and maintenance.
[0089] Customer Code with Real Estate Management Calls and Visitor
Learning Calls
[0090] API Calls contained in Customer Web Page Code that utilize
Real Estate personalization software. In one call, the Real Estate
Management Call, the Real Estate to be shown is chosen from the
Customer Real Estate Library. In another API call, the Visitor
Learning Call, newly learned information about the Visitor is added
to the Virtual Representative currently visiting and to the Virtual
Representative at the Virtual Population Site.
[0091] Customer Data Store
[0092] A local data store mechanism used internally by the Plugin
that facilitates the storage and retrieval of all Customer Site
information used by the Plugin or Enterprise Expert's Toolset. The
set of facts comprising currently visiting Virtual Representatives,
the Rule Base, Customer Real Estate Library, and the Reasoning
Component log files are among that which is stored in the Customer
Data Store.
[0093] Customer Real Estate Library
[0094] The Customer Real Estate Library contains the various Real
Estate that is available to display to the User. It is displayed as
a result of the dynamic application of rules to facts. Real Estate
can be supplied by the customer (i.e., e-commerce store) or by the
technology provider (e.g., ResponseLogic.TM.). From the system's
perspective, Real Estate is completely arbitrary. Examples of such
Real Estate include store promotions, backgrounds with specific
colors, surveys, department store headings, personalized messages,
pictures, catalog items, animated displays, and the like. Virtually
anything that can appear on a web page can be stored in the
Customer Real Estate Library. The Library is simply managed by the
Enterprise Expert's Toolset.
[0095] Enterprise Expert's Toolset
[0096] The Enterprise Expert's Toolset consists of a set of tools
that allow for Customer Real Estate Library management, rule
editing, and access to reports. These tools can be web-based and,
as such, can be used over the Internet or over a company Intranet
or Extranet. The tools desirably have a point-and-click interface.
For instance, the Rule Editor component has a point-and-click
interface for entering rules. This is possible because the Semantic
Model for Virtual Visitors is fixed. A Content Library Management
Tool provides a mechanism for storing, viewing, updating,
annotating, and labeling arbitrary Customer Real Estate. This
labeling of content may result in the making of labels which will
be available for rule decisions. Additionally, this content
management system may manage pointers to the content.
[0097] A report generator provides access to a wealth of reports
based on a wide variety of information available from the
Enterprise Expert that sees every Virtual Visitor and can remember
everything it ever showed any such Visitor, including all of the
reasoning that went into this, among other things. The report
generator is desirably capable of reporting one expert activity,
rules used, rule success, rules suggested, and/or learnings. For an
e-commerce use of the present invention, the Enterprise Expert's
Toolset might be called a Marketer's Toolset.
[0098] Fact Base
[0099] The Fact Base is a database of facts about currently
visiting Virtual Visitors. A User's Virtual Representative has been
retrieved from the Virtual Population at the initiation of a
Virtual Visit. A set of facts comprise a model of the Virtual
Visitor. As seen in FIG. 4, each box 48 represents a set of facts
comprising a single Virtual Representative. The set of all these
Virtual Visitors correspond to Visitor currently visiting the
Site.
[0100] Basic facts, preferences and behaviors are stored in a
simple format so that the Inference Engine can apply rules to them.
Some fact examples are "Fact, Client_Visits, 5, 100%" and
"Behavior, Purchases, $350, 100%", and "Preference, Books,
Science". The Fact Base is populated in one of two ways. Upon the
initiation of the Virtual Visit, the Plugin component provides a
mechanism for retrieving the Virtual Representative of the User as
a Virtual Place is entered. Also, as the visit proceeds, new facts
can be added in real time using the API call provided to the
Virtual Place interface programmer.
[0101] Reasoning Component
[0102] This is the core of the expert system. It includes a
Reasoning Engine suited and tailored to its expert task, a Rule
Base, a Fact Base which comprises a set of current Virtual
Visitors, and a set of Reasoning Engine log files.
[0103] The expert system software employed in the present invention
desirably comprises the Reasoning Engine and may be provided as a
software plugin with an Application Programmer Interface. It may
also be provided with an Application Programmer Interface as an
extension of a web programming environment. Additionally,
Application Programmer Interface call may be included which
provides access to a Virtual Enterprise Expert whose judgements
decide upon web page real estate to be shown to a Virtual Visitor.
Further, multiple Application Programmer Interface calls may be
used on multiple web pages to manage web page content and may
provide access to a virtual enterprise expert which may
intelligently add new information to a Virtual Representative. This
new information may be sent over the Electronic Communication
Network to intelligently update a Virtual Population instance
corresponding to a Visitor. The Application Programmer Interface
may also include a parameter which allows for a default piece of
content to be displayed.
[0104] This expert system software desirably effects the expert
systems of the present invention (e.g., the enterprise expert, the
visitor expert, and the population expert, for example) and has
numerous capabilities. For instance, such software is desirably
capable of learning about the Virtual Population, can intelligently
optimize enterprise goals, and is capable of generating new rules.
Such software also may include a set of rules representative of
expert knowledge and may include reasoning engine log information
which may store self-observational expert events and learned
information. This software is also desirably capable of making rule
suggestions and is desirably programmatically accessible to outside
systems. This software is also desirably capable of additional
functions such as rule ordering to control execution.
[0105] In the present invention, the expert system software
includes expert knowledge, and may include a consumer expert, e.g.,
an expert in the marketing and/or selling of goods and services.
The expert system software may be deployed on a customer-hosted
computer.
[0106] Reasoning or Inference Engine
[0107] The Inference Engine applies rules to a Fact Base in order
to respond to one or more Virtual Visitors. It deduces the correct
Real Estate to retrieve from the Customer Real Estate Library when
interfaced by the API call through the Plugin.
[0108] Rule Base
[0109] The Rule Base is a database of rules that are applied to
facts in order to make deductions. An example of a rule would be
"IF Client_Is_Never_Before_Seen THEN Mall Promotion Area IS
Default_MallPromo", or "IF (Client_Visits>5) OR
(Purchases>$60) Play_$10_Gift_Certificate_Promo)".
[0110] A rule is successful if the normal rules of logic make the
clause in the "IF" part of the rule true. Otherwise a rule is
unsuccessful. For example, if a rule says "IF male and >45" and
the Virtual Visitor is female, the rule will fail. The normal rules
of logic apply. A list of conjunctions is true if all parts are
true. A list of disjunctions is true if any part is true, e.g., if
>45 OR <18 is true if either fact is true.
[0111] Virtual Population
[0112] A population of Virtual Visitors is held in a universal
database. This database is accessible to one or more Enabled Sites.
The population is rendered with a Rich Semantic Model.
[0113] Virtual Visitor Manager
[0114] The Virtual Visitor Manager manages the currently visiting
Virtual Population. It loads Virtual Representatives into the
Customer Data Store, adds newly learned information to the set of
current Visitors, and sends newly learned information back to the
Virtual Population Site. The Virtual Visitor Manager has embedded
into it the knowledge of the Visitor Expert required to update a
Virtual Visitor in real time to achieve the same results as the
Visitor Tool achieves.
[0115] Visitor Tool
[0116] This tool allows a Visitor to update her Virtual
Representative. For example, the Virtual Visitor Expert may
interact with the real world Visitor through the Visitor Tool.
Additionally, the Virtual Visitor Expert may interact with the real
world Visitor through the Visitor Tool to restrict access to at
least some information in an instance of the Virtual Population
under the control of the real world User. A Reasoning Component
effects the Visitor Expert that intelligently manages this
engagement online. The goal of the Visitor Expert is to assist in
maintaining a self-consistent and rich model which is as correct
and complete as possible. While the Visitor Tool can add new facts
to maintain a self-consistent profile, the Visitor Tool is more
complex in that it engages in real time interaction with the
Visitor and, as such, can prompt for new and related information,
for example.
[0117] The following is an example of the interaction of certain
components of the present invention to achieve dynamic content and
personalization, dynamic optimization, Visitor learning, and
Visitor management of the relationship, for electronic retail
stores and a population of consumer Visitors on the World Wide
Web.
[0118] One or more electronic stores are enabled with the
technology of the present invention with a simple installation. The
installation includes a Plugin which provides programmers with
access to two API calls that provide Real Estate management and
Visitor learning support. The API may comprise a parameter which
allows for a default piece of content to be displayed should the
system employed in the present invention not be responding. Also
included in the Plugin is a Reasoning Component and other
supporting components that effect the electronic retail Enterprise
Expert. At the electronic store site(s) is also installed the
Enterprise Expert's Toolset which provides web access to a Rule
Editor, Report Generator, and Content Library Management Tool. Both
the Plugin and Enterprise Expert's Toolset are described further
herein.
[0119] In one aspect of the invention, the Plugin provides the API,
the Expert Reasoning Component for the Enterprise, the network
access code that allows access to the Virtual Population, Customer
Data Store access which manages local data store requirements
needed by the systems, and the Virtual Visitor Manager which
manages Virtual Visitor updates.
[0120] A Virtual Population of consumers is available to the
electronic retail stores. The consumer population has been richly
semantically modeled and is available on the network through
supporting software provided by the system of the current
invention. The population has been modeled to account for relevant
consumer knowledge applicable to retail selling. Simple facts,
preferences, and behavior are accounted for in a rich and
changeable, but fixed, model. This model is inherently understood
by the retail site Cyber-Expert which will dynamically use this
knowledge to achieve Enterprise and consumer goals. Instances of
the Virtual Population are either anonymous or known. Known
instances can be edited and managed by the Visitor Tool described
herein. Anonymous members of the Virtual Population do not contain
personally-identifiable information.
[0121] In one configuration of the system of the current invention,
as a Visitor comes into the electronic retail store, the
corresponding Virtual Representative is loaded over the Internet to
the electronic commerce store site and is held in memory until the
Virtual Visit is completed. If the Visitor is anonymous, a cookie
is used to index an anonymous representative from the Virtual
Population. If the Visitor is known, cookies or other technology,
such as e-wallet technology or other infomediary technology, is
used to index the Virtual Population correctly.
[0122] The Rich Semantic Model of the Virtual Population is one
which is rich in meaning. Semantic Modeling is an artificial
intelligence technique for providing rich renderings of complex
things.
[0123] The electronic retail stores have coded their Sites to use
the provided API. Code is added to the web pages of these Sites
wherever it is desirable to have the Enterprise Expert decide what
is the best Real Estate to put on pages. These decisions are made
dynamically as web pages load by the Enterprise Expert. Any amount
of Real Estate on any number of pages can be managed with Real
Estate Calls. The Real Estate Call basically calls the Enterprise
Expert, tells the Expert the area of the page for which it needs a
decision, and for which Virtual Visitor it is personalizing the
experience. The Enterprise Expert uses built-in expert knowledge
(in this case retail knowledge), knowledge about the particular
Virtual Representative, and knowledge of the marketer as expressed
in the rulebase, to decide how best to personalize the Visitor
experience. Any number of Virtual Visitors are managed by the
system of the current invention. The first time that the Content
Management Call is made without a recognized Virtual Visitor, the
Plugin will use the Virtual Visitor Manager to obtain the Virtual
Representative of the Visitor from the Virtual Population.
[0124] New information learned about Visitors at the Site can also
be dynamically added and used while the Virtual Visitor visits at
the electronic store. For example, suppose the marketer (Enterprise
Expert) decides that it is desirable to record preferences for
particular sports and that such a preference can be determined from
past purchase behavior. Programmers can be instructed to use a
second API call of the current invention to record such a
preference at any time during the visit. As another example, new
facts may be learned from a survey which the Visitor fills out at
the Site. The Virtual Visitor Manager, part of the Plugin, manages
Virtual Visitor updates, intelligently updating the Virtual
Representative in real time and sending information updates back to
the Virtual Population. In this way, new information is available
immediately as well as when the Visitor returns or goes to another
Enabled Site. This API call can be used to add or update any aspect
of a selected Virtual Visitor which is instantly made available to
the Enterprise Expert.
[0125] Marketing and other Real World Experts have provided the
Real Estate to be managed by the system of the present invention by
adding this Real Estate to the Customer Real Estate Library using
the Content Library Management Program Interface. The Content
Library Management Tool stores the location, label, and other
information such as annotations for each piece of Real Estate to be
managed by the system. It does not store the Real Estate itself.
Once a Real Estate label has been entered, it is then available as
a Real Estate choice within the Rule Editor. Such Real Estate can
be any code and images that can appear on a web page. For example,
product images, product lists, store departments, offers, and
entire web pages can be managed by the system. Real Estate is
completely arbitrary and can be anything that can appear on a web
page.
[0126] Once the desired Real Estate is entered in the system and
Site coding is complete, the only thing left to do is enter rules,
using the Rule Editor, to be used by the system. These rules are
typically entered by the marketing expert (Enterprise Expert). For
each area to be managed by the system, the area is labeled and
rules are added. This is easy to do as the Rule Editor interface is
desirably point-and-click. This is possible because the Semantic
Model, while changeable, is fixed, and available Real Estate is
known. An example of a rule might be "IF male AND income>$100K
AND frequent buyer THEN MainPagePromotion IS
"FrequentCustomerPromoMale". "MainPagePromotion" is a Real Estate
area on the main page and API calls looking for the correct Real
Estate will refer to it. "FrequentCustomerPromoMale" is a piece of
Real Estate in the Customer Real Estate Library with display
characteristics likely to appeal to males.
[0127] The Rule Editor desirably supports a rule language in
agreement with the Semantic Model and the expert system software
used in the present invention. Additionally, it desirably supports
a simplified rule language which is understood by such expert
system software. The Rule Editor also supports an arbitrary number
of conjunctions and/or disjunctions in the rule language supported
by such expert system software, supports categories, supports a
default rule that succeeds when no other rule has succeeded, and
supports any other typical or natural expert construct available to
such expert system software.
[0128] The rule stated above could also be expressed using
"Preferred Male Customer" if a category was defined in the rule
editor defining "Preferred Customer" as "male AND income>$100K
AND frequent buyer". Real World Experts would use this technique if
they expect to create many rules using such a concept as "preferred
male customer".
[0129] Rule ordering is important. If the marketer or other Real
World Expert adding rules to the system has a goal to sell a
truckload of blue widgets before selling other items, then widget
rules are placed before other rules in a set of rules for a
particular web page area. Only one piece of Real Estate can be
shown in a given area and the first successful rule is used.
[0130] The Visitor Tool of the current invention is used by the
consumer to access and manage her Virtual Representative. The tool
is web-enabled and allows for secure and protected access to the
instance of the Virtual Population that represents her. The tool
can be used to enter a new Virtual Representative, or change the
existing representative or its use. Information can be added or
deleted, and the use of information in the representative can be
managed. For example, it can be specified which retail stores are
allowed to use the information. The Visitor Expert applies expert
Visitor knowledge to interact with the User and assists in
obtaining information about the User that is complete and
self-consistent. The Visitor Expert may also interact with the
Visitor to suggest information-sharing options.
[0131] Naturally, the Visitor Tool only applies to instances in the
population that are not anonymous. Anonymous Virtual
Representatives cannot be accessed by the Visitor Tool.
[0132] A Population Expert applies expert knowledge to the
aggregate Virtual Population to exploit the aggregate knowledge
therein and to mine Visitor similarities and behavior patterns. The
Population Expert is designed to suit particular populations.
[0133] To summarize briefly, content is made dynamic with the
system of the current invention because decisions as to which Real
Estate to display are made in real time by the Enterprise Expert
(e.g., marketing expert) as the electronic store web pages are
displayed. Content is dynamically personalized because the unique
nature of the individual is used throughout the Visitor's Virtual
Visit to the electronic store. Dynamic optimization is achieved
because newly learned information is recorded and applied in real
time as the Visitor is visiting. Dynamic optimization is also
realized because the Enterprise Expert (marketing expert) has
inherent knowledge about retail selling. Additional optimization is
achieved because aggregate information is gained and shared at all
Enabled Sites. Finally, optimization is achieved because the goals
of the Enterprise Expert (marketing expert), as expressed in
entered rules, have been entered to support the goals of the
Enterprise. Aggregate knowledge is exploited and increased by the
Population Expert. Visitor management of the relationship is
afforded through the Visitor Tool and the supporting Visitor Tool
technology of the system of the current invention.
[0134] Turning to FIG. 1, a Visitor 2 at a particular Site accesses
an Electronic Communication Network 8, such as the Internet, using
a Visitor Access Device 6 which is present at a Visitor Site 4. The
Visitor 2 visits a Virtual Place at Site 10, which is desirably
representative of a place in the real world, on the Electronic
Communication Network 8 by interacting with a Virtual Place program
interface 14. For example, the Virtual Place at Site 10 may be a
virtual store, virtual library, virtual country or other such
representation of a real entity. The Visitor 2 exists on the
Electronic Communication Network 8 as a Virtual Visitor. This
Virtual Visitor has a corresponding instance which fits the Rich
Semantic Model of Virtual Population 22. As the individual Visitor
2 accesses an Enabled Site, i.e. a Virtual Place at Site 10 which
has deployed a system and process of the present invention on a
computing device in the Electronic Communication Network 8, the
Visitor 2 is recognized as having previously been to the Enabled
Site or as a first time Visitor. As illustrated in FIG. 5, if the
Visitor 2 has previously visited any Enabled Site, or has a Virtual
Representative in the Virtual Population 22 as a result of having
entered an Enabled Site as show in FIG. 7, a message is sent to the
Virtual Population Site 20 where information relating to her
Virtual Representative resides, and the Virtual Representative of
the Visitor 2 is retrieved. This Virtual Visitor is a specific
Virtual Population 22 instance in the Rich Semantic Model which
contains information about the Visitor 2. This Virtual
Representative of the Visitor 2 is recognized as having
preferences, behavioral information, and simple facts about the
Visitor 2. Samples of these types of information are described
further herein.
[0135] As shown in FIG. 1, the Virtual Place at Site 10 (Enabled
Site) has associated with it expert system technology employing an
Enterprise Expert 16 which includes a Reasoning Component which has
been tailored to a particular Virtual Population 22 and to a
particular Enterprise, of which the Virtual Place at Site 10 is an
example. As shown in FIG. 10, the Reasoning Component is desirably
tailored to an Enabled Site by entering rules 46 which are specific
to the Enterprise. For example, Site 10 may be an electronic retail
store and the Virtual Population is a population of modeled retail
shoppers. As seen in FIG. 4, within a Reasoning Component are
included a set of rules 46, 46', or 46", which are designed to
serve the goals of the Enterprise, and a Reasoning Engine 42, 42',
or 42". Generally, rules 46, 46' or 46" are applied to facts 48 to
achieve judgements 18, 28, or 36, respectively. From within the set
of facts 48, Reasoning Engine.sub.1 applies the description of a
Virtual Representative stored as an instance which conforms to the
Rich Semantic Model of the Virtual Population 22 to the rules 46 to
make a judgement 18. Judgements made personalize the experience of
the Visitor 2 at the Virtual Place at Site 10.
[0136] Referring now to FIG. 5, the Reasoning Engine 42, also
referred to as an Inference Engine, has the function of applying
rules 46, which have been specifically designed to the needs of the
organizational entity which is responsible for the enabled Virtual
Place at Site 10, to facts 48. It reasons by applying the rules 46
to the facts 48 which are retrieved from the Virtual Population 22
at the Virtual Population Site 20. Several sets of such facts 48
correspond to any number of Visitors 2 at any number of Visitor
Sites 4, as shown in FIGS. 3 and 5. In fact, as shown in FIG. 3,
any number of Sites may also access and use the same Virtual
Population 22, such as Sites 40, 40' and 40" enabled with the
technology of the present invention. As shown in FIG. 5, this
results in judgements 18 which are transmitted to the Virtual
Population Site 20 to dynamically update the specific instance
within the Virtual Population 22 and which are used to provide a
customized visitation experience to the Virtual Visitor
representing the Visitor 2. As shown in FIG. 4, the Reasoning
Engine also has associated therewith a log of Reasoning Engine
files 44 which communicate with a Reasoning Engine 42, 42', or 42"
to store data utilized and gained by the Reasoning Engine 42, 42',
or 42". As shown in FIG. 9, Virtual Visitor update requests 68 are
made to the Virtual Population 22 for a Virtual Visitor at Site 10.
The Virtual Visitor is subsequently retrieved 56 from the Virtual
Population 22 and new information about the Visitor 2, represented
by this Virtual Visitor, is then used by the Enterprise Expert 16
to customize the Visitor's 2 visitation experience at the Virtual
Place at Site 10. FIG. 9 also shows an additional component,
Virtual Visitor Manager 42, which is used to dynamically add new
facts to a set of facts 48 comprising a Virtual Visitor. The
requests for such facts updates may come from a program directly or
from judgements 18. Virtual Visitor update requests (programmatic
requests) 68 are achieved through the Plugin interface API 66 and
Virtual Visitor Manager 42. Virtual Visitor Manager 42 also
desirably automatically sends Virtual Visitor (Representative)
updates 54 over the Electronic Communication Network (such as the
Internet) 8 to Virtual Population 22. Subsequent Virtual Visitor
retrieval 56 to any Enabled Site will contain such Virtual Visitor
updates 54.
[0137] Referring again to FIG. 1, the Virtual Population Site 20
desirably also has associated therewith expert system technology
including a Population Expert 26 which includes a Reasoning
Component. As shown in FIG. 4, this Reasoning Component also
includes a set of rules 46' which are applied to facts 48 relating
to the specific instances of the Virtual Population 22 by a
Reasoning Engine 42' to reach a judgement 28 with respect to the
overall Virtual Population 22. This is illustrated diagrammatically
in FIG. 6. The Population Expert 26 applies its expert knowledge of
a Virtual Population 22 to achieve additional knowledge 50 about
the Virtual Population 22 for future reasoning thereon. Thus,
knowledge 50 regarding the Virtual Population 22 augments and
updates population 22 dynamically in accordance with the judgements
28 made by the Population Expert 26. Additionally, judgements 28
regarding the Semantic Model may also be made, which in turn may
augment the model relating to a specific Virtual Population 22. As
an example, likely preferences may be added to all members of a
population based on expert reasoning on that population and may be
added to the Rich Semantic Model of the population. Such judgements
28 have been made in accordance with the expert knowledge of the
population contained in Population Expert 26.
[0138] The present invention is not limited to a particular number
of expert systems, and therefore is not limited to a particular
number of Reasoning Engines or sets of rules which these engines
apply to specific facts. A first Reasoning Component is basic to
the architecture of the present invention and is tailored to a
particular Virtual Population 22 and to a particular Virtual Place
at Site 10. This Reasoning Component contains at least one
Reasoning Engine 42. As stated above, this Reasoning Engine 42 is
used to apply facts 48 which related to a specific instance of the
Virtual Population 22. Rules 46, which are tailored to the Virtual
Place at Site 10, are applied to the facts 48 relating to a
specific instance of the Virtual Population 22 to reach judgements
18. As shown in FIG. 4, these judgements 18 are used to produce
personalized decisions 50 about the specific instance. As a result
of the mutual exchange of information between the Visitor 2 and the
Enterprise Expert 16, the Reasoning Component determines the type
and content of images to display at the Enabled Site 10 which are
viewed by the Visitor 2 at Visitor access device 6.
[0139] Further, as discussed above, a second Reasoning Component is
also tailored to a particular Virtual Population 22. As seen in
FIGS. 1 and 6, this Reasoning Component is also comprised of a
Reasoning Engine 42' and a set of rules 46' designed for a
particular Virtual Population 22. These rules 46' desirably relate
to aggregate knowledge of the Virtual Population 22, as opposed to
specific instances of the Virtual Population 22. The Reasoning
Engine 42' applies these rules 46' to facts 48 about the Virtual
Population 22 and makes judgements 28, representing a gain in
knowledge about the Virtual Population 22 as an aggregate. Sets of
facts 48 comprise the Virtual Population 22, with each set of facts
48 corresponding to a known or anonymous Virtual Visitor. Dynamic
modification or augmentation of the rules 46' for the Virtual
Population 22, or the Virtual Population model per se, can be made
as a result of such reasoning.
[0140] As shown in FIGS. 2 and 4, a Visitor Expert 34 includes a
third Reasoning Component, a set of rules 46"designed for a
particular Virtual Population 22, and a third Reasoning Engine 42"
which is designed to act on the specific facts 48 which describe a
particular Virtual Representative. The Reasoning Engine 42" is
designed to assist the Visitor 2 in modifying her Virtual
Representative by applying these rules 46" to the facts 48 which
describe this Virtual Representative. In this manner, the Virtual
Representative can be directly defined by the Visitor 2 with the
assistance of the Reasoning Component. Judgements 36 modify the
Virtual Representative in accordance with the User's wishes, and
Visitor Expert 34 interacts with the User to maximize the
usefulness, accuracy, and value of information entered. As shown in
FIGS. 2 and 7, a Visitor relationship management program interface
30 permits communication between the Visitor Expert 34 and the
Visitor 2 using access device 6 across an Electronic Communications
Network 8.
[0141] It should be noted that any Reasoning Component has the
intelligence to report on its activities and its understanding of
what it is doing, as well as on newly learned information. Log
files 44, as shown in FIG. 4, are used for this purpose. Newly
learned information can include Visitor learnings and result in
rule suggestions.
[0142] The present invention can also be used to allow for the
management of multiple Virtual Places at Sites 40, 40', and 40"
with the same Virtual Populations 22, as shown in FIG. 3. Sets of
Virtual Visitors 41, 41', and 41" access the Virtual Places at
Sites 40, 40', and 40" through the Electronic Communications
Network 8. In this example, the model for the Virtual Population 22
is the same for all Virtual Places at Enabled Sites 40, 40', and
40". If the Virtual Places at Sites 40, 40', and 40" are all
clothing stores, a specific model may be designed specific to the
Virtual Population 22 which is expected to visit these Sites.
[0143] In one aspect of Me invention, when a Virtual Visitor has
been requested and retrieved from Virtual Population 22, the
Enterprise Expert 16 applies rules 46 to facts 48 using a Reasoning
Engine 42 to produce a personalized Real Estate judgement 58, as
shown in FIG. 8. A Real Estate lot call 64 is placed to the
Reasoning Engine 42 by a Virtual Place at Site 10 that the Virtual
Visitor is visiting. This call is made by a Plugin 60 which applies
the personalized Real Estate judgements 58 made by the Enterprise
Expert 16 to call a Customer Real Estate Library 62 to select
appropriate Real Estate, i.e., a portion of a User interface web
page, to be shown to the Visitor 2 at the Enabled Site. The
particular images shown to the Visitor 2 dynamically change as
judgements 58 are made and processed. Further, calls 68 to update
Virtual Visitors are made at Site 10 using a Plugin API 60. When
these calls are placed, the update request 68 is made over the
Internet or Intranet 8 to the Virtual Population Site 20 which
updates Virtual Visitor in the Virtual Population 22 and also
updates the Virtual Representative at the Virtual Place at Site 10
with changes to appropriate facts 48.
[0144] The present invention may also be used in a configuration to
remotely manage personalized Real Estate for many Customers from a
central location. This is an alternative to Real Estate being
stored at the Customer Site, as in FIG. 8. As shown in FIG. 13,
calls 78 for Real Estate and Virtual Visitor updates are made by
multiple Enabled Sites 72, 72', and 72" across an Electronic
Communications Network 8. These calls are received by a Customer
Service Engine 74 which communicates with the Virtual Population 22
to retrieve instances of the Virtual Population 22. Real estate is
selected from Customer Real Estate libraries 62, 62', and 62" by
the Customer Service Engine 74 based on the selected instances from
the Virtual Population 22. Such instances correspond to current
Visitors 2 at Sites 72, 72' 72" The selected Real Estate is then
sent 76 across the Internet 8 to the appropriate Enabled Sites 72,
72', and 72" where the Real Estate is viewed by the Visitors 2, as
in FIG. 3. Instances of Reasoning Components as in FIG. 4 are all
located at the central Site for each Enabled Site 72, 72', 72".
Each such Enabled Site has a special version of the Plugin 60 which
serves to proxy API calls across the Electronic Communication
Network 8 for remote resolution.
[0145] Referring to FIG. 10, a Real World Expert 71 at a particular
Site 96 accesses an Electronic Communications Network 8, such as
the Internet, using a Real World Expert access device 70 which is
present at Real World Expert Site 96. The Real World Expert 71,
accessing a rule editor program interface 87, accesses a set of
rules 46 for the purpose of editing them. Rules 46 can be added to,
deleted from, and changed in accordance with the purpose and expert
knowledge of Real World Expert 71. Rules 46 can be ordered to suit
expert goals. The language of the rules 46 is supported such that
the rules understand population and Enterprise-specific goals. For
example, a rule language which supports "IF NEVER SEEN BEFORE" is a
useful rule in e-commerce applications.
[0146] Referring to FIG. 11, a Real World Expert 71 at a particular
Site 96 accesses an Electronic Communications Network 8, such as
the Internet, using a Real World Expert access device 70 which is
present at Real World Expert Site 96. The Real World Expert 71,
accessing a reporting tool program interface 88, selects reports
that are to be viewed, printed, or saved on an electronic Storage
Medium. Reports are selected by the Real World Expert 71 in
accordance with the purpose and expert knowledge of the Real World
Expert 71. Software in support of reporting tool program interface
88, which retrieves the correct information from Reasoning Engine
log files 44, may also be present (not shown). A Customer Data
Store (not shown) may also be utilized in the present invention to
store and mange the Reasoning Engine log files.
[0147] Turning to FIG. 12, a Real World Expert 71 at a particular
Site 96 accesses an Electronic Communication Network 8, such as the
Internet, using a Real World Expert access device 70 which is
present at Real World Expert Site 96. The Real World Expert 71,
accessing a content library management program interface 89, adds
Real Estate to Customer Real Estate library 62 by indicating where
such Real Estate reside and how it should be labeled. Software
which adds labels and pointers, as well as additional relevant
information such as annotations, to a Customer Data Store may also
be utilized by the present invention for storage management (not
shown).
[0148] In the examples that follow, e-commerce is the assumed
application. As such, the Enterprise Toolkit is called the
Marketer's Toolkit, and the assumed Virtual Population is a
population of consumers.
EXAMPLE 1
A Known Visitor
[0149] This example illustrates how simple facts, preferences and
behavior are used at a web site enabled with the technology of the
present invention to dynamically offer a unique visitation
experience designed for a particular Virtual Visitor. The Visitor's
name is Beatrice. Beatrice comes into an enabled e-Store
(electronic-Store) website (Site). She is a frequent visitor to
this site and has made several purchases. Because she has made 3 or
more purchases, a promotional incentive is offered to her as she
enters the store. The promotion offers a $30.00 gift certificate if
she will fill out a questionnaire about store services and personal
shopping experience. After she leaves the home page where the offer
appears, Beatrice goes to her favorite section in the store, the
sports section, where she has made many purchases. She buys her
third pair of sport shorts at the store. A new preference for
sports clothing is dynamically added to Beatrice's Virtual
Representative. This representative is a specific instance of a
Virtual Population of consumers that corresponds to Beatrice.
[0150] The population, which includes Beatrice's representative, is
stored in the Virtual Population site in a database. Beatrice's
Virtual Representative has been loaded into memory by a transaction
over a network as she came into the Site. Beatrice may have been
referenced using any number of technologies including e-wallet
technology, various other infomediary technologies, or a cookie
recognition scheme and login procedure. Beatrice's Virtual
Representative changes locally at the e-Store Site to reflect the
new preference instantly, and the newly learned preference for
sports clothing has also been automatically sent to the Virtual
Population site. This makes it available for future visits.
[0151] All information learned, whether it is specific to a
particular e-Store, a particular category of store (e.g., travel or
retail), or universal information about Beatrice such as her name,
is available for instant reasoning while Beatrice visits, and is
made available for subsequent visits because her Virtual
Representative is also updated across the network. After making her
purchase, Beatrice leaves the sports section. Later, she goes back
to the sports section, either on the same date after more shopping
at the e-Store, or at a later date. Because she has an instantly
recognized preference for sports clothing, a new offer appears to
her the next time the sports section page loads. A tee shirt offer
on special appears on the revisited sports section page, where
there previously was no product offer at all.
[0152] This is an e-commerce example of the experience a known
visitor to an enabled web site might have. Beatrice has been
modeled and factual information relating to her preferences,
behavior, and simple facts such as age, income and the like are
stored in Beatrice's Virtual Representative in the Virtual
Population site database. Rules which are specific to the virtual
place e-Store and virtual consumer population were applied by a
reasoning engine to Beatrice's facts comprising her Virtual
Representative to produce a unique visitation experience for
Beatrice. Dynamically, Beatrice's virtual representative is updated
during her visit.
[0153] Beatrice's experience will differ from the experiences of
other visitors, since their Virtual Representatives will contain
different facts relating to their preferences, behavior and simple
factual data, notwithstanding the fact that they each fit the model
designed for a specific Virtual Population. The overall experience
received at Beatrice's access site in the form of information,
images, and sensory perception will depend on Beatrice's previous
activity at the Enterprise, her current interaction with the
Enterprise Expert, and the rules of engagement that the expert
system is currently using.
[0154] As Beatrice uses her web browser to access the home web page
of the e-Store, a plugin is activated by a call to manage real
estate on the e-Store's main page. The call looks as follows:
[0155] IntelligentRealEstateManager (Main Page Real Estate Offer,
Beatrice Id).
[0156] The plugin is provided as a natural extension of a web
environment. For example, as a "CFX" extension in a Cold Fusion
environment, or as a COM or DCOM object in Microsoft's.RTM. Active
Server Pages (ASP) environment.
[0157] Because Beatrice has just arrived, the plugin makes a call
over the network to the database housing Beatrice's Virtual
Representative and her Virtual Representative is sent to the
Enabled Site. Only Beatrice's facts relevant to the present
discussion are shown below although any number of other facts may
be present. The following are stored and referenced in memory as
correlating to Beatrice:
1 Category Attribute Value Certainty Fact Name Beatrice 99%
Behavior Total number of purchases 6 99%
[0158] The "name" fact is considered a "universal" fact and is
available to all Enabled Sites using the consumer Virtual
Population.
[0159] The "total number of purchases" fact is a custom,
customer-specific fact which is available and useful only to the
e-Store that Beatrice is visiting.
[0160] Not shown are information tags that indicate the status of
the facts discussed above and fact category types. The status is
such that this information can be used by this Site because of
Beatrice's expressed control over her information (discussed
below). Also not shown are other facts that are relevant to
categories of e-Stores, such as facts relevant to various
industries such as travel, retail, etc.
[0161] The set of rules in the rule base particular to the e-Store
Beatrice is visiting includes the following rule:
[0162] If total number of purchases>3 THEN Main Page Promotion
Offer Is $30 gift certificate
[0163] It also contains the following rule:
[0164] If There Is A Preference For women's clothes=sports THEN
Sports Page Cross Sell Offer IS Tee Shirt Offer
[0165] We have seen the main page call and we have seen Beatrice's
Virtual Representative. Therefore, we can now understand why
Beatrice will be offered the $30 gift certificate. As the main page
loads, the plugin calls the Inference Engine with Beatrice's ID,
the RuleBase is applied to Beatrice, and the decision is made to
make the offer because it is true that Beatrice has made more than
3 purchases.
[0166] Next, the plugin calls the Customer Real Estate Library
telling it to retrieve the $30 gift certificate. The retrieved Real
Estate is passed back to the main page call to the plugin, the web
page finishes loading, and the $30 gift certificate is
displayed.
[0167] Turning now to learning, when Beatrice makes her third
sports shorts purchase a call is made to add a new fact to the
FactBase comprising the Virtual Representative for Beatrice. The
Web programmer has put this line of code into the shopping cart
code for the e-Store. Since the total number of purchases is also a
fact that is tracked, this behavior fact is also updated. The API
calls to the plugin look like this:
[0168] IntelligentVRBuilder (Beatrice-Id,behavior,total number of
purchases, 1)
[0169] IntelligentVRBuilder (Beatrice Id,preference,women's
clothes, sports)
[0170] The relevant facts for Beatrice now look like this:
2 Category Attribute Value Certainty Fact Name Beatrice 99%
Behavior total number of purchases 7 99% Preference women's clothes
sports 75%
[0171] These API calls update facts that are held in Beatrice's
Virtual Representative at the e-Store. The expert system installed
with the plugin, which is designed specifically to understand
retail selling, is aware that purchases are increasing and adds an
additional purchase to the total.
[0172] By the time Beatrice leaves the shopping cart page, her
Virtual Representative has already been updated at the e-Store with
these two new pieces of information, and her remotely stored
representative is simultaneously updated at the Virtual Population
site via a call over the network. The plugin makes use of the
Virtual Visitor Manager part of the expert system to achieve this.
With Beatrice's profile updated dynamically, changes to it will
change her visitation experience as rules are applied to facts to
produce results every time a page loads.
[0173] When Beatrice goes back to the sportswear page, the rules
are once again applied to the virtual Beatrice which has now been
changed, the result being that a T-shirt Offer is displayed. The
expert system has applied rules to the profile of Beatrice's
Virtual Representative and reached a judgement or decision to make
Beatrice the T-shirt offer. As before, the Customer Real Estate
Library is then contacted. The T-shirt offer is then returned to
the program call that exists on the sportswear section page.
Previously, the following rule was not successful. Beatrice causes
the sports clothing main page to load again and a call is made as
follows as the page loads:
[0174] IntelligentRealEstateManager (Sports Page Cross Sell Offer,
Beatrice Id).
[0175] The RuleBase applies the following rule to the FactBase:
[0176] IF There Is A Preference For women's clothes=sports THEN
Sports Page Cross Sell Offer IS Tee Shirt Offer
[0177] Some facts are customer specific. The "total number of
purchase" fact of this example is a custom, customer specific fact.
It is specific to the e-Store of this example and as such is
retrieved as part of the Virtual Representative of Beatrice as she
visits. On the other hand, the preference fact of this example is
an example of a fact that does not happen to be a customer specific
fact. When Beatrice goes shopping to another Enabled Site, this
preference will enhance her shopping experience wherever it makes
sense to do so (for example a sports e-Mall would likely use this
information about Beatrice). But this will be possible only if
Beatrice has allowed this use of her personal information as
expressed through her interaction with the Visitor Tool.
[0178] Rule ordering is important. Any number of rules can be
present which are relevant to the "Sports Page Cross Sell Offer"
area. The first successful rule in the list of rules for this area
will be the one to succeed. On the other hand, rules which are more
important that others can be ordered by the marketer using this
feature to optimize what is most important to sell, for
example.
EXAMPLE 2
An Unknown Visitor
[0179] In this example, an unknown Visitor, Tamara, comes to the
same e-Store that Beatrice is visiting. This could occur at the
same time as any number of visitors can be handled, each
individually.
[0180] As Tamara enters the e-Store, an attempt to load her Virtual
Representative reveals that there are no relevant facts about her
at all. Her Virtual Representative is empty. Tamara is an unknown
visitor. Nonetheless, the following call to the API serviced by the
plugin will be made:
[0181] IntelligentRealEstateManager (Main Page Real Estate Offer,
Unknown1 Id)
[0182] The ID is Tamara's Id, which is "Unknown 1".
[0183] The following rule is in the RuleBase:
[0184] IF Unknown Visitor THEN Main Page Promotion Offer IS $10
gift certificate for registering
[0185] In the same place where the $30 gift certificate promotion
was shown to Beatrice, (the area identified in the call above as
"Main Page Promotion Offer"), a $10 gift certificate offer is shown
to Tamara. "$10 gift certificate for registering" is shown her in
the hope that she will become a registered customer. Therefore,
even when the information is that there is "no information", the
system is able to exploit this dynamically.
[0186] Tamara decides to fill out the form associated with the gift
certificate, resulting in a whole new list of facts becoming
available for instant reasoning, which will then be used to
dynamically change her visitation experience. These facts are also
sent back to the Virtual Population site and are ready for Beatrice
to view and change, allowing her to specify how she wants them to
be used by other Sites and businesses.
[0187] In the above Examples, the following components may be used
as set forth:
[0188] Marketer Toolset Component
[0189] The marketer's tools are web-based and are intended for use
over the Internet. These tools allow for rule editing, content
management, and access to reports. They are designed to be used by
a marketing person of the enabled site. After the marketer enters
her or his username and password, the main page of the Marketer
Toolset appears. Each tool is described below.
[0190] Marketer Toolset Component: Rule Editor
[0191] The rule editor makes use of the fact that the semantic
model is fixed. This makes it possible for the rule editor to have
a "point and click" style interface. A rule is built by clicking on
the relevant possible choices. For example, set clickable lists are
used to create the following conjunction:
[0192] male AND age>50 AND income>$100,000
[0193] This is possible because the simple facts "gender", "age",
and "income" are part of the Semantic Model. The rules that a
marketer can enter are point and click, which makes the entering of
rules very simple.
[0194] Similarly, what comes after the "THEN" part of a rule is
also selectable from a known list because these are areas which are
made available through the Content Library Manager, described
below. For example:
[0195] THEN Main Page PromoArea
[0196] This may be selected from known lists of all real estate
areas. A new area has to be typed in once.
[0197] What follows the "IS" part of a rule is also available as a
clickable list because this is information which is readily
electronically available from the Content Library, described below.
This tool makes all content available to the system for automatic
retrieval. For example:
[0198] IS $30 Gift Certificate
[0199] "$30 Gift Certificate" can be selected from the known list
of content presented to the user in a clickable form.
[0200] Any number of conjunctions, disjunctions and so on are also
easily supported with the point and click interface, as are special
aspects of the rule language such as "Unknown Visitor".
[0201] Marketer Toolset Component: Report Generator
[0202] The report generator exploits the wealth of information made
available by the expert system that knows every rule that succeeds,
every visitor it sees, any real estate it shows, and all aspects of
all virtual visitors. As such a rich set of reports available by
time period are available. The information used is saved in a
database accessible to the system. Of particular importance is the
set of data the Enterprise Expert stores, making available
information for a user specified time period. Such information
includes, for example, without limitation, the following:
[0203] Number of successful rules per real estate area
[0204] Number of known visitors
[0205] Number of unknown visitors
[0206] Number of visitors with any particular attribute
[0207] Number of virtual visitor updates (and for any particular
attribute)
[0208] Number of preference facts added to virtual representatives
(behavior, simple facts)
[0209] Real estate shown
[0210] Real estate shown to visitors with a particular specified
attribute
[0211] Most successful rules
[0212] Least successful rules
[0213] Rules not used
[0214] Marketer Toolset Component: Content Library Manager
[0215] The Content Library Manager is a very simple tool that
provides the system with access to customer specific content. It
performs tasks including, but not limited to the following:
[0216] Allows for the addition of a real estate, which includes a
label and a location
[0217] Allows for real estate update
[0218] In one aspect of the present invention, it is only necessary
to store pointers to the Real Estate in the scheme above, and not
to store the actual Real Estate. Any content that can be displayed
on a web page can be pointed to and/or stored and used by the
system.
[0219] Visitor Tool
[0220] The Visitor Tool provides web-based real world Visitor
access to their corresponding Virtual Representative in the Virtual
Population. A Visitor Expert conducts the edit/change session with
the User. The Visitor Expert applies rules which understand the
population model. For example, if a Visitor changes the number of
children in the household from 2 to 3, the expert will ask if there
is a newborn in the family. This expert system is desirably
population specific.
[0221] Other Aspects of the System
[0222] As to the extensible semantic model, the system of the
present invention desirably dissociates syntax from semantics and
new attributes can be added at any time and can be understood by
all the components of the system using them. For example, the
various attributes available to the point and click rule editor can
be read dynamically, and a new attribute such as a preference for
sports cars can be added by simply reading the appropriate list of
supported attributes. A new expert system may also need to be added
which has an inherent understanding of this preference. However,
this too can be updated at the same time. The semantic model is
simply not hard-coded.
[0223] A Population Expert, desirably specific to the population
modeled, reasons about the aggregate population. This expert looks
at the entire population and deduces purchasing behavior patterns,
for example, in a population of modeled consumers.
[0224] The examples set forth above serve to illustrate the present
invention, but in no way are intended to limit the spirit and scope
thereof, which is defined by the following claims.
* * * * *