U.S. patent application number 13/337500 was filed with the patent office on 2012-04-26 for temporally sequenced recommendations and associated explanations in subscription-based systems.
This patent application is currently assigned to MANYWORLDS, INC.. Invention is credited to Steven Dennis Flinn, Naomi Felina Moneypenny.
Application Number | 20120101976 13/337500 |
Document ID | / |
Family ID | 38457944 |
Filed Date | 2012-04-26 |
United States Patent
Application |
20120101976 |
Kind Code |
A1 |
Flinn; Steven Dennis ; et
al. |
April 26, 2012 |
Temporally Sequenced Recommendations and Associated Explanations in
Subscription-based Systems
Abstract
A computer-implemented method and system for temporally
sequenced recommendations and associated explanations in
subscription-based systems delivers to users of a
subscription-based system multiple recommended objects that are
arranged in a temporal sequence. The delivered recommended objects
are in accordance with user subscriptions and inferences of
preferences that are based, at least in part, on usage behaviors.
Variations of the system and method include delivering recommended
objects in accordance with the contents of the objects and user
direct feedback with regard to the objects. Information as to why
objects were delivered to users is provided to the users.
Inventors: |
Flinn; Steven Dennis; (Sugar
Land, TX) ; Moneypenny; Naomi Felina; (Houston,
TX) |
Assignee: |
MANYWORLDS, INC.
Houston
TX
|
Family ID: |
38457944 |
Appl. No.: |
13/337500 |
Filed: |
December 27, 2011 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13270049 |
Oct 10, 2011 |
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13337500 |
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11559145 |
Nov 13, 2006 |
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13270049 |
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PCT/US2005/011951 |
Apr 8, 2005 |
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11559145 |
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60572565 |
May 20, 2004 |
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Current U.S.
Class: |
706/58 |
Current CPC
Class: |
G06Q 10/0631 20130101;
G06Q 10/0633 20130101; G06Q 30/0631 20130101; G06F 16/60 20190101;
G06Q 10/06 20130101; G06Q 30/0206 20130101; G06Q 30/0269
20130101 |
Class at
Publication: |
706/58 |
International
Class: |
G06N 5/04 20060101
G06N005/04 |
Claims
1. A computer-implemented method, comprising: generating through
execution of a computer-implemented function on a processor-based
device a plurality of temporally-sequenced recommended objects for
delivery to a user, wherein the recommended objects are generated
in accordance with an inference of a preference that is based, at
least in part, on a plurality of usage behaviors, wherein one
behavior of the plurality of usage behaviors is a subscription; and
providing to the user through execution of a computer-implemented
function a reason for the delivery of at least one object of the
plurality of recommended objects.
2. The method of claim 1, further comprising: generating the
plurality of temporally-sequenced recommended objects, wherein the
recommended objects are generated in accordance with the contents
of at least one object of the plurality of recommended objects.
3. The method of claim 1, further comprising: generating the
plurality of temporally-sequenced recommended objects, wherein the
recommended objects are generated in accordance with a
user-controlled tuning function.
4. The method of claim 1, further comprising: generating the
plurality of temporally-sequenced recommended objects, wherein the
recommended objects are generated in accordance with an inference
of a preference that is based, at least in part, on a physiological
response.
5. The method of claim 1, further comprising: generating the
plurality of temporally-sequenced recommended objects, wherein the
recommended objects are generated in accordance with an inference
of a preference that is based, at least in part, on a direct
feedback behavior.
6. The method of claim 1, further comprising: generating the
plurality of temporally-sequenced recommended objects for delivery
to a user, wherein the recommended objects are generated in
accordance with the inference of the preference that is based, at
least in part, on the plurality of usage behaviors, wherein the one
behavior of the plurality of usage behaviors is the subscription,
wherein the subscription is to a user.
7. The method of claim 1, further comprising: providing the reason
to the user, wherein providing the reason to the user comprises
providing additional explanatory information upon request by the
user.
8. A computer-implemented system, comprising: a
recommendation-generating function executed on a processor-based
device that generates a plurality of recommended objects and
temporally sequences the plurality of objects for delivery to a
user, wherein the recommended objects are generated in accordance
with an inference of a preference that is based, at least in part,
on a plurality of usage behaviors, wherein one behavior of the
plurality of usage behaviors is a subscription; and a
computer-implemented explanatory function that provides a reason to
the user for the delivery of at least one object of the plurality
of recommended objects.
9. The system of claim 8, further comprising: the
recommendation-generating function that generates the plurality of
recommended objects, wherein the recommended objects are generated
in accordance with the contents of at least one object of the
plurality of recommended objects.
10. The system of claim 8, further comprising: the
recommendation-generating function that generates the plurality of
recommended objects, wherein the recommended objects are generated
in accordance with a user-controlled tuning function.
11. The system of claim 8, further comprising: the
recommendation-generating function that generates the plurality of
recommended objects, wherein the recommended objects are generated
in accordance with the inference of a preference that is based, at
least in part, on a physiological response.
13. The system of claim 8, further comprising: the
recommendation-generating function that generates the plurality of
recommended objects, wherein the recommended objects are generated
in accordance with the inference of a preference that is based, at
least in part, on a direct feedback behavior.
14. The system of claim 8, further comprising: the
recommendation-generating function that generates the plurality of
recommended objects, wherein the recommended objects are generated
in accordance with the inference of a preference that is based, at
least in part, on the plurality of usage behaviors, wherein the one
behavior of the plurality of usage behaviors is a subscription,
wherein the subscription is to a user.
15. The system of claim 8, further comprising: an explanatory
function that provides additional explanatory information upon
request by the user.
16. An article comprising a non-transitory computer-readable medium
storing instructions for enabling a processor-based system to:
generate a plurality of temporally-sequenced recommended objects
for delivery to a user, wherein the recommended objects are
generated in accordance with an automatic inference of a preference
that is based, at least in part, on a plurality of usage behaviors,
wherein one behavior of the plurality of usage behaviors is a
subscription; and provide to the user a computer-generated reason
for the delivery of at least one object of the plurality of
recommended objects.
17. The article of claim 16, further storing instructions for
enabling a processor-based system to: generate the plurality of
temporally-sequenced recommended objects, wherein the recommended
objects are generated in accordance with the contents of at least
one object of the plurality of recommended objects.
18. The article of claim 16, further storing instructions for
enabling a processor-based system to: generate the plurality of
temporally-sequenced recommended objects, wherein the recommended
objects are generated in accordance with a user-controlled tuning
function.
19. The article of claim 16, further storing instructions for
enabling a processor-based system to: generate the plurality of
temporally-sequenced recommended objects for delivery to the user,
wherein the recommended objects are generated in accordance with
the automatic inference of the preference that is based, at least
in part, on the plurality of usage behaviors, wherein the one
behavior of the plurality of behaviors is the subscription, wherein
the subscription is to a user.
20. The article of claim 16, further storing instructions for
enabling a processor-based system to: provide the reason to the
user, wherein providing the reason to the user comprises providing
additional explanatory information upon request by the user.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application is a divisional of U.S. patent
application Ser. No. 13/270,049, filed on Oct. 10, 2011, which is a
continuation of U.S. patent application Ser. No. 11/559,145, filed
on Nov. 13, 2006, which is a continuation of International Patent
Application No. PCT/US2005/011951, filed on Apr. 8, 2005, which
claimed benefit under 35 U.S.C. .sctn.119(e) to U.S. Provisional
Patent Application No. 60/572,565, filed May 20, 2004.
FIELD OF THE INVENTION
[0002] This invention relates to extending the business process
paradigm so as to make processes more explicitly adaptive over
time. More specifically, adaptive recombinant processes relates to
processes that automatically structure and re-structure themselves
so as to deliver increasing value to the participants in the
processes over time.
BACKGROUND OF THE INVENTION
[0003] The business process paradigm was first introduced in a
rigorous form by Rummler and Brache in the late 1980's, and was
increasingly popularized by authors such as Michael Hammer, and a
wide range of business consultants, during the 1990's. The terms
"process redesign" or "process reengineering" have been typically
used to denote the explicit establishment of processes that are
optimized for specific business requirements. It should be
understood that although the modifier "business" may be applied to
the term "process" herein, processes are relevant to, and may apply
to, non-business organizations or institutions, as well as
individuals.
[0004] Business processes can be broadly defined as a set of
activities that collectively perform a business function. The
activities within a process are typically performed in a specific
sequence, with the sequence of activities subsequent to any
specified activity being potentially dependent on conditions and
decisions taken at the previous activity step.
[0005] The prior art associated with process design constitutes
developing processes that are optimized for current business
conditions, while attempting to build in enough flexibility in the
design of the process for the process to remain effective if
business conditions change within a limited range over time.
Training of individuals performing tasks within processes is often
a mixture of formalized training, classroom and/or on-line
training, as well as on-the-job experience. In general, however,
the current process paradigm is not one of adaptive processes; that
is, processes that can effectively change as business conditions
change without significant, explicit human redesign efforts, and
processes that adapt to the on-going learning needs, and more
generally, the preferences or interests, of individual participants
in the processes. Specifically, the current process paradigm does
not have a built-in learning mechanism, resulting in a significant
penalty in efficiency and effectiveness.
SUMMARY OF THE INVENTION
[0006] In accordance with the embodiments described herein, a
method and system for adaptive recombinant processes is
disclosed.
[0007] The present invention, "adaptive recombinant processes," is
a method and system for embedding adaptation and learning within
any type of process. Adaptive recombinant processes enable design
and implementation of processes that automatically capture process
participant behaviors associated with the use of, interaction with,
or, most generally, participation in, the associated process. These
process participant behaviors include both individual and community
usage behaviors. The resulting adaptive process can thereby
effectively reconfigure itself on a continuous and potentially
real-time basis, based, at least in part, on inferences of
preferences or interests derived from process interactions by
participants in the process. Such inferences may be conducted on an
automatic or semi-automatic basis; in either case, application of
the inferences can potentially dramatically reduce explicit, manual
process design and redesign efforts. Adaptive recombinant processes
can also dramatically reduce traditional training costs, and
effectively integrates the domains of e-learning and knowledge
management directly within business processes.
[0008] Furthermore, adaptive recombinant processes can enable the
syndication of processes or elements of processes among
organizations, which can then be automatically or
semi-automatically integrated with existing processes or process
elements. This recombinant process approach can significantly
increase process adaptiveness and increase efficiency through the
maximizing of reuse. Furthermore, an evolutionary approach may be
used to create a diversity of processes that can be evaluated
automatically or semi-automatically, and then preferentially
combined based on evaluation results.
[0009] Adaptive recombinant processes enables both increasing the
adaptiveness of existing classes of processes and the enablement of
entirely new types of processes that were not feasible with prior
methods. An example of increasing the adaptiveness of existing
processes is building in "real-time learning" within any instance
of existing classes of processes, to create an adaptive "cockpit"
that facilitates process learning, use and execution. Examples of
new types of processes enabled by adaptive recombinant processes
include processes that are underpinned by syndication and/or
recombination of processes and sub-processes across a series or
network of organizations. Such capabilities may be applied to
facilitate, for example, marketing and business development,
product or service/solution development and delivery, innovation,
coordinated operations, and/or collaborative learning. Specific
examples of new types of processes enabled by adaptive recombinant
processes are adaptive online asset management, adaptive viral
marketing processes, adaptive sales and marketing processes,
adaptive commercial processes such as adaptive product and service
bundling and pricing, processes enabled by location-aware and
collectively adaptive systems, and adaptive publishing
processes.
[0010] Adaptive recombinant processes can apply the fuzzy content
network approach as defined in U.S. Pat. No. 6,795,826, entitled
"Fuzzy Content Network Management and Access," and adaptive
recombinant systems approaches as defined in PCT Patent Application
No. PCT/US04/37176, entitled "Adaptive Recombinant Systems," filed
on Nov. 4, 2004, both of which are incorporated by reference
herein, as if set forth in their entirety.
[0011] Other features and embodiments will become apparent from the
following description, from the drawings, and from the claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] FIGS. 1A and 1B are block diagrams of process and
organization topologies, according to the prior art;
[0013] FIGS. 2A and 2B are block diagrams of sub-processes and
activities, according to the prior art;
[0014] FIG. 3 is a block diagram describing the relationship
between a process and associated supporting content and computer
applications, according to the prior art;
[0015] FIG. 4A is a block diagram of an adaptive process, according
to some embodiments;
[0016] FIG. 4B is a detailed block diagram of the adaptive process
of FIG. 4A, according to some embodiments;
[0017] FIG. 4C is a block diagram of an adaptive recombinant
process, according to some embodiments;
[0018] FIG. 5 is a diagram of the process participant usage
framework, according to some embodiments;
[0019] FIG. 6 is a diagram of process participant communities and
associated relationships, according to some embodiments;
[0020] FIG. 7 is a block diagram of an adaptive system, according
to some embodiments;
[0021] FIG. 8 is a block diagram contrasting the adaptive system of
FIG. 7 with a non-adaptive system, according to some
embodiments;
[0022] FIG. 9A is a block diagram of the structural aspect of the
adaptive system of FIG. 7, according to some embodiments;
[0023] FIG. 9B is a block diagram of the content aspect of the
adaptive system of FIG. 7, according to some embodiments;
[0024] FIG. 9C is a block diagram of the usage aspect of the
adaptive system of FIG. 7, according to some embodiments;
[0025] FIG. 10 is a block diagram of the adaptive recommendations
function used by the adaptive system of FIG. 7, according to some
embodiments;
[0026] FIG. 11 is a block diagram showing structural subsets
generated by the adaptive recommendations function of FIG. 7,
according to some embodiments;
[0027] FIG. 12 is a flow chart showing how recommendations of the
adaptive system of FIG. 7 are generated, whether to support system
navigation and use or to update structural or content aspects of
the adaptive system, according to some embodiments;
[0028] FIG. 13 is a block diagram of a fuzzy network selection
operation, according to some embodiments;
[0029] FIG. 14 is a block diagram of the adaptive system of FIG. 7
in which the structural aspect is a fuzzy network, according to
some embodiments;
[0030] FIG. 15 is a block diagram of a structural aspect including
multiple network-based structures, according to some
embodiments;
[0031] FIG. 16 is a block diagram of an adaptive recombinant
system, according to some embodiments;
[0032] FIG. 17 is a block diagram of the adaptive recombinant
system of FIG. 16 in which the structural aspect is a fuzzy
network, according to some embodiments;
[0033] FIG. 18 is a block diagram of the fuzzy network operators
used by the adaptive recombinant system of FIG. 16, according to
some embodiments;
[0034] FIGS. 19A and 19B are block diagrams of alternative
topologies between fuzzy networks and adaptive processes, according
to some embodiments;
[0035] FIGS. 20A and 20B are block diagrams of a process topic
object and a process content object, respectively, according to
some embodiments;
[0036] FIGS. 21A and 21B are block diagrams of alternative
structures of process activity objects, according to some
embodiments;
[0037] FIGS. 22A and 22B are block diagrams of process activity
networks, according to some embodiments;
[0038] FIGS. 23A and 23B are block diagrams of a process network,
according to some embodiments;
[0039] FIG. 24 is a flow diagram describing structural modification
of the process network of FIGS. 23A and 23B, according to some
embodiments;
[0040] FIG. 25 is a block diagram of a process network selection
operation, according to some embodiments;
[0041] FIG. 26 is a block diagram of a process network syndication
operation, according to some embodiments;
[0042] FIG. 27 is a block diagram of a process network resulting
from a combination of process networks, according to some
embodiments;
[0043] FIG. 28 is a block diagram of the adaptive system of FIG. 7
in which the structural aspect is a process network, according to
some embodiments;
[0044] FIG. 29 is a block diagram of the adaptive recombinant
system of FIG. 16 in which the structural aspect is a process
network, according to some embodiments;
[0045] FIGS. 30A and 30B are block diagrams illustrating
syndication and recombination of process networks and process
network subsets, according to some embodiments;
[0046] FIGS. 31A and 31B are block diagrams illustrating
syndication and recursive recombination of process networks and
process network subsets, according to some embodiments;
[0047] FIG. 32 is a block diagram of the process network
topologies, according to some embodiments;
[0048] FIG. 33 is a block diagram of extensions to the process
network topologies of FIG. 32, according to some embodiments;
[0049] FIG. 34 is a diagram of a process lifecycle framework,
according to some embodiments;
[0050] FIG. 35 is a diagram of process functionality layers,
according to some embodiments;
[0051] FIG. 36 is a diagram of a process lifecycle management
framework, according to some embodiments;
[0052] FIG. 37 is a block diagram of an adaptive asset management
system and process, according to some embodiments;
[0053] FIG. 38 is a block diagram of a real-time learning system
interface, according to some embodiments;
[0054] FIG. 39 is a block diagram of an adaptive system to support
an innovation process, according to some embodiments;
[0055] FIG. 40 is a block diagram of a system and process for
adaptive publishing, according to some embodiments;
[0056] FIG. 41 is a block diagram of a system and process for
adaptive commerce, according to some embodiments;
[0057] FIG. 42 is a block diagram of a system and process for
adaptive price discovery, according to some embodiments;
[0058] FIG. 43 is a block diagram of a system and process for
adaptive commercial solutions, according to some embodiments;
[0059] FIG. 44 is a block diagram of location aware collectively
adaptive systems, according to some embodiments;
[0060] FIG. 45 is a block diagram of a possible configuration of
the location aware collectively adaptive systems of FIG. 44,
according to some embodiments;
[0061] FIG. 46 is a block diagram of an alternative configuration
of the location aware collectively adaptive systems of FIG. 45,
according to some embodiments;
[0062] FIG. 47 is a block diagram of syndication and combination of
content networks within the structural aspect of the adaptive
recombinant system of FIG. 16, according to some embodiments;
[0063] FIG. 48 is a block diagram of syndication and combination of
elements of the structural aspects and usage aspects across
multiple instances of adaptive systems of FIG. 7 within the
adaptive recombinant system of FIG. 16, according to some
embodiments;
[0064] FIGS. 49A and 49B are block diagrams of recursive
syndication and combination of networks of the structural aspects
of the adaptive recombinant systems of FIG. 47 or 48 across
organizations, according to some embodiments;
[0065] FIG. 50 is a block diagram of an evolvable adaptive
recombinant system and process, according to some embodiments;
and
[0066] FIG. 51 is a diagram of alternative computing topologies of
adaptive recombinant processes, according to some embodiments.
DETAILED DESCRIPTION
[0067] In the following description, numerous details are set forth
to provide an understanding of the present invention. However, it
will be understood by those skilled in the art that the present
invention may be practiced without these details and that numerous
variations or modifications from the described embodiments may be
possible.
[0068] In accordance with the embodiments described herein, a
method and a system for development, management and application of
adaptive processes is disclosed.
Processes
[0069] Processes are ubiquitous throughout the business world, and
apply as well to non-business institutions such as government and
non-profit organizations and institutions. In the following
descriptions of processes and the application of adaptive
recombinant processes, business examples will typically be used,
but it should be understood that the descriptions of processes and
related features, and the application of adaptive recombinant
processes, extends to non-business institutions and
organizations.
[0070] Processes can be defined as categorizations of activities,
along with associated inputs and outputs. A process may apply to,
but is not limited to, the following general application areas:
marketing, sales, price determination, innovation, research and
development (R&D), product development, service and solutions
development, business development, tangible or intangible asset
management, manufacturing, supply chain management, logistics and
transportation, procurement, finance and accounting, investment and
portfolio management, human resources, education, entertainment,
information technology, security, military, legal, administrative
processes and business strategy.
[0071] FIGS. 1A, 1B, 2A, 2B and 3 describe prior art and
definitions associated with processes.
[0072] FIG. 1A depicts a business enterprise 110 including a
plurality of processes, a specific example being "process 3" 105. A
business may include one or more processes. It is a typical
practice to determine a number of processes that can be effectively
remembered and managed by people in the associated business--for
example, seven processes (plus or minus two) is a commonly selected
number of processes for an organization. Although not explicitly
shown in FIG. 1A, each process may have one or more linkages to
another process. The linkages may denote a workflow between the
processes, or the linkage may denote an information flow, or a
linkage may denote both workflow and information flow.
[0073] As depicted in FIG. 1B, processes may extend across
businesses or enterprises, or most broadly, organizations. For
example, in FIG. 1B, "Process 8" 120 is shown extending across
"Enterprise A" 110A and "Enterprise B" 110B. It should be
understood that, in general, multiple processes may extend across
multiple enterprises or organizations.
[0074] FIG. 2A illustrates that each process 125 may include one or
more sub-processes. As in the case of processes, sub-processes may
have one or more directed linkages 132 to other sub-processes
within the process, or to processes outside the process within
which the sub-process exists. These external links may constitute
inbound links 132a or outbound links 132d. There may exist a
plurality of links between any two sub-processes, and the plurality
of links may include inbound 132b or outbound links 132c. Although
not explicitly shown in FIG. 2A, each sub-process may contain one
or more other sub-processes, and this recursive decomposition of
sub-processes can continue without limit. It should be noted, as
defined herein, that the only essential distinguishing feature of a
sub-process with regard to a process is that a sub-process is
understood to be a subset of a process. Where the term sub-process
is used herein, it is understood that the term process could be
used without loss of generality.
[0075] FIG. 2B depicts a sub-process. A sub-process 135 is
comprised of other sub-processes (not shown), and/or a series of
activities, for example, "Activity 1" 140. These activities are
conducted by process participants 200. In a business setting, each
activity typically represents a unit of work to be conducted in a
prescribed manner by one or more participants 200 in the process,
and possibly according to a prescribed workflow. However, as
defined herein, an activity may also simply constitute a process
participant 200 action or behavior. For example, a process
participant 200 for a sales process might be a prospective
customer, and a behavior of the prospective customer may constitute
an activity. In such cases a process participant, for example, a
customer or prospective customer, may not be aware that their
behaviors or interactions with a process constitute conducting a
formally defined activity, although from the perspective of another
process participant or the process owner, the activity may
constitute a formally defined activity.
[0076] Participants in a process 200, or "process participants,"
are defined as individuals that perform some activity within a
process, or otherwise interact with a process, or provide input to,
or use the output from, a process or sub-process. For example, a
process participant in a sales process may include sales people
that perform selling activities, but may also include customers or
prospective customers that interact with the sales process,
including the review and consideration of, and/or the purchasing of
goods or services. Further, managers who rely on input from, and/or
provide guidance to, the sales process may be considered process
participants in the sales process. Further, specific actions or
behaviors of the customer or prospective customer may be defined as
activities corresponding to the process or sub-process.
[0077] Although more than one activity is depicted in FIG. 2B, it
should be understood that a process or sub-process may include only
a single activity.
[0078] Any two activities may be linked, which implies a temporal
sequencing or workflow, as for example the linkage 155 between
"Activity 1" 140 and "Activity 2" 150. An activity may be
cross-linked, back linked, or forward linked to more than one other
activity. An activity may contain conditional decisions that
determine which forward links to other activities, such as depicted
by links 155a and 155b, are selected during execution of the
antecedent activity 150. Parallel activities may exist as
represented by "Activity 3" 161 and "Activity 4" 160. Inbound links
145 to activities of the sub-process 135 from other processes,
sub-processes or activities may exist, as well as outbound links
165 from activities of the sub-process 135 to other processes,
sub-processes, or activities.
[0079] FIG. 3 illustrates a general approach to information and
computing infrastructure support for processes. The workflow of
activities within a process or sub-process 168 may be managed by a
computer-based workflow application 169 that enables the
appropriate sequencing of workflow. Each activity, as for example
"Activity 2" 170, may be supported by on-line content or computer
applications 175. On-line content or computer applications 175
include pure content 180, a computer application 181, and a
computer application that includes content 182. Information or
content may be accessed by the sub-process 168 from each of these
sources, shown as content access 180a, information access 181a, and
information access 182a.
[0080] For example, content 180 may be accessed 180a (a content
access 180a) as an activity 170 is executed. Although multiple
activities are depicted in FIG. 3, a process or sub-process may
include only one activity. The term "content" is defined broadly
herein, to include text, graphics, video, audio, multi-media,
computer programs or any other means of conveying relevant
information. During execution of the activity 170, an interactive
computer application 181 may be accessed. During execution of the
activity 170, information 181a may be delivered to, as well as
received from the computer application 181. A computer application
182, accessible by process participants 200 during execution of the
activity 170, and providing and receiving information 182a during
execution of the activity 170, may also contain and manage content
such that content and computer applications and functions that
support an activity 170 may be combined within a computer
application 182. An unlimited number of content and computer
applications may support a given activity, sub-process or process.
A computer application 182 may directly contain the functionality
to manage workflow 169 for the sub-process 168, or the workflow
functionality may be provided by a separate computer-based
application.
Adaptive Processes
[0081] FIGS. 4A and 4B depict the application of adaptive
recommendations to support a process or sub-process, according to
some embodiments. In FIG. 4A, an adaptive process 900 is depicted,
which includes one or more process participants 200, an adaptive
instance of a process or sub-process 930 (hereinafter, adaptive
process instance 930 or process instance 930), and an adaptive
computer-based application 925. In FIG. 4B, the adaptive process
900 may include many of the features of the prior art process in
FIG. 3. Thus, the adaptive process instance 930 features the
workflow application 169, if applicable, with multiple activities
170, one or more of which may be linked. Further, the adaptive
computer-based application 925 is depicted as part of supporting
content and computer applications 175. FIG. 4A provides a broad
overview of the adaptive process 900 while FIG. 4B includes many
more details.
[0082] One or more participants 200 in the adaptive process
instance 930 generate behaviors associated with their participation
in the process instance 930. The participation in the process
instance 930 may include interactions with computer-based systems
181 and content 180, such as content access 180a and information
access 181a, but may also include behaviors not directly associated
with interactions with computer-based systems or content.
[0083] Process participants 200 may be identified by the adaptive
computer-based application 925 through any means of computer-based
identification, including, but not limited to, sign-in protocols or
bio-metric-based means of identification; or through indirect means
based on identification inferences derived from selective process
usage behaviors 920.
[0084] The adaptive process 900 includes an adaptive computer-based
application 925, which includes one or more system elements or
objects, each element or object being executable software and/or
content that is meant for direct human access. The adaptive
computer-based application 925 tracks and stores selective process
participant behaviors 920 associated with a process instance 930.
It should be understood that the tracking and storing of selective
behaviors by the adaptive computer-based application 925 may also
be associated with one or more other processes, sub-processes, and
activities other than the process instance 930, though this is not
explicitly depicted in FIGS. 4A and 4B. In addition to the direct
tracking and storing of selective process usage behaviors, the
adaptive computer-based application 925 may also indirectly acquire
selective behaviors associated with process usage through one or
more other computer-based applications that track and store
selective process participant behaviors.
[0085] FIGS. 4A and 4B also depict adaptive recommendations 910
being generated and delivered by the adaptive computer-based
application 925 to process participants 200. The adaptive
recommendations 910 are shown being delivered to one or more
process participants 200 engaged in "Activity 2" 170 of the
adaptive process instance 930 in FIG. 4B. It should be understood
that the adaptive recommendations 910 may be delivered to process
participants 200 during any activity or any other point during
participation in a process or sub-process.
[0086] The adaptive recommendations 910 delivered by the adaptive
computer-based application 925 are informational or computing
elements or subsets of the adaptive computer-based application 925,
and may take the form of text, graphics, Web sites, audio, video,
interactive content, other computer applications, or embody any
other type or item of information. These recommendations are
generated to facilitate participation in, or use of, an associated
process, sub-process, or activity. The recommendations are derived
by combining the context of what the process participant is
currently doing and the inferred preferences or interests of the
process participant based, at least in part, on the behaviors of
one or more process participants, to generate recommendations. As
the process, sub-process or activity is executed more often by the
one or more process participants, the recommendations adapt to
become increasingly effective. Hence, the adaptive process 900
itself can adapt over time to become increasingly effective.
[0087] Furthermore, the adaptive recommendations 910 may be applied
to automatically or semi-automatically self-modify 905 the
structure, elements, objects, content, information, or software of
a subset 1632 of the adaptive computer-based application 925,
including representations of process workflow. (The terms
"semi-automatic" or "semi-automatically," as used herein, are
defined to mean that the described activity is conducted through a
combination of one or more automatic computer-based operations and
one or more direct human interventions.) For example, the elements,
objects, or items of content of the adaptive computer-based
application 925, or the relationships among elements, objects, or
items of content associated with the adaptive computer-based
application 925 may be modified 905 based on inferred preferences
or interests of one or more process participants. These
modifications may be based solely on inferred preferences or
interests of the one or more process participants 200 derived from
process usage behaviors, or the modifications may be based on
inferences of preferences or interests of process participants 200
from process usage behaviors integrated with inferences based on
the intrinsic characteristics of elements, objects or items of
content of the adaptive computer-based application 925. These
intrinsic characteristics may include patterns of text, images,
audio, or any other information-based patterns.
[0088] For example, inferences of subject matter based on the
statistical patterns of words or phrases in a text-based item of
content associated with the adaptive computer-based application 925
may be integrated with inferences derived from the process usage
behaviors of one or more process participants to generate adaptive
recommendations 910 that may be applied to deliver to participants
in the process, or may be applied to modify 905 the structure of
the adaptive computer-based application 925, including the
elements, objects, or items of content of the adaptive
computer-based application 925, or the relationships among
elements, objects, or items of content associated with the adaptive
computer-based application 925.
[0089] Structural modifications 905 applied to the adaptive
computer-based application 925 enables the structure to adapt to
process participant preferences, interests, or requirements over
time by embedding inferences on these preferences, interests or
requirements directly within the structure of the adaptive
computer-based application 925 on a persistent basis.
[0090] Adaptive recommendations generated by the adaptive
computer-based application 925 may be applied to modify the
structure, including objects and items of content, of other
computer-based systems 175, including the computer-based workflow
application 169, supporting, or accessible by, participants in the
process instance 930. For example, a system that manages workflow
169 may be modified through application of adaptive recommendations
generated by the adaptive computer-based application 925,
potentially altering activity sequencing or other workflow aspects
for one or more process participants associated with the adaptive
process instance 930.
[0091] In addition to adaptive recommendations 910 being delivered
to process participants 200, process participants 200 may also
access or interact 915 with adaptive computer-based application 925
in other ways. The access of, or interaction with, 915 the adaptive
computer-based application 925 by process participants 200 is
analogous to the interactions 182a with computer application 182 of
FIG. 3. However, a distinguishing feature of adaptive process 900
is that the access or interaction 915 of the adaptive
computer-based application 925 by process participants 200 may
include elements 1632 of the adaptive computer-based application
925 that have been adaptively self-modified 905 by the adaptive
computer-based application 925.
[0092] FIG. 4C depicts an extension of the adaptive process 900 of
FIG. 4A in which the adaptive recombinant function 850 is combined
with the adaptive computer-based application 925 to form an
adaptive recombinant computer-based application 925R. The adaptive
recombinant computer-based application 925R enables the management
of multiple computer-based representations of adaptive process or
sub-process instances 930, where each process or sub-process
representation may be in whole or in part. Further, the adaptive
recombinant computer-based application 925R enables the management
of multiple information structures associated with a specific
process instance 930. The management of the representations of
process or sub-process instances 930 and/or multiple information
structures thereof, may include the distribution and combination of
the representations of process or sub-process instances 930 and/or
other information structures, within or across computing systems
and/or organizations. These capabilities enable the adaptive
recombinant process 901.
[0093] For some process applications described herein, adaptive
process 900 is sufficient to implement the application. Other
process applications described herein utilize the additional
adaptive recombinant capabilities 850 provided by the adaptive
recombinant process 901 for full implementation. Notwithstanding
that the term "adaptive recombinant processes" is the general term
used herein to describe the present invention, it should be
understood that in some process application areas, the additional
adaptive recombinant capabilities 850 of the adaptive recombinant
process 901 (that are extensions to the adaptive process
capabilities of the adaptive process 900) are not necessary for
implementation.
Process Participant Behavior Categories
[0094] In Table 1, several different process participant behaviors
920, which may also be described as process "usage" behaviors
without loss of generality, are identified by the adaptive
computer-based application 925 and categorized. The usage behaviors
920 may be associated with the entire community of process
participants, one or more sub-communities, or with individual
process participants or users associated with the sub-process
instance 930.
TABLE-US-00001 TABLE 1 Usage behavior categories and usage
behaviors usage behavior category usage behavior examples
navigation and access activity, content and computer application
accesses, including buying/selling paths of accesses or click
streams subscription and personal of community subscriptions to
self-profiling process topical areas interest and preference
self-profiling affiliation self-profiling (e.g., job function)
collaborative referral to others discussion forum activity direct
communications (voice call, messaging) content contributions or
structural alterations reference personal or community storage and
tagging personal or community organizing of stored or tagged
information direct feedback user ratings of activities, content,
computer applications and automatic recommendations user comments
attention direction of gaze brain patterns physical location
current location location over time relative location to
users/object references
[0095] A first category of process usage behaviors 920 is known as
system navigation and access behaviors. System navigation and
access behaviors include usage behaviors 920 such as accesses to,
and interactions with online computer applications and content such
as documents, Web pages, images, videos, audio, multi-media,
interactive content, interactive computer applications, e-commerce
applications, or any other type of information item or system
"object." These process usage behaviors may be conducted through
use of a keyboard, a mouse, oral commands, or using any other input
device. Usage behaviors 920 in the system navigation and access
behaviors category may include, but are not limited to, the viewing
or reading of displayed information, typing written information,
interacting with online objects orally, or combinations of these
forms of interactions with computer-based applications.
[0096] System navigation and access behaviors may also include
executing transactions, including commercial transactions, such as
the buying or selling of merchandise, services, or financial
instruments. System navigation and access behaviors may include not
only individual accesses and interactions, but the capture and
categorization of sequences of information or system object
accesses and interactions over time.
[0097] A second category of usage behaviors 920 is known as
subscription and self-profiling behaviors. Subscriptions may be
associated with specific topical areas or other elements of the
adaptive computer-based application 925, or may be associated with
any other subset of the adaptive computer-based application 925.
Subscriptions may thus indicate the intensity of interest with
regard to elements of the adaptive computer-based application 925.
The delivery of information to fulfill subscriptions may occur
online, such as through electronic mail (email), on-line
newsletters, XML feeds, etc., or through physical delivery of
media.
[0098] Self-profiling refers to other direct, persistent (unless
explicitly changed by the user) indications explicitly designated
by the one or more process participants regarding their preferences
and interests, or other meaningful attributes. A process
participant 200 may explicitly identify interests or affiliations,
such as job function, profession, or organization, and preferences,
such as representative skill level (e.g., novice, business user,
advanced). Self-profiling enables the adaptive computer-based
application 925 to infer explicit preferences of the process
participant. For example, a self-profile may contain information on
skill levels or relative proficiency in a subject area,
organizational affiliation, or a position held in an organization.
A process participant 200 that is in the role, or potential role,
of a supplier or customer may provide relevant context for
effective adaptive e-commerce applications through self-profiling.
For example, a potential supplier may include information on
products or services offered in his or her profile. Self-profiling
information may be used to infer preferences and interests with
regard to system use and associated topical areas, and with regard
to degree of affinity with other process participant community
subsets. A process participant may identify preferred methods of
information receipt or learning style, such as visual or audio, as
well as relative interest levels in other communities.
[0099] A third category of usage behaviors 920 is known as
collaborative behaviors. Collaborative behaviors are interactions
among the one or more process participants. Collaborative behaviors
may thus provide information on areas of interest and intensity of
interest. Interactions including online referrals of elements or
subsets of the adaptive computer-based application 925, such as
through email, whether to other process participants or to
non-process participants, are types of collaborative behaviors
obtained by the adaptive computer-based application 925.
[0100] Other examples of collaborative behaviors include, but are
not limited to, online discussion forum activity, contributions of
content or other types of objects to the adaptive computer-based
application 925, or any other alterations of the elements, objects
or relationships among the elements and objects of adaptive
computer-based application 925. Collaborative behaviors may also
include general user-to-user communications, whether synchronous or
asynchronous, such as email, instant messaging, interactive audio
communications, and discussion forums, as well as other
user-to-user communications that can be tracked by the adaptive
computer-based application 925.
[0101] A fourth category of process usage behaviors 920 is known as
reference behaviors. Reference behaviors refer to the saving or
tagging of specific elements or objects of the adaptive
computer-based application 925 for recollection or retrieval at a
subsequent time. The saved or tagged elements or objects may be
organized in a manner customizable by process participants. The
referenced elements or objects, as well as the manner in which they
are organized by the one or more process participants, may provide
information on inferred interests of the one or more process
participants and the associated intensity of the interests.
[0102] A fifth category of process usage behaviors 920 is known as
direct feedback behaviors. Direct feedback behaviors include
ratings or other indications of perceived quality by individuals of
specific elements or objects of the adaptive computer-based
application 925, or the attributes associated with the
corresponding elements or objects. The direct feedback behaviors
may therefore reveal the explicit preferences of the process
participant. In the adaptive computer-based application 925, the
adaptive recommendations 910 may be rated by process participants
200. This enables a direct, adaptive feedback loop, based on
explicit preferences specified by the process participant. Direct
feedback also includes user-written comments and narratives
associated with elements or objects of the computer-based system
925.
[0103] A sixth category of process usage behaviors is known as
attention behaviors. These behaviors are associated with the focus
of attention of process participants and/or the intensity of the
intention. For example, the direction of the visual gaze of one or
more process participants may be determined. This behavior can
inform inferences associated with preferences or interests even
when no physical interaction with the adaptive computer-based
application 925 is occurring. Even more direct assessment of the
level of attention may be conducted through access to the brain
patterns or signals associated with the one or more process
participants. Such patterns of brain functions during participation
in a process can inform inferences on the preferences or interests
of process participants, and the intensity of the preferences or
interests. The brain patterns assessed may include MRI images,
brain wave patterns, relative oxygen use, or relative blood flow by
one or more regions of the brain.
[0104] Attention behaviors may include any other type of
physiological response of a process participant 200 that may be
relevant for making preference or interest inferences,
independently, or collectively with the other usage behavior
categories. Other physiological responses may include, but are not
limited to, utterances, gestures, movements, or body position.
Attention behaviors may also include other physiological responses
such as breathing rate, blood pressure, or galvanic response.
[0105] A seventh category of process usage behaviors is known as
physical location behaviors. Physical location behaviors identify
physical location and mobility behaviors of process participants.
The location of a process participant may be inferred from, for
example, information associated with a Global Positioning System or
any other positionally or locationally aware system or device. The
physical location of physical objects referenced by elements or
objects of adaptive computer-based application 925 may be stored
for future reference. Proximity of a process participant to a
second process participant, or to physical objects referenced by
elements or objects of the computer-based application, may be
inferred. The length of time, or duration, at which one or more
process participants reside in a particular location may be used to
infer intensity of interests associated with the particular
location, or associated with objects that have a relationship to
the physical location. Derivative mobility inferences may be made
from location and time data, such as the direction of the process
participant, the speed between locations or the current speed, the
likely mode of transportation used, and the like. These derivative
mobility inferences may be made in conjunction with geographic
contextual information or systems, such as through interaction with
digital maps or map-based computer systems.
[0106] In addition to the usage behavior categories depicted in
Table 1, usage behaviors may be categorized over time and across
user behavioral categories. Temporal patterns may be associated
with each of the usage behavioral categories. Temporal patterns
associated with each of the categories may be tracked and stored by
the adaptive computer-based application 925. The temporal patterns
may include historical patterns, including how recently an element,
object or item of content associated with adaptive computer-based
application 925. For example, more recent behaviors may be inferred
to indicate more intense current interest than less recent
behaviors.
[0107] Another temporal pattern that may be tracked and contribute
to preference inferences that are derived is the duration
associated with the access or interaction with the elements,
objects or items of content of the adaptive computer-based
application 925, or the user's physical proximity to physical
objects referenced by system objects of the adaptive computer-based
application 925, or the user's physical proximity to other process
participants. For example, longer durations may generally be
inferred to indicate greater interest than short durations. In
addition, trends over time of the behavior patterns may be captured
to enable more effective inference of interests and relevancy.
Since adaptive recommendations 910 may include one or more
elements, objects or items of content of the adaptive
computer-based application 925, the usage pattern types and
preference inferencing may also apply to interactions of the one or
more process participants with the adaptive recommendations 910
themselves.
Process Participant Behavior and Usage Framework
[0108] FIG. 5 depicts a usage framework 1000 for performing
preference inferencing of tracked or monitored usage behaviors 920
associated with a process or sub-process instance 930 by the
adaptive computer-based application 925. The usage framework 1000
summarizes the manner in which process usage patterns are managed
within the adaptive computer-based application 925. Usage
behavioral patterns associated with an entire community, affinity
group, or segment of process participants 1002 are captured by the
adaptive computer-based application 925. In another case, usage
patterns specific to an individual, shown in FIG. 5 as individual
usage patterns 1004, are captured by the adaptive computer-based
application 925. Various sub-communities of usage associated with
process participants may also be defined, as for example
sub-community A usage patterns 1006, sub-community B usage patterns
1008, and sub-community C usage patterns 1010.
[0109] Memberships in the communities are not necessarily mutually
exclusive, as depicted by the overlaps of the sub-community A usage
patterns 1006, sub-community B usage patterns 1008, and
sub-community C usage patterns 1010 (as well as and the individual
usage patterns 1004) in the usage framework 1000. Recall that a
community may include a single process participant or multiple
process participants. Sub-communities may likewise include one or
more process participants. Thus, the individual usage patterns 1004
in FIG. 5 may also be described as representing the process usage
patterns of a community or a sub-community. For the adaptive
computer-based application 925, usage behavior patterns may be
segmented among communities and individuals so as to effectively
enable adaptive recommendations 910, 905 for each sub-community or
individual.
[0110] The communities identified by the adaptive computer-based
application 925 may be determined through self-selection, through
explicit designation by other process participants or external
administrators (e.g., designation of certain process participants
as "experts"), or through automatic determination by the adaptive
computer-based application 925. The communities themselves may have
relationships between each other, of multiple types and values. In
addition, a community may be composed not of human users, or solely
of human users, but instead may include one or more other
computer-based systems, which may have reason to interact with the
adaptive computer-based application 925. Or, such computer-based
systems may provide an input into the adaptive computer-based
application 925, such as by being the output from a search engine.
The interacting computer-based system may be another instance of
the adaptive computer-based application 925.
[0111] The usage behaviors 920 included in Table 1 may be
categorized by the adaptive computer-based application 925
according to the usage framework 1000 of FIG. 5. For example,
categories of usage behavior may be captured and categorized
according to the entire community usage patterns 1002,
sub-community usage patterns 1006, and individual usage patterns
1004. The corresponding usage behavior information may be used to
infer preferences and interests at each of the user levels.
[0112] Multiple usage behavior categories shown in Table 1 may be
used by the adaptive computer-based application 925 to make
reliable inferences of the preferences of a process participant
with regard to elements, objects, or items of content associated
with the adaptive computer-based application 925. There are likely
to be different preference inferencing results for different
process participants. In addition, preference inferencing may be
different with regard to optimizing the delivery of adaptive
recommendations 910 to process participants than the preference
inferencing optimized for modifying the structure 905 of the
adaptive computer-based application 925, as modifications to the
structure are likely to be persistent and affect many process
participants.
[0113] As an example, simply using the sequences of content
accesses as the sole relevant usage behavior on which to base
updates to the structure will generally yield unsatisfactory
results. This is because the structure itself, through navigational
proximity, will create a tendency toward certain navigational
access sequence biases. Using just object or content access
sequence patterns as the basis for updates to the structural will
therefore tend to reinforce the pre-existing structure of the
adaptive computer-based application 925, which may limit the
adaptiveness of the adaptive computer-based application 925.
[0114] By introducing different or additional behavioral
characteristics, such as the duration of access of an item of
content, on which to base updates to the structure of adaptive
computer-based application 925, a more adaptive process is enabled.
For example, duration of access will generally be much less
correlated with navigational proximity than access sequences will
be, and therefore provide a better indicator of true user
preferences. Therefore, combining access sequences and access
duration will generally provide better inferences and associated
system structural updates than using either usage behavior alone.
Effectively utilizing additional usage behaviors as described above
will generally enable increasingly effective system structural
updating. In addition, the adaptive computer-based application 925
may employ user affinity groups to enable even more effective
system structural updating than are available merely by applying
either individual (personal) usage behaviors or entire community
usage behaviors.
[0115] Furthermore, relying on only one or a limited set of usage
behavioral cues and signals may more easily enable potential
"spoofing" or "gaming" of the computer-based application 925.
"Spoofing" or "gaming" the adaptive computer-based application 925
refers to conducting consciously insincere or otherwise intentional
usage behaviors 920, so as to influence the adaptive
recommendations 910 or adaptive modifications 905 to the intrinsic
elements and structure of the adaptive computer-based application
925. Utilizing broader sets of system usage behavioral cues and
signals may lessen the effects of spoofing or gaming. One or more
algorithms may be employed by computer-based application 925 to
detect such contrived usage behaviors, and when detected, such
behaviors may be compensated for by the preference and interest
inferencing algorithms of computer-based application 925.
[0116] In some embodiments, the computer-based application 925 may
provide process participants 200 with a means to limit the
tracking, storing, or application of their usage behaviors 920. A
variety of limitation variables may be selected by the process
participant 200. For example, a process participant 200 may be able
to limit usage behavior tracking, storing, or application by usage
behavior category described in Table 1. Alternatively, or in
addition, the selected limitation may be specified to apply only to
particular user communities or individual process participants 200.
For example, a process participant 200 may restrict the application
of the full set of her process usage behaviors 920 to preference or
interest inferences by adaptive computer-based application 925 for
application to only herself, and make a subset of process behaviors
920 available for application to process participants only within
her workgroup, but allow none of her process usage behaviors to be
applied by computer-based application 925 in making inferences of
preferences or interests for other process participants.
Process Participant Communities
[0117] As described above, a process participant associated with an
adaptive process instance 930 may be a member of one or more
communities of interest, or affinity groups, with a potentially
varying degree of affinity associated with the respective
communities. These affinities may change over time as interests of
the user 200 and communities evolve over time. The affinities or
relationships among process participants and communities may be
categorized into specific types. An identified process participant
200 may be considered a member of a special sub-community
containing only one member, the member being the identified process
participant. A process participant can therefore be thought of as
just a specific case of the more general notion of process
participant or user segments, communities, or affinity groups.
[0118] FIG. 6 illustrates the affinities among user communities and
how these affinities may automatically or semi-automatically be
updated by the adaptive computer-based application 925 based on
user preferences which are derived from process participant
behaviors 920. An entire community 1050 is depicted in FIG. 6. The
community may extend across organizational, functional, or process
boundaries. The entire community 1050 extends across process A 1060
and process B 1061. The entire community 1050 includes
sub-community A 1064, sub-community B 1062, sub-community C 1069,
sub-community D 1065, and sub-community E 1070. A process
participant 1063 who is not part of the entire community 1050 is
also featured in FIG. 6.
[0119] Sub-community B 1062 is a community that has many
relationships or affinities to other communities. These
relationships may be of different types and differing degrees of
relevance or affinity. For example, a first relationship 1066
between sub-community B 1062 and sub-community D 1065 may be of one
type, and a second relationship 1067 may be of a second type. (In
FIG. 6, the first relationship 1066 is depicted using a
double-pointing arrow, while the second relationship 1067 is
depicted using a unidirectional arrow.)
[0120] The relationships 1066 and 1067 may be directionally
distinct, and may have an indicator of relationship or affinity
associated with each distinct direction of affinity or
relationship. For example, the first relationship 1066 has a
numerical value 1068, or relationship value, of "0.8." The
relationship value 1068 thus describes the first relationship 1066
between sub-community B 1062 and sub-community D 1065 as having a
value of 0.8.
[0121] The relationship value may be scaled as in FIG. 6 (e.g.,
between 0 and 1), or may be scaled according to another interval.
The relationship values may also be bounded or unbounded, or they
may be symbolically represented (e.g., high, medium, low).
[0122] The process participant 1063, which could be considered a
process participant community including a single member, may also
have a number of relationships to other communities, where these
relationships are of different types, directions and relevance.
From the perspective of the process participant 1063, these
relationship types may take many different forms. Some
relationships may be automatically formed by the adaptive
computer-based application 925, for example, based on interests or
geographic location or similar traffic/usage patterns. Thus, for
example the entire community 1050 may include process participants
in a particular city. Some relationships may be context-relative.
For example, a community to which the process participant 1063 has
a relationship could be associated with a certain process, and
another community could be related to another process. Thus,
sub-community E 1070 may be the process participants associated
with a product development business to which the process
participant 1063 has a relationship 1071; sub-community B 1062 may
be the members of a cross-business innovation process to which the
user 1063 has a relationship 1073; sub-community D 1065 may be
experts in a specific domain of product development to which the
process participant 1063 has a relationship 1072. The generation of
new communities which include the process participant 1063 may be
based on the inferred interests of the process participant 1063 or
other process participants within the entire community 1050.
[0123] Membership of communities may overlap, as indicated by
sub-communities A 1064 and C 1069. The overlap may result when one
community is wholly a subset of another community, such as between
the entire community 1050 and sub-community B 1062. More generally,
a community overlap will occur whenever two or more communities
contain at least one process participant or user in common. Such
community subsets may be formed automatically by the adaptive
process 900, based on preference inferencing from process
participant behaviors 920. For example, a subset of a community may
be formed based on an inference of increased interest or demand of
particular content or expertise of an associated community. The
adaptive computer-based application 925 is also capable of
inferring that a new community is appropriate. The adaptive
computer-based application 925 of the adaptive process 900 will
thus create the new community automatically.
[0124] For each process participant, whether residing within, say,
sub-community A 1064, or residing outside the community 1050, such
as the process participant 1063, the relationships (such as arrows
1066 or 1067), affinities, or "relationship values" (such as
numerical indicator 1068), and directions (of arrows) are unique.
Accordingly, some relationships (and specific types of
relationships) between communities may be unique to each process
participant. Other relationships, affinities, values, and
directions may have more general aspects or references that are
shared among many process participants, or among all process
participants of the adaptive process 900. A distinct and unique
mapping of relationships between process participants, such as is
illustrated in FIG. 6, could thus be produced for each process
participant by the adaptive computer-based application 925.
[0125] The adaptive computer-based application 925 may
automatically generate communities, or affinity groups, based on
process participant behaviors 920 and associated preference
inferences. In addition, communities may be identified by process
participants, such as administrators of the process or sub-process
instance 930. Thus, the adaptive computer-based application 925
utilizes automatically generated and manually generated communities
in generating adaptive recommendations 910, 905.
[0126] The communities, affinity groups, or user segments aid the
adaptive computer-based application 925 in matching interests
optimally, developing learning groups, prototyping process designs
before adaptation, and many other uses. For example, some process
participants that use or interact with the adaptive computer-based
application 925 may receive a preview of a new adaptation of a
process for testing and fine-tuning, prior to other process
participants receiving this change.
[0127] The process participants or communities may be explicitly
represented as elements or objects within the adaptive
computer-based application 925. This feature enhances the
extensibility and adaptability of the adaptive process 900.
Adaptive System
[0128] FIG. 7 depicts a possible configuration of the adaptive
computer-based application 925, as part of the adaptive process 900
of FIGS. 4A and 4B. The adaptive computer-based application 925
includes, at least in part, an adaptive system 100 (shaded for
convenience of identification), according to some embodiments. The
adaptive system 100 includes three aspects: 1) a structural aspect
210, a usage aspect 220, and a content aspect 230. One or more
process participants 200 (who may also be termed "users" of the
adaptive process 900) interact with, or are monitored by, the
adaptive system 100, which tracks selected behaviors 920 of the
process participants, which are in turn selectively stored and
processed by the usage aspect 220. An adaptive recommendations
function 240 generates adaptive recommendations based on inputs
from the usage aspect 220, and, optionally, based on the structural
aspect 210 and/or the content aspect 230. The adaptive
recommendations function 240 determines inferred interests of
process participants 200, and generates adaptive recommendations
250 that may be delivered 910 to process participants 200 or may be
delivered 265 to non-process participants 260. The adaptive
recommendations function 240 may also apply adaptive
recommendations to modify 905 the structural aspect 210 or to
modify 935 the content aspect 230.
[0129] In some embodiments, the adaptive process 900 utilizes the
methods and systems of adaptive fuzzy network and process models,
as defined in U.S. Pat. No. 6,795,826, entitled "Fuzzy Content
Network Management and Access," and PCT Patent Application No.
PCT/US04/37176, entitled "Adaptive Recombinant Systems," filed on
Nov. 4, 2004, which are hereby incorporated by reference as if set
forth in their entirety.
[0130] FIG. 8 contrasts the non-adaptive computer-based application
182 (FIG. 3) with the adaptive computer-based application 925
(FIGS. 4A and 4B). In FIG. 8, an adaptive computer-based
application 925 includes the non-adaptive computer-based
application 182 (FIG. 3), plus other features of the adaptive
system 100 (FIG. 7). The non-adaptive computer-based application
182 includes at least a structural aspect and a content aspect, but
does not include a usage aspect 220 and an adaptive recommendations
function 240, and therefore cannot generate and apply 910, 905, 935
adaptive recommendations. The structural aspect or content aspect
of the non-adaptive computer-based application 182 may be
integrated with a usage aspect 220 and an adaptive recommendation
function 240 to create the adaptive system 100 (FIG. 7), and hence,
the adaptive computer-based application 925. This integration may
be through integration of the associated software functions of the
structural aspect 210 and the content aspect 230 of the
non-adaptive computer-based application 182 with a usage aspect 220
and an adaptive recommendation function 240. Or, the integration
may be effected through transmission of elements of the structural
aspect 210 and the content aspect 230 of the non-adaptive
computer-based application 182 with a second system that contains
usage aspect 220 and an adaptive recommendation function 240.
[0131] As used herein, one or more process participants 200 may be
a single user or multiple users of the adaptive computer-based
application 925. As shown in FIG. 8, the one or more process
participants or users 200 may receive 910 the adaptive
recommendations 250. Individuals not participating in the process
260 of the adaptive system 100 may also receive 265 adaptive
recommendations 250 from the adaptive system 100.
[0132] The process participant or user 200 may be a human entity, a
computer system, or a second adaptive system (distinct from the
adaptive system 100) that interacts with, or otherwise uses the
adaptive computer-based application 925 and the associated adaptive
system 100. The one or more users 200 may include non-human users
of the adaptive system 100. In particular, one or more other
adaptive systems may serve as virtual system "users." These other
adaptive systems may operate in accordance with the architecture of
the adaptive system 100. Thus, multiple adaptive systems may be
mutual users for one another. These adaptive systems may each
support the same process, or each system 100 may each support
different processes.
[0133] It should be understood that the structural aspect 210, the
content aspect 230, the usage aspect 220, and the recommendations
function 240 of the adaptive system 100, and elements of each, may
be contained within one computer, or distributed among multiple
computers. Furthermore, one or more non-adaptive computer-based
applications 182 may be modified to comprise one or more adaptive
systems 100 by integrating the usage aspect 220 and the
recommendations function 240 with the one or more non-adaptive
computer-based applications 182.
[0134] The term "computer system" or the term "system," without
further qualification, as used herein, will be understood to mean
either a non-adaptive or an adaptive system. Likewise, the terms
"system structure" or "system content," as used herein, will be
understood to refer to the structural aspect 210 and the content
aspect 230, respectively, whether associated with the non-adaptive
system 182 or the adaptive computer-based application 925, and
associated adaptive system 100. The term "system structural subset"
or "structural subset," as used herein, will be understood to mean
a portion or subset of the structural aspect 210 of a system.
Structural Aspect
[0135] The structural aspect 210 of the adaptive system 100 is
depicted in the block diagram of FIG. 9A. The structural aspect 210
denotes a collection of system objects 212 that are part of the
adaptive system 100, as well as the relationships among the objects
214. The relationships among objects 214 may be persistent across
user sessions, or may be transient in nature. The objects 212 may
include or reference items of content, such as text, graphics,
audio, video, interactive content, or embody any other type or item
of information. The objects 212 may also include references to
content, such as pointers. Computer applications, executable code,
or references to computer applications may also be stored or
referenced as objects 212 in the adaptive system 100. The content
of the objects 212 is known herein as information 232. The
information 232, though part of the object 214, is also considered
part of the content aspect 230, as depicted in FIG. 9B, and as
described below.
[0136] The objects 212 may be managed in a relational database, or
may be maintained in structures such as flat files, linked lists,
inverted lists, hypertext networks, or object-oriented databases.
The objects 212 may include meta-information 234 associated with
the information 232 contained within, or referenced by the objects
212.
[0137] As an example, in some embodiments, the World-wide Web may
be considered a structural aspect, where web pages constitute the
objects of the structural aspect and links between web pages
constitute the relationships among the objects. Alternatively, or
in addition, in some embodiments, the structural aspect may feature
objects associated with an object-oriented programming language,
and the relationships between the objects associated with the
protocols and methods associated with interaction and communication
among the objects in accordance with the object-oriented
programming language.
[0138] The one or more users 200 of the adaptive system 100 may be
explicitly represented as objects 212 within the system 100,
thereby becoming directly incorporated within the structural aspect
210. The relationships among objects 214 may be arranged in a
hierarchical structure, a relational structure (e.g. according to a
relational database structure), or according to a network
structure.
Content Aspect
[0139] The content aspect 230 of the adaptive system 100 is
depicted in the block diagram of FIG. 9B. The content aspect 230
denotes the information 232 contained in, or referenced by the
objects 212 that are part of the structural aspect 210. The content
aspect 230 of the objects 212 may include text, graphics, audio,
video, and interactive forms of content, such as applets,
tutorials, courses, demonstrations, modules, or sections of
executable code or computer programs. The one or more users 200
interact with the content aspect 230.
[0140] The adaptive system 100 may enable an item of information
232 to be decomposed into other items of information 232. For
example, a text document could be decomposed into sections, each of
which could become separate items of information 232. Further,
these items of information could then become an object 212; that
is, an explicit element of the structural aspect 210. The
decomposition process may also generate appropriate relationships
214 among the decomposed objects, which also become explicit
elements of the structural aspect 210. The recursive decomposition
of information 232 into other information 232 and associated
objects 212 and corresponding relationships among the objects 214
may continue without limit.
[0141] The content aspect 230 may be updated or modified 935 (FIG.
7) by the adaptive recommendations function 240 based, at least in
part, on the usage aspect 220, including usage behavior metrics. To
achieve this, the adaptive system 100 may employ the usage aspect,
or elements of the usage aspect, of other systems. Such systems may
include, but are not limited to, other computer systems, other
networks, such as the World Wide Web, multiple computers within an
organization, other adaptive systems, or other adaptive recombinant
systems. In this manner, the content aspect 230 benefits from usage
occurring in other environments, including other process
environments.
Usage Aspect
[0142] The usage aspect 220 of the adaptive system 100 is depicted
in the block diagram of FIG. 9C. Recall from FIG. 7 that the usage
aspect 220 tracks or monitor usage behaviors 920 of process
participants 200. The usage aspect 220 denotes captured usage
information 202, further identified as usage behaviors 270, and
usage behavior pre-processing 204. The usage aspect 220 thus
reflects the tracking, storing, categorization, and clustering of
the use and associated usage behaviors 920 of the one or more users
or process participants 200 interacting with the adaptive system
100.
[0143] The captured usage information 202, known also as system
usage or system use 202, includes any interaction by the one or
more process participants or users 200 with the system, or
monitored behavior by the one or more users 200. The adaptive
system 100 may track and store user key strokes and mouse clicks,
for example, as well as the time period in which these interactions
occurred (e.g., timestamps), as captured usage information 202.
From this captured usage information 202, the adaptive system 100
identifies usage behaviors 270 of the one or more process
participants 200 (e.g., web page access or physical location
changes of the process participant). Finally, the usage aspect 220
includes usage-behavior pre-processing, in which usage behavior
categories 246, usage behavior clusters 247, and usage behavioral
patterns 248 are formulated for subsequent processing of the usage
behaviors 270 by the adaptive system 100. Some usage behaviors 270
identified by the adaptive system 100, as well as usage behavior
categories 246 designated by the adaptive system 100, are listed in
Table 1, above, and are described in more detail below.
[0144] The usage behavior categories 246, usage behaviors clusters
247, and usage behavior patterns 248 may be interpreted with
respect to a single user 200, or to multiple users 200, in which
the multiple users may be described herein as a community, an
affinity group, or a user segment. These terms are used
interchangeably herein. A community is a collection of one or more
users, and may include what is commonly referred to as a "community
of interest." A sub-community is also a collection of one or more
users, in which members of the sub-community include a portion of
the users in a previously defined community. Communities, affinity
groups, and user segments are described in more detail, below.
[0145] Usage behavior categories 246 include types of usage
behaviors 270, such as accesses, referrals to other users,
collaboration with other users, and so on. These categories and
more are included in Table 1, above. Usage behavior clusters 247
are groupings of one or more usage behaviors 270, either within a
particular usage behavior category 246 or across two or more usage
categories. The usage behavior pre-processing 204 may also
determine new "clusterings" of user behaviors 270 in previously
undefined usage behavior categories 246, across categories, or
among new communities. Usage behavior patterns 248, also known as
"usage behavioral patterns" or "behavioral patterns," are also
groupings of usage behaviors 270 across usage behavior categories
246. Usage behavior patterns 248 are generated from one or more
filtered clusters of captured usage information 202.
[0146] The usage behavior patterns 248 may also capture and
organize captured usage information 202 to retain temporal
information associated with usage behaviors 270. Such temporal
information may include the duration or timing of the usage
behaviors 270, such as those associated with reading or writing of
written or graphical material, oral communications, including
listening and talking, or physical location of the process
participant 200. The usage behavioral patterns 248 may include
segmentations and categorizations of usage behaviors 270
corresponding to a single user of the one or more users 200 or
according to multiple users 200 (e.g., communities or affinity
groups). The communities or affinity groups may be previously
established, or may be generated during usage behavior
pre-processing 204 based on inferred usage behavior affinities or
clustering. Usage behaviors 270 may also be derived from the use or
explicit preferences 252 associated with other adaptive or
non-adaptive systems.
Adaptive Recommendations Function
[0147] Returning to FIG. 7, the adaptive system 100 includes an
adaptive recommendations function 240, which interacts with the
structural aspect 210, the usage aspect 220, and the content aspect
230. The adaptive recommendations function 240 generates adaptive
recommendations 250 based on the application of the usage aspect
220, and, optionally, the structural aspect 210 and/or the content
aspect 230. The adaptive recommendations function 240 may also
optionally apply other contextual information, rules, or algorithms
through the application of other computer-based functions residing
within adaptive system 100, or through access to, or interaction
with, other computer-based functions residing outside of adaptive
system 100.
[0148] The term "recommendations" associated with the adaptive
recommendations function 240 is used broadly in the adaptive system
100. The adaptive recommendations 250 generated by recommendations
function 240 may be displayed or otherwise delivered 910, 265 to a
recommendations recipient. As used herein, a recommendations
recipient is an entity who receives the adaptive recommendations
250. Thus, the recommendations recipient may include the one or
more process participants 200 of the adaptive system 100, as
indicated by the dotted arrow 910 in FIG. 7, or a non-participant
260 of the associated process (see dotted arrow 265). However, the
adaptive recommendations function 240 may also be applied
internally by the adaptive system 100 to update the structural
aspect 210 (see dotted arrow 905). In this manner, the usage
behavior 270 of the one or more process participants 200 may be
influenced by the system structural alterations that are
automatically or semi-automatically applied. Or, the adaptive
recommendations function 240 may be used by the adaptive system 100
to update the content aspect 230 (see dotted arrow 935).
[0149] FIG. 10 is a block diagram of the adaptive recommendations
function 240 used by the adaptive system 100 of FIG. 7. The
adaptive recommendations function 240 includes two algorithms, a
preference inferencing algorithm 242 and a recommendations
optimization algorithm 244. These algorithms (which actually may
include many more than two algorithms) are used by the adaptive
system 100 to generate adaptive recommendations 250.
[0150] Preferably, the adaptive system 100 identifies the
preferences of the user 200 and self-adapts the adaptive system 100
in view of the preferences. Preferences describe the likes, tastes,
partiality, and/or predilection of the user 200 that may be
inferred during access of, interaction with, or while attention is
directed to, the objects 212 of the adaptive system 100. In
general, user preferences exist consciously or sub-consciously
within the mind of the user. Since the adaptive system 100 has no
direct access to these preferences, they are generally inferred by
the preference inferencing algorithm 242 of the adaptive
recommendations function 240.
[0151] The preference inferencing algorithm 242, infers preferences
based, at least in part, on information that may be obtained as the
process participant 200 accesses the adaptive system 100.
Additional information may also be optionally used by the
preference inferencing algorithm 242, including meta-information
234 and intrinsic information 232 within objects 212, and from
information, rules, or algorithms accessed from other
computer-based functions residing within the adaptive system 100,
or through access to, or interaction with, other computer-based
functions residing outside of the adaptive system 100.
[0152] The preference inferencing algorithm and associated output
242 is also described herein generally as "preference inferencing"
or "preference inferences" of the adaptive system 100. The
preference inferencing algorithm 242 identifies three types of
preferences: explicit preferences 252, inferred preferences 253,
and inferred interests 254. Unless otherwise stated, the use of the
term "preferences" herein is meant to include any or all of the
elements 252, 253, and 254 depicted in FIG. 10.
[0153] As used herein, explicit preferences 252 describe explicit
choices or designations made by the user 200 during use of the
adaptive system 100. The explicit preferences 252 may be considered
to more explicitly reveal preferences than inferences associated
with other types of usage behaviors. A response to a survey is one
example where explicit preferences 252 may be identified by the
adaptive system 100.
[0154] Inferred preferences 253 describe preferences of the user
200 that are based on usage behavioral patterns 248. Inferred
preferences 253 are derived from signals and cues made by the
process participant 200, where "signals" are consciously intended
communications by the process participant, and "cues" are behaviors
that are not intended as explicit communications, but nevertheless
provide information of a process participant with which to infer
preferences and interests.
[0155] Inferred interests 254 describe interests of the user 200
that are based on usage behavioral patterns 248. In general, the
adaptive recommendations 250 generated by the adaptive
recommendations function 240 are derived from the preference
inferencing algorithm 242 and combine inferences from overall user
community behaviors and preferences, inferences from sub-community
or expert behaviors and preferences, and inferences from personal
user behaviors and preferences. As used herein, preferences
(whether explicit 252 or inferred 253) are distinguishable from
interests (254) in that preferences imply a ranking (e.g., object A
is better than object B) while interests do not necessarily imply a
ranking.
[0156] A second algorithm 244, designated recommendations
optimization 244, optimizes the adaptive recommendations 250
generated by the adaptive recommendations function 240 within the
adaptive system 100. The adaptive recommendations 250 may be
augmented by automated inferences and interpretations about the
content within individual and sets of objects 232 using statistical
pattern matching of words, phrases or representations, in written
or audio format, or in pictorial format, within the content. Such
statistical pattern matching may include, but is not limited to,
principle component analysis, semantic network techniques, Bayesian
analytical techniques, neural network-based techniques, support
vector machine-based techniques, or other statistical analytical
techniques.
Adaptive Recommendations
[0157] As shown in FIG. 7, the adaptive system 100 generates
adaptive recommendations 250 using the adaptive recommendations
function 240. The adaptive recommendations 250, or suggestions,
enable users to more effectively use and navigate through the
adaptive system 100.
[0158] The adaptive recommendations 250 are presented as structural
subsets of the structural aspect 210. FIG. 11 depicts a
hypothetical structural aspect 210, including a plurality of
objects 212 and associated relationships 214. The adaptive
recommendations function 240 generates adaptive recommendations 250
based on usage of the structural aspect 210 by the one or more
process participants 200, possibly in conjunction with
considerations associated with the structural aspect 210 and the
content aspect 230.
[0159] Three structural subsets 280A, 280B, and 280C (collectively,
structural subsets 280) are depicted. The structural subset 280A
includes three objects 212 and two associated relationships, which
are reproduced by the adaptive recommendations function 240 in the
same form as in the structural aspect 210 (objects are speckle
shaded). The structural subset 280B includes a single object
(object is shaded), with no associated relationships (even though
the object originally had a relationship to another object in the
structural aspect 210).
[0160] The third structural subset 280C includes five objects
(striped shading), but the relationships between objects has been
changed from their orientation in the structural aspect 210. In the
structural subset 280C, a relationship 282 has been eliminated
while a new relationship 284 has been formed by the adaptive
recommendations function 240. The structural subsets 280 depicted
in FIG. 11 represent but three of a myriad of possible structural
subsets that may be derived from the original network of objects by
the adaptive recommendations function 240.
[0161] The illustration in FIG. 11 shows a simplified
representation of structural subsets 280 being generated from
objects 212 and relationships 214 of the structural aspect 210.
Although not shown, the structural subset 280 may also include
corresponding associated subsets of the usage aspect 220, such as
usage behaviors and usage behavioral patterns. As used herein,
references to structural subsets 280 are meant to include the
relevant subsets of the usage aspect, or usage subsets, as
well.
[0162] The adaptive recommendations 250 may be in the context of a
currently conducted activity or behavior detected by the adaptive
system 100, a currently accessed object 232, or a communication
with another process participant 200 or non-participant in the
process 260. The adaptive recommendations 250 may also be in the
context of a historical path of executed system activities,
accessed objects 212, or communications during a specific user
session or across user sessions. The adaptive recommendations 250
may be without context of a current activity, currently accessed
object 212, current session path, or historical session paths.
Adaptive recommendations 250 may also be generated in response to
direct user requests or queries. Such user requests may be in the
context of a current system navigation, access or activity, or may
be outside of any such context.
[0163] Adaptive recommendations 250 generated by the adaptive
recommendations function 240 may combine inferences from community,
sub-community (including expert), and personal behaviors and
preferences, as discussed above, to deliver to the one or more
process participants 200, one or more system structural subsets
280. The process participants 200 may find the structural subsets
particularly relevant given the current navigational context of the
user within the system, the physical location of the user, and/or a
response to an explicit request of the system by the one or more
users. In other words, the adaptive recommendation function 240
determines preference "signals" from the "noise" of system usage
behaviors.
[0164] The sources of user behavioral information, which typically
include the objects 212 referenced by the user 200, may also
include the actual information 232 contained therein. In generating
adaptive recommendations 250, the adaptive system 100 may thus
employ search algorithms that use text matching or more general
statistical pattern matching to provide inferences on the inferred
themes of the information 232 embedded in, or referenced by,
individual objects 212. Furthermore, the structural aspect 210 may
itself inform the specific adaptive recommendations 250 generated.
For example, existing relationship structures within the structural
aspect 210 at the time of the adaptive recommendations 250 may be
combined with the user preference inferences based on usage
behaviors, along with any inferences based on the content aspect
230 (the information 232).
Delivery of Adaptive Recommendations
[0165] FIG. 12 is a flow diagram showing how adaptive
recommendations 250 are delivered by the adaptive system 100.
Recall from FIG. 7 that adaptive recommendations 250 may be
delivered directly to the one or more users 200 (dotted arrow 910),
or the adaptive recommendations function 240 may be applied to
automatically or semi-automatically update the structural aspect
210 (dotted arrow 905) or the content aspect 230 (dotted arrow
935), or adaptive recommendations 250 may be delivered directly to
the non-user 260 of the adaptive system 100 (dotted arrow 265).
[0166] The adaptive system 100 begins by determining the relevant
usage behavioral patterns 248 (FIG. 9C) to be analyzed (block 283).
The adaptive system 100 thus identifies the relevant communities,
affinity groups, or user segments of the one or more process
participants 200. Affinities are then inferred among objects 212,
structural subsets 280, and among the identified affinity groups
(block 284). This data enables the adaptive recommendations
function 240 to generate adaptive recommendations 250 for multiple
application purposes. The adaptive system 100 next determines
whether the adaptive recommendations function 240 will generate
recommendations 250 to be delivered directly to the recommendations
recipients (e.g., 910 to process participants 200 or 265 to
non-participants 260), or are to be used to update the adaptive
system 100 (e.g., 905 to the structural aspect 210 or 935 to the
content aspect 230) (block 285). Where the recommendations
recipients are to directly receive the adaptive recommendations
(the "no" prong of block 285), the adaptive recommendations 250 are
generated based on mapping the context of the current system use
(or "simulated" use if the current context is external to the
actual use of the system) (block 286) to the usage behavior
patterns 248 generated by the preference inferencing algorithm 242
(block 286).
[0167] Adaptive recommendations are then delivered visually and/or
in other communications forms, such as audio, to the
recommendations recipients (block 287). The recommendations
recipients may be individual users or a group of users, or may be
non-users 260 of the adaptive system 100. For Internet-based
applications, the adaptive recommendations 250 may be delivered
through a web browser directly, or through RSS/Atom feeds and other
similar protocols.
[0168] Where, instead, adaptive system 100 itself is to be the
"recipient" of the adaptive recommendations (the "yes" prong of
block 285), the adaptive recommendations function 240 applies the
adaptive recommendations to update the structural aspect 210 (905)
or the content aspect 230 (935). The adaptive recommendations 250
generated by the adaptive recommendations function 240 are
determined based, at least in part, on mapping potential
configurations of the structural aspect 210 or content aspect 230
to the affinities generated by the usage behavioral inferences
(block 288). The adaptive recommendations 905 or 935 are then
delivered to enable updating of the structural aspect 210 or the
content aspect 230 (block 289), respectively.
[0169] The adaptive recommendations function 240 may operate
completely automatically, performing in the background and updating
the structural aspect 210 independent of human intervention. Or,
the adaptive recommendations function 240 may be used by users or
experts who rely on the adaptive recommendations 250 to provide
guidance in maintaining the system structure as a whole, or
maintaining specific structural subsets 280 (semi-automatic
maintenance of the structural aspect 210).
[0170] The navigational context for the recommendation 250 may be
at any stage of navigation of the structural aspect 210 (e.g.,
during the viewing of a particular object 212) or may be at a time
when the recommendation recipient is not engaged in directly
navigating the structural aspect 210. In fact, the recommendation
recipient need not have explicitly used the system associated with
the recommendation 250.
[0171] Some inferences will be weighted as more important than
other inferences in generating the recommendation 250. These
weightings may vary over time, and across recommendation
recipients, whether individual recipients or sub-community
recipients. As an example, the characteristics associated with
objects 212 which are explicitly stored or tagged by the user 200
in a personal structural aspect 210 would typically be a
particularly strong indication of preference as storing or tagging
system structural subsets requires explicit action by the user 200.
The recommendations optimization algorithms 244 may thus prioritize
this type of information to be more influential in driving the
adaptive recommendations 250 than, say, general community traffic
patterns within the structural aspect 210.
[0172] The recommendations optimization algorithm 244 will
particularly try to avoid recommending objects 212 that the process
participant or user 200 is already familiar with. For example, if
the process participant 200 has already stored or tagged the object
212 in a personal structural subset 280, then the object 212 may be
a low ranking candidate for recommendation to the user, or, if
recommended, may be delivered to the user with a designation
acknowledging that the user has already saved or marked the object
for future reference. Likewise, if the user 200 has recently
already viewed the associated system object (regardless of whether
it was saved to his personal system), then the object would
typically rank low for inclusion in a set of recommended
objects.
[0173] The preference inferencing algorithm 242 may be tuned by the
individual user. The tuning may occur as adaptive recommendations
250 are provided to the user, by allowing the user to explicitly
rate the adaptive recommendations. The user 200 may also set
explicit recommendation tuning controls to adjust the adaptive
recommendations to her particular preferences. For example, the
user 200 may guide the adaptive recommendations function 240 to
place more relative weight on inferences of expert preferences
versus inferences of the user's own personal preferences. This may
particularly be the case if the user was relatively inexperienced
in the corresponding domain of knowledge associated with the
content aspect 230 of the system, or a structural subset 280 of the
system. As the user's experience grows, she may adjust the
weighting toward inferences of the user's personal preferences
versus inferences of expert preferences.
[0174] Adaptive recommendations, which are structural subsets of
the adaptive system 100 (see FIG. 11), may be displayed in variety
of ways to the user. The structural subsets 280 may be displayed as
a list of objects 212 (where the list may be null or a single
object). The structural subset 280 may be displayed graphically.
The graphical display may provide enhanced information that may
include depicting relationships among objects (as in the
"relationship" arrows of FIG. 6).
[0175] In addition to the structural subset 280, the recommendation
recipient may be able to access information or logic to assist in
gaining an understanding about why the particular structural subset
was selected as the recommendation to be presented to the user. The
reasoning may be fully presented to the recommendation recipient as
desired by the recommendation recipient, or it may be presented
through a series of interactive queries and associated answers,
where the recommendation recipient desires more detail. The
reasoning may be presented through display of the logic of the
recommendations optimization algorithm 244. A natural language
(e.g., English) interface may be employed to enable the reasoning
displayed to the user to be as explanatory and human-like as
possible.
[0176] The personal preference of the user may affect the nature of
the display of the information. For example some users may prefer
to see the structural aspect in a visual, graphic format while
other users may prefer a more interactive question and answer or
textual display.
[0177] Users of the adaptive system 100, and by extension, process
participants 200, may be explicitly represented as objects in the
structural aspect 210 and hence embodied in structural subsets 280.
Either embodied as structural subsets or represented separately
from structural subsets 280, the adaptive recommendations 250 may
include a set of users of the adaptive system 100 that are
determined and displayed to recommendation recipients, providing
either implicit or explicit permission is granted by the set of
users to be included in the adaptive recommendations 250. The
recommendations optimization algorithm 244 may match the
preferences of other users of the system with the current user. The
preference matching may include applying inferences derived from
the characteristics of structural subsets stored or tagged by
users, their structural subset subscriptions and other
self-profiling information, and their system usage patterns 248.
Information about the recommended set of users may be displayed.
This information may include names, as well as other relevant
information such as affiliated organization and contact
information. The information may also include system usage
information, such as common system objects subscribed to, etc. As
in the case of structural subset adaptive recommendations, the
adaptive recommendations of other users may be tuned by an
individual user through interactive feedback with the adaptive
system 100.
[0178] The adaptive recommendations 250 may be in response to
explicit requests from the user. For example, a user may be able to
explicitly designate one or more objects 212 or structural subsets
280, and prompt the adaptive system 100 for a recommendation based
on the selected objects or structural subsets. The recommendations
optimization algorithm 244 may put particular emphasis on the
selected objects or structural subsets, in addition to applying
inferences on preferences from usage behaviors, as well as
optionally, content characteristics.
[0179] In some embodiments, the adaptive recommendations function
240 may augment the preference inferencing algorithm 242 with
considerations related to enhancing the revelation of user
preferences, so as to better optimize the adaptive recommendations
250 in the future. In other words, where the value of information
associated with reducing uncertainty associated with user
preferences is high, the adaptive recommendations function 240 may
choose to recommend objects 212 or other recommended structural
aspects 210 as an "experiment." For example, the value of
information will typically be highest for relatively new users, or
when there appears to be a significant change in usage behavioral
pattern 248 associated with the user 200. The adaptive
recommendations function 240 may employ design of experiment (DOE)
algorithms so as to select the best possible "experimental"
adaptive recommendations, and to optimally sequence such
experimental adaptive recommendations, and to adjust such
experiments as additional usage behaviors 270 are assimilated. In
some embodiments, the adaptive recommendations function 240 may
apply methods and systems disclosed in U.S. Provisional Patent
Application Ser. No. 60/652,578, entitled "Adaptive Decision
Process," filed Feb. 14, 2005, which is incorporated by reference
herein, as if set forth in its entirety.
[0180] The preference inferencing 242 and recommendations
optimization 244 algorithms may also preferentially deliver content
that is specially sponsored; for example, promotional, advertising
or public relations-related content.
[0181] In summary, the adaptive recommendations generated by the
adaptive recommendations function 240 may be delivered 910 to the
users 200, delivered 265 to the non-user 260, or delivered 905, 935
back to the adaptive system 100, for updating either the structural
aspect 210 (905) or the content aspect 230 (935). The adaptive
recommendations 250 generated by the adaptive recommendations
function 240 will thus influence subsequent user interactions and
behaviors associated with the adaptive system 100, creating a
dynamic feedback loop.
Automatic or Semi-Automatic System Structure Maintenance
[0182] The adaptive recommendations function 240, optionally in
conjunction with system structure maintenance functions that reside
within, or are accessible by, the adaptive computer-based
application 925 (not shown), may be used to automatically or
semi-automatically update and enhance the structural aspect 210 of
the adaptive system 100. The adaptive recommendations function 240
may be employed to determine new relationships 214, or modify
existing relationships 214, among objects 212 in the adaptive
system, within structural subsets 280, or among structural subsets
associated with a specific sub-community. The automatic updating
may include potentially assigning a relationship between any two
objects to zero (effectively deleting the relationship between the
two objects). The modified relationships may represent the workflow
sequencing among objects within the structural aspect 210, where
objects represent a process, sub-process or activity.
[0183] In either an autonomous mode of operation, or in conjunction
with human expertise, the adaptive recommendations function 240 may
be used to integrate new objects 212 into the structural aspect
210, or to delete existing objects 212 from the structural
aspect.
[0184] The adaptive recommendations function 240 may also be
extended to scan and evaluate structural subsets 280 that have
special characteristics. For example, the adaptive recommendations
function 240 may suggest that certain of the structural subsets
that have been evaluated are candidates for special designation.
This may include being a candidate for becoming a new specially
designated sub-system or structural subset. The adaptive
recommendations function 240 will present to human users or experts
the structural subset 280 that is suggested to become a new
sub-system or structural subset, along with existing sub-system or
structural subsets that are deemed "closest" in relationship to the
new suggested structural subset. A human user or expert may then be
invited to add the object or objects 212, and may manually create
relationships 214 between the new object and existing objects.
[0185] As another alternative, the adaptive recommendations
function 240, optionally in conjunction with the system structure
maintenance functions, may automatically generate the object or
objects 212, and may automatically generate the relationships 214
between the newly created object and other objects 212 in the
structural aspect 210.
[0186] This capability is extended such that the adaptive
recommendations function 240, in conjunction with system structure
maintenance functions, automatically maintains the structural
aspect and identified structural subsets 280. The adaptive
recommendations function 240 may identify new objects 212, generate
associated objects 212, and generate associated relationships 214
among the new objects 212 and existing objects 212, but also may
identify objects 212 that are candidates for deletion. The adaptive
recommendations function 240 may also automatically delete the
object 212 and its associated relationships 214.
[0187] The adaptive recommendations function 240, in conjunction
with system structure maintenance functions, may apply "global"
considerations and logic when conducting modifications to the
structural aspect 210 to ensure effective use and navigation of the
structural aspect 210. For example, thresholds or limits may guide
the absolute number or relative number of relationships among
objects. Similarly, rules may be applied to the number of elements
in the structural aspect 210 as a whole, or within designated
subsets of structural aspect 210. Rules related to the duration an
object 212 has been incorporated within the structural aspect 210,
or collective quality thresholds for objects 212 may also be
applied. These global rules help ensure that adaptive system 100
performs at an optimum possible level of efficiency and
effectiveness for process participants 200 collectively, according
to some embodiments.
[0188] In this way the adaptive recommendations function 240,
optionally in conjunction with a system structure maintenance
function, may automatically adapt the structural aspect 210 of the
adaptive system 100, whether on a periodic or continuous basis, so
as to optimize the user experience.
[0189] In some embodiments, each of the automatic steps listed
above with regard to updating the structural aspect 210 may be
employed interactively by human users and experts as desired.
[0190] Hence, the adaptive recommendations function 240, driven in
part by usage behaviors, automatically or semi-automatically
updates the system structural aspect 210 (see dotted arrow 905 in
FIG. 7). The feedback loop is closed as process participant
interactions with the adaptive system 100 are influenced by the
structural aspect 210, providing an adaptive, self-reinforcing
feedback loop between the usage aspect 230 and the structural
aspect 210.
Automatic or Semi-Automatic System Content Maintenance
[0191] As shown in FIG. 7, the adaptive recommendations function
240 may provide the ability to automatically or semi-automatically
update the content aspect 230 of the adaptive system 100 (see
dotted arrow 935). Examples of on-line content or information 232
within the content aspect 230 that may be updated or modified
include text, animation, audio, video, tutorials, manuals,
executable code, and interactive applications. Further,
meta-information 234, such as reviews and brief descriptions of the
content may also be updated or modified 935.
[0192] The content aspect information items 232 may be directly
modified 235 by the adaptive recommendations function 240.
Following are some illustrative examples. For text-based
information 232, words or phrases may be altered, alternative
languages may be applied, and/or the formatting of information 232
may be altered 235. Hyperlinks may be added or deleted to
text-based information 232. For image or graphical-based
information 232, images may be altered 235, or formatting such as
color may be adjusted 235. For audio-based or video-based
information 232, alternative languages may be applied 235 and/or
alternative sound tracks may be applied 235.
[0193] Advertising or promotional elements may be added, deleted,
or adjusted within information 232.
[0194] Customized text or multi-media content suitable for online
viewing or printing may be generated and stored 235 in the content
aspect 230. U.S. patent application Ser. No. 10/715,174 entitled "A
Method and System for Customized Print Publication and Management"
discloses relevant approaches for updating the content aspect 230
with adaptive print media instances and is incorporated by
reference herein, as if set forth in its entirety.
[0195] The adaptive recommendations function 240 may operate
automatically, performing in the background and updating the
content aspect 230 independently of human intervention. Or, the
adaptive recommendations function 240 may be used by users 200 or
special experts who rely on the adaptive recommendations 250 to
provide guidance in maintaining the content aspect 230.
[0196] As in the case of the structural aspect 210, different
communities may also be used to model the maintenance of the
content aspect 230. The communities, affinity groups, and user
segments are used to adapt the relevancies and to create, alter or
delete relationships 214 between the objects 212. The adaptive
recommendations 250 may present the objects 212 to the user 200 in
a different combination than initially may have been assembled or
inputted, and may treat sections of a superordinate object 212 such
as a document, book, manual, video, sound track, or interactive
software as multiple subordinate objects 212 that can be recombined
in a pattern that is aligned with community usage, by creating or
altering relationships between sections of the superordinate object
212.
[0197] In addition, as user feedback on system activities and usage
behavioral patterns 248 is accumulated, the adaptive system 100 may
suggest areas where additional content would be beneficial to
users. For example, if the object 212 is frequently rated by users
200 as difficult to understand, or if only expert users in a
community are accessing the object, the adaptive system 100 may
recognize the need for generating supplemental content (e.g., in
the form of documentation or online tutorials or demonstrations),
and/or a need to re-structure object 212 and/or the associated
meta-information 234 or information 232.
[0198] The re-structuring 935 of the object 212 may include
decomposing the associated meta-information 234 or information 232
into subordinate objects 212, and/or meta-information 234 or
information 232, and applying appropriate relationships 214 to
these newly created elements.
[0199] Hence, as shown in FIG. 7, the adaptive recommendations
function 240, driven in part by usage behaviors 270 (see FIG. 9C),
automatically or semi-automatically updates 935 the content aspect
230. The feedback loop is closed as the interactions of the user
200 with the adaptive system 100 are influenced by updates to the
content aspect 230, providing an adaptive, self-reinforcing
feedback loop between the usage aspect 210 and the content aspect
230, and, in some embodiments, between the usage aspect 210, the
structural aspect 220, and the content aspect 230.
Network-Based Embodiments
[0200] The structural aspect 210 of the adaptive system 100 may be
based on a network structure. The structural aspect 210 thus
includes two or more objects, along with associated relationships
among the objects. Networks, as used herein, are distinguished from
other structures, such as hierarchies, in that networks allow
potential relationships between any two objects of a collection of
objects. In a network, there does not necessarily exist
well-defined parent objects, and associated children,
grandchildren, etc., objects, nor a "root" object associated with
the entire system, as there would be by definition in a hierarchy.
In other words, networks may include cyclic relationships that are
not permitted in strict hierarchies. As used herein, a hierarchy
can be thought of as just one particular form of a network, with
some additional restrictions on relationships among network
objects.
[0201] The structural aspect 210 of the adaptive system 100 may
also have a fuzzy network structure. Fuzzy networks are
distinguished from other types of network structures in that the
relationships between objects in fuzzy networks may be by degree.
In non-fuzzy networks, the relationships between objects are
binary. Thus, in non-fuzzy networks, between any two objects
relationships either exist or they do not exist.
[0202] As used herein, a fuzzy network is defined as a network of
information in which each individual item of information may be
related to any other individual item of information, and the
associated relationship between the two items may be by degree. A
fuzzy network can be thought of abstractly as a manifestation of
relationships among fuzzy sets (rather than classical sets), hence
the designation "fuzzy network." As used herein, a non-fuzzy
network is a subset of a fuzzy network, in which relationships are
restricted to binary values (i.e., relationship either exists or
does not exist.
[0203] Generalizing further, both classical networks and fuzzy
networks may have a-directional (also called non-directed) or
directed links between nodes. Four network topologies are listed in
Table 2.
TABLE-US-00002 TABLE 2 Network Topologies network type links
between nodes link type type i (classical) binary a-directional
type ii (classical) binary distinctly directional type iii (fuzzy)
multi-valued a-directional type iv (fuzzy) multi-valued distinctly
directional
The first two types (i and ii) are classical networks. Fuzzy
networks, as used herein, are networks with topologies iii or
iv.
[0204] For each of the four network topologies listed in Table 2,
another possible variation exists: whether the network allows only
a single link or multiple links between any two nodes, where the
multiple links may correspond to multiple types of links. For
example, the fuzzy network types (iii and iv) of Table 2 may permit
multiple directionally distinct and multi-valued links between any
two nodes in the network. The adaptive system 100 encompasses any
of the network topologies listed in Table 2, including those which
allow multiple links and multiple link types between nodes.
[0205] Mathematically, for a non-fuzzy network, it can be said,
without loss of generality, that a relationship translates to
either a "0" or a "1"-"0," for example if there is not a
relationship, and "1" if there is a relationship. For fuzzy
networks, the relationships between any two nodes, when normalized,
may have values along a continuum between 0 and 1 inclusive, where
0 implies no relationship between the nodes, and 1 implies the
maximum possible relationship between the nodes.
[0206] The structural aspect 210 of the adaptive system 100 of FIG.
7 may support any of the network topologies described above.
A-directional relationships between nodes (no arrows), directed
relationships between nodes (whether single- or double-arrow), and
multiple types of relationships between nodes, are supported by the
adaptive system 100. Further, relationship indicators which are
binary (e.g., 0 or 1) or multi-valued (e.g., range between 0 and 1)
are supported by the adaptive system.
[0207] It can readily be seen that a hierarchy may be described as
a directed fuzzy network with the additional restrictions that the
relationship values and indicators associated with each
relationship must be either "1" or "0" (or the symbolic
equivalent). Further, hierarchies do not support cyclic or closed
relationship paths.
[0208] FIG. 13 illustrates a fuzzy network 500, including a subset
502 of fuzzy network 500. The subset 502 includes three objects
504, 506, and 508, designated as shaded for ease of identification.
The subset 502 also includes associated relationships (arrows) and
relationship indicators or weightings (values) among the three
objects. The separated subset of the network 502 yields a fuzzy
network (subset) 500s.
[0209] A particular implementation of a fuzzy network structure, a
fuzzy content network, which may advantageously constitute the
fuzzy network 500, is disclosed in U.S. Pat. No. 6,795,826,
entitled "Fuzzy Content Network Management and Access," and is
incorporated by reference herein, as if set forth in its
entirety.
[0210] The adaptive system 100 of FIG. 7 may utilize fuzzy network
structures, such as the fuzzy network 500 of FIG. 13. In FIG. 14,
an adaptive system 100C includes a structural aspect 210C that is a
fuzzy network 500. Thus, adaptive recommendations 250 generated by
the adaptive system 100C are also structural subsets that are
themselves fuzzy networks. Further, although not explicitly shown
in FIG. 14, the usage aspect 220 may also be entirely, or in part,
represented by a fuzzy network.
[0211] The structural aspect 210 of the adaptive system 100 may
include multiple types of structures, comprising network-based
structures, non-network-based structures, or combinations of
network-based structures and non-network-based structures. In FIG.
15, the adaptive system 100D includes a structural aspect 210D,
which includes multiple network-based structures and
non-network-based structures. The multiple structures of 210D may
reside on the same computer system, or the structures may reside on
separate computer systems.
Adaptive Recombinant Systems
[0212] In FIG. 16, according to some embodiments, a particular
configuration of the adaptive recombinant computer-based
application 925R (FIG. 4C) is depicted, in which the adaptive
recombinant computer-based application 925R includes an adaptive
recombinant system 800. The adaptive recombinant system 800
includes the adaptive system 100 of FIG. 7, as well as the adaptive
recombinant function 850. The adaptive recombinant function 850
includes a syndication function 810, a fuzzy network operators
function 820, and an object evaluation function 830. Just as the
adaptive system 100 may be part of the adaptive process 900, the
adaptive recombinant system 800 may be part of the adaptive
recombinant process 901. The adaptive recombinant function 850,
including the syndication function 810, the fuzzy network operators
function 820, and the object evaluation function 830 functions may
all reside within the adaptive recombinant computer-based
application 925R, as shown in FIG. 16, or one or all of the
functions may be external to the computer-based application
925R.
[0213] The adaptive recombinant system 800 is capable of
syndicating and recombining structural subsets 280. The structural
subsets 280 may be derived through either direct access of the
structural aspect 210 by the fuzzy network operators function 820,
or the structural subsets 280 may be generated by the adaptive
recommendations function 240. The adaptive recombinant system 800
of FIG. 16 is capable of syndicating (sharing) and recombining the
structural subsets, whether for display to the user 200 or non-user
260, or to update the structural aspect 210 and/or the content
aspect 230 of the adaptive system 100. In addition, these functions
are capable of accessing and updating multiple adaptive systems
100, or aiding in the generation of a new adaptive system 100.
[0214] The syndication function 810 may syndicate elements of the
usage aspect 220 associated with syndicated structural subsets 280,
thus enabling elements of the usage clusters and patterns, along
with the corresponding structural subsets, to be combined with
other structural subsets and associated usage clusters and
patterns.
[0215] As explained above, the structural aspect 210 of the
adaptive system 100 may employ a network structure, and is not
restricted to a particular type of network. In some embodiments,
the adaptive recombinant system 800 operates in conjunction with an
adaptive system in which the structural aspect 210 is a fuzzy
network. The structural subsets 280 generated by the adaptive
recombinant system 800 during syndication or recombination are
likewise fuzzy networks in these embodiments, and are also called
adaptive recombinant fuzzy networks. Recall that a structural
subset is a portion or subset of the structural aspect 210 of the
adaptive system 100. The structural subset 280 may include a single
object, or multiple objects, and, optionally, their associated
relationships.
[0216] The adaptive recombinant system 800 of FIG. 16 is able to
syndicate and combine structural subsets 280 of the structural
aspect 210 (where a structural subset 280 may contain the entire
structural aspect 210). The structural subsets 280, which are fuzzy
networks, in some embodiments, may be syndicated in whole or in
part to other computer networks, physical computing devices, or in
a virtual manner on the same computing platform or computing
network. Although the adaptive recombinant system 800 is not
limited to generating structural subsets which are fuzzy networks,
some of the following figures and descriptions, used to illustrate
the concepts of syndication and recombination, feature fuzzy
networks. Designers of ordinary skill in the art will recognize
that the concepts of syndication and recombination may be
generalized to other types of networks.
[0217] Thus, the adaptive recombinant system 800 of FIG. 16 may
utilize fuzzy network structures. In FIG. 17, an adaptive
recombinant system 800C includes the adaptive system 100C of FIG.
14, in which the structural aspect 210C is a fuzzy network. Thus,
the adaptive recombinant system 800C may perform syndication and
recombination operations, as described above, to generate
structural subsets that are fuzzy networks.
Fuzzy Network Subsets and Adaptive Operators
[0218] The adaptive recombinant system 800 of FIG. 16 includes
fuzzy network operators 820. The fuzzy network operators 820 may
manipulate one or more fuzzy or non-fuzzy networks. Some of the
operators 820 may incorporate usage behavioral inferences
associated with the fuzzy networks that the operators act on, and
therefore these operators may be termed "adaptive fuzzy network
operators." The fuzzy network operators 820 may apply to any fuzzy
network-based system structure, including fuzzy content network
system structures, described further below.
[0219] FIG. 18 is a block diagram depicting some fuzzy network
operators 820, also called functions or algorithms, used by the
adaptive recombinant system 800. A selection operator 822, a union
operator 824, an intersection operator 826, a difference operator
828, and a complement operator 832 are included, although
additional logical operations may be used by the adaptive
recombinant system 800. Additionally, the fuzzy network operators
820 include a resolution function 834, which is used in conjunction
with one or more of the operators in the fuzzy network operators
820.
[0220] A selection operator 822, which selects subsets of networks,
may designate the selected network subsets based on degrees of
separation. For example, subsets of a fuzzy network may be selected
from the neighborhood, around a given node, say Node X. The
selection may take the form of selecting all nodes within the
designated network neighborhood, or all the nodes and all the
associated links as well within the designated network
neighborhood, where the network neighborhood is defined as being
within a certain degree of separation from Node X. A non-null fuzzy
network subset will therefore contain at least one node, and
possibly multiple nodes and relationships.
[0221] Two or more fuzzy network subsets may then be operated on by
network operations such as union, intersection, difference, and
complement, as well as any other network operators that are
analogous to Boolean set operators. An example is an operation that
outputs the intersection (intersection operator 826) of the network
subset defined by the first degree or less of separation from Node
X and the network subset defined by the second or less degree of
separation from Node Y. The operation would result in the set of
nodes and relationships common to these two network subsets, with
special auxiliary rules optionally applied to resolve duplicative
relationships as explained below.
[0222] The fuzzy network operators 820 may have special
capabilities to resolve the situation in which union 824 and
intersection 826 operators define common nodes, but with differing
relationships or values of the relationships among the common
nodes. The fuzzy network intersection operator 826,
Fuzzy_Network_Intersection, may be defined as follows:
Z=Fuzzy_Network_Intersection(X,Y,W)
where X, Y, and Z are network subsets and W is the resolution
function 834. The resolution function 834 designates how
duplicative relationships among nodes common to fuzzy network
subsets X and Y are resolved.
[0223] Specifically, the fuzzy network intersection operator 826
first determines the common nodes of network subsets X and Y,
applying the object evaluation function 830 to determine the degree
to which nodes are identical, to form a set of nodes, network
subset Z. The fuzzy network intersection operator 826 then
determines the relationships and associated relationship value and
indicators uniquely deriving from X among the nodes in Z (that is,
relationships that do not also exist in Y), and adds them into Z
(attaching them to the associated nodes in Z). The operator then
determines the relationships and relationship indicators and
associated values uniquely deriving from Y (that is, relationships
that do not also exist in X) and applies them to Z (attaching them
to the associated nodes in Z).
[0224] For relationships that are common to X and Y, the resolution
function 834 is applied. The resolution function 834 may be any
mathematical function or algorithm that takes the relationship
values of X and Y as arguments, and determines a new relationship
value and associated relationship indicator.
[0225] The resolution function 834, Resolution_Function, may be a
linear combination of the corresponding relationship value of X and
the corresponding relationship value of Y, scaled accordingly. For
example:
Resolution_Function(X.sub.RV,Y.sub.RV)=(c.sub.1*X.sub.RV+c.sub.2*Y.sub.R-
V)/(c.sub.1+c.sub.2)
where X.sub.RV and Y.sub.RV are relationship values of X and Y,
respectively, and c.sub.1 and c.sub.2 are coefficients. If
c.sub.1=1, and c.sub.2=0, then X.sub.RV completely overrides
Y.sub.RV. If c.sub.1=0 and c.sub.2=1, then Y.sub.RV completely
overrides X.sub.RV. If c.sub.1=1 and c.sub.2=1, then the derived
relationship is a simple average of X.sub.RV and Y.sub.RV. Other
values of c.sub.1 and c.sub.2 may be selected to create weighted
averages of X.sub.RV and Y.sub.RV. Nonlinear combinations of the
associated relationships values, scaled appropriately, may also be
employed.
[0226] The Fuzzy_Network_Union operator 824 may be derived from the
Fuzzy_Network_Intersection operator 826, as follows:
Z=Fuzzy_Network_Union(X,Y,W)
where X, Y, and Z are network subsets and W is the resolution
function 834. Accordingly,
Z=Fuzzy_Network_Intersection(X,Y,W)+(X-Y)+(Y-X)
That is, fuzzy network unions of two network subsets may be defined
as the sum of the differences of the two network subsets (the nodes
and relationships that are uniquely in X and Y, respectively) and
the fuzzy network intersection of the two network subsets. The
resulting network subset of the difference operator contains any
unique relationships between nodes uniquely in an originating
network subset and the fuzzy network intersection of the two
subsets. These relationships are then added to the fuzzy network
intersection along with all the unique nodes of each originating
network subset, and all the relationships among the unique nodes,
to complete the resulting fuzzy network subset.
[0227] For the adaptive recombinant system 800, the resolution
function 834 that applies to operations that combine multiple
networks may incorporate usage behavioral inferences related to one
or all of the networks. The resolution function 834 may be
instantiated directly by the adaptive recommendations function 240
(FIG. 16), or the resolution function 834 may be a separate
function that invokes the adaptive recommendations function. The
resulting relationships in the combined network will therefore be
those that are inferred by the system to reflect the collective
usage histories and preference inferences of the predecessor
networks.
[0228] For example, where one of the predecessor networks was used
by larger numbers of individuals, or by individuals that members of
communities or affinity groups that are inferred to be best
informed on the subject of the associated content, then the
resolution function 834 may choose to preferentially weight the
relationships of that predecessor network higher versus the other
predecessor networks. The resolution function 834 may use any or
all of the usage behaviors 270, along with associated user
segmentations and affinities obtained during usage behavior
pre-processing 204 (see FIG. 9C), as illustrated in FIG. 6 and
Table 1, and combinations thereof, to determine the appropriate
resolution of common relationships and relationship values among
two or more networks that are combined into a new network.
[0229] The object evaluation function 830 may applied when the
adaptive recombinant system 800 of FIG. 16 is used to combine
networks. Combining networks requires a determination of which
objects 212 in two or more networks are identical, or near enough
to being identical to be considered identical, for the purposes of
combining the networks. In some embodiments, the object evaluation
function 830 may enable a global identification management process
in which each object 212 has a unique system designator, which
enables direct determination of identity of the objects. This
approach may be augmented by the tracking versions or generations
of objects 212, such that the adaptive recombinant system 800 has
options for using more recent versions of an object 212 when
networks are combined. In other embodiments, the object evaluation
function 830 may compare the intrinsic information associated with
two objects 212 to determine whether they are identical or nearly
identical enough to be considered identical for the purposes of
combining the networks. For example, for text-based objects 212,
associated meta-information 234 or information 232 may be compared
between two objects using text-based pattern matching or
statistical algorithms. For audio or video-based objects 212, other
appropriate pattern matching algorithms may be applied by the
object evaluation function 830 to the associated meta-information
234 or information 232
Fuzzy Process Networks
[0230] In some embodiments, implementation of a fuzzy network-based
process may be through connecting an existing or new process with a
fuzzy network 500A, as is shown in FIG. 19A. For example, an
activity 45 within a process or sub-process 136 may precede another
activity 50 in the sub-process, with an explicit workflow 55
between the activities. It should be understood that there may be a
greater number of activities in the process or sub-process 136 than
the minimal number illustrated in FIG. 19A. The fuzzy content
network 500A, managed by the adaptive computer-based application
925, which is "external" to the activities 45, 50 in the
sub-process 136, may be accessible 56, 57 by one or more of the
activities 45, 50.
[0231] In other embodiments, implementation of a fuzzy
network-based process may be through including an existing or new
process within a fuzzy network 500B managed by the adaptive
computer-based application 925, as is shown in FIG. 19B. For
example, an activity 65 within a process or sub-process 137 may
precede another activity 70 in the sub-process, with an explicit
workflow 75 between the activities 75. These activities and their
relationships are represented directly within the fuzzy network
500B in this case. It should be understood that there may be a
greater number of activities in the process/sub-process 137 than
the minimal number illustrated in FIG. 19B.
[0232] In some embodiments, adaptive recombinant processes may
employ structures based on fuzzy content networks, as defined in
U.S. Pat. No. 6,795,826, entitled "Fuzzy Content Network Management
and Access." These structures may include the use or adaptation of
fuzzy content networks and associated topic objects and content
objects, as defined therein.
[0233] For "inclusive" fuzzy network embodiments, such as the fuzzy
content network 500B of FIG. 19B, according to some embodiments,
FIG. 20A depicts the structure of a process topic object 445t,
which consists of meta-information 450t only, and is analogous to a
fuzzy content network topic object. Likewise, FIG. 20B depicts a
process content object 445c, which consist of embedded information,
or references (for example, pointers or URLs) to information 455c,
and the associated meta-information 450c. Fuzzy process content
objects 455c are analogous to fuzzy content network content
objects. According to some embodiments, process activities may be
included within the fuzzy content network, and as shown in FIG.
21A, and a process activity object 445a contains meta-information
450a, analogous to the process topic object 455t of FIG. 20A. In
other embodiments, as shown in FIG. 21B, process activities may be
included within the fuzzy content network, and a process activity
object 446a will contain meta-information 451a, as well as
information or a pointer to information 456a, analogous to the
process content object 445c of FIG. 20B. For all of these fuzzy
network object structures, relationships and associated
relationship indicators may be established between any two process
objects in the process network, and there may be plurality of types
of relationships and associated relationship indicators between any
two process objects. In some embodiments, at least one relationship
type denotes process sequence or workflow, and is typically applied
among process activity objects, but may apply among other process
objects as well.
[0234] As reviewed previously, FIGS. 20A, 20B, 21A and 21B depict
in some embodiments how fuzzy network objects may be converted to
process network objects, and how special process objects, process
activity objects 445a and 446a may be defined.
[0235] FIG. 22A illustrates a process activity "network A" 460,
including four activities (465a, 465b, 465c, and 465d) and work
flow relationships among the activities (470a, 470b, 470c, and
470d), as well as relationships to activities external to process
activity "network A" 470e. Each relationship has an associated
relationship indicator 471. In some embodiments, the relationship
indicator is represented in the form:
Sequence(Relationship type,First Activity,Second Activity)
The relationship indicator "S(1,1,2)" 470 of relationship 470a thus
implies a relationship of type 1 between activity 1 and activity 2,
in that sequence.
[0236] FIG. 22B illustrates a process activity network 475, which
may have multiple relationship types 476a and 476b outbound from an
activity (activity 1 474a), and may also have multiple relationship
types inbound 476b and 476c to an activity (activity 4 474b).
Furthermore, multiple relations of different relationship types may
be outbound from one or more activities in the process activity
network to destinations outside the process activity network. For
example, in FIG. 22B, relationship 476d of relationship type 2
(S(2,4,M)) is outbound from activity 4 474b; likewise, relationship
476e having relationship type 1 (S(1,4,N)) is also outbound from
activity 4 474b.
[0237] According to some embodiments, FIGS. 23A and 23B depict
process networks 480A and 480B (collectively, process network 480).
The process networks 480A and 480B are depicted for a particular
relationship and associated relationship indicators, at particular
times (t.sub.0 and t.sub.2), in some embodiments. The process
networks 480A and 480B are process activity networks (see FIGS. 22A
and 22B). The process networks 480A and 480B are integrated with
process content objects, for example, "content object 1" 485a and
process topic objects, for example, "topic object 1" 485b.
Relationships and associated relationship indicators may exist
between process activity objects and process content or topic
objects, for example, 490.
[0238] FIG. 24 is a flow diagram illustrating how process usage
information associated with the process networks 480A and 480B are
processed, according to some embodiments, over a period of time.
During time t.sub.1, usage behavior information 920 is tracked and
processed (block 4495). The adaptive recommendations function 240
of the adaptive system 100 is invoked (block 4500), and the process
structure of the process network 480A is automatically or
semi-automatically updated (block 4505), resulting in process
network 480B at time t.sub.2. Thus, process network 480A at time
t.sub.0 (FIG. 23A) automatically or semi-automatically becomes
process network 480B at time t.sub.2 (FIG. 23B), using the
procedure in FIG. 24. Structures that may be updated within the
process network 480 include relationship indicators; for example,
relationship indicators 515 between content object 1 485a and
activity 1 520 had values of 0.4 and 0.6 at time t.sub.0 (FIG.
23A); at time t.sub.2, the relationship indicators 515 have values
of 0.8 and 0.6 (FIG. 23B). Relationships may be deleted, as for
example between process activity 1 520, and process activity 4 525
(formerly S(2,1,2) in FIG. 23A). Relationships and associated
relationship indicators may be added, as for example 530 between
activity 4 525 and content object 4 540. And process objects, and
associated relationships may be deleted. For example the former
content object 5 of FIG. 23A and its associated relationships and
relationship indicators, is not part of process network 480B.
[0239] FIG. 25 depicts process network 480B (FIG. 24B) at time
t.sub.2. Process activity objects (shaded) are selected, along with
the associated relationships between these process activity
objects, as well as other selected process objects that have a
relationship to the selected process activity objects, and the
associated relationships. In some embodiments, the selection of the
process network subset may be through application of network
neighborhood metrics, such as degrees of separation metrics, or
fuzzy degrees of separation network neighborhood metrics. In other
embodiments, other selection methods may be used, including
individually specifying process objects and associated
relationships. In this example, the result of the
selection/sub-setting 555 of process network 480B is process
network 560.
Adaptive Recombinant Processes
[0240] FIGS. 26 and 27 illustrate the syndication and combination
of process networks by the adaptive recombinant system 800C. (The
process network activity objects are shaded, to distinguish from
the content and topic objects.) In FIG. 26, process network subset
B 560 (FIG. 25) is syndicated to an existing process network C 580
that may exist on the same computer system or a different computer
system. It should be noted that a process network need not
represent a "complete" or "functional" process. For example,
process network C 580 contains two process activity objects 581,
582 that do not have a direct relationship to one another. In
addition, associated relationships 581r and 582r have no
corresponding forward sequence process activity object within the
process network 580. In general, a process network may be
fragmentary, without completeness of process objects and
relationships.
[0241] FIG. 27 illustrates the results of the combination of
process network B 560 and process network C 580 by the adaptive
recombinant system 800C, and the application of the fuzzy network
operators function 820, the adaptive recommendations function 240
and the object evaluation function 830 (FIG. 17). The result is
process network D 590. Note that all distinct process activity
objects from 560 and 580 reside in 590, and the associated
relationships among the process activity objects are resolved and
established. Note also that these relationships may be reflexive,
as in the case of 591 and 592. In the process network subset C 580
(FIG. 26), a relationship indicator "S(2,M, 4)" is indicated,
although no "activity 4" is present in the sub-network 580. Once
syndication with process network subset B 560, which includes
"Activity 4," occurs, the adaptive recombinant system 800C
automatically relates the two activities 4 and M, as shown in FIG.
27. Other process objects and corresponding relationships may be
resolved as previously described.
[0242] FIG. 28 illustrates that the process network 560 may be
encompassed by the structural aspect 210C of adaptive system 100C
(FIG. 7). The process network 560 may be the sole content network
within structural aspect 210C, or may be one of multiple network or
non-network structures within 210C, as is more generally depicted
in FIG. 15, above.
[0243] Likewise, FIG. 29 illustrates that the process network 560
may be encompassed by the structural aspect 210C of the adaptive
system 100C, which may form part of the adaptive recombinant system
800C. Again, the process network 560 may be the sole content
network within structural aspect 210C, or may be one of multiple
networks within 210C, and may be syndicated, modified, and combined
with other content or process networks, as is more generally
depicted in FIGS. 47 and 48, below. The process network 560 or
another process network structure within the structural aspect 210C
may correspond to the adaptive process instance 930 of FIGS. 4A and
4B, and hence FIGS. 15, 29, 47 and 48 illustrate the ability to
syndicate and combine representations of adaptive process instances
930, thereby enabling the adaptive recombinant process 901.
[0244] FIGS. 30A, 30B, 31A, and 31B illustrate the general
approaches associated with process network syndication and
combinations, as managed by the adaptive recombinant system 800C,
and applied as part of a particular type of application of the
adaptive recombinant process 901, designated in FIGS. 30A, 30B, 31A
and 31B as process application type 901A. FIG. 30A illustrates a
hypothetical starting condition, and depicts three organizations,
650, 655, 660. These may be organizations (which may be
individuals) within the same business or institution, or one or
more may be in businesses or institutions external to the others. A
first process network, "process network 1" 665, is used solely by,
or resides within, "organization 1" 650. A second process network,
"process network 2" 670, is used solely by, or resides within,
"organization 2" 655. "Organization 3" 660 does not have a process
network initially, in this example.
[0245] FIG. 30B illustrates that a subset of "process network 1"
665 is selected to form "process network 1A" 680. "Process network
1A" 680 is then syndicated as "process network 1A" 685 to
"organization 2." "Organization 2" 655 then syndicates "process
network 1A" 685 to "organization 3" 660 as "process network 1A"
690. Thus, FIG. 30B illustrates how process networks, or subsets of
process networks, can be syndicated among organizations without
limit.
[0246] FIG. 31A depicts a subset of "process network 1" 665 and
"process network 1A" 695 residing in "organization 1," in which
"process network 1a" 695 is syndicated to "organization 2" 655 as
"process network 1A" 700. "Process network 1A" 700 and the existing
"process network 2" 670 in "organization 2" are combined 710 to
form "process network 2a" 715 in organization 2 655. "Process
network 2a" 715 is then syndicated to "organization 3" 660 as
process network 2A 720.
[0247] FIG. 31B represents a continuation of FIG. 31A, in which
additional combination and syndication takes place. "Process
network 2a" 720 in "organization 3" 660 is syndicated to
"organization 1" 650 as process network 2A 730. Process network 2A
730 is then combined with the original "process network 1" 665 in
"organization 1" 650 to generate "process network 3" 740 in
"organization 1" 650.
[0248] FIGS. 30A, 30B, 31A, and 31B demonstrate that, in some
embodiments, adaptive recombinant processes may indefinitely enable
sub-setting of process networks, syndicating the subsets to one or
more destinations, and enabling the syndicated process networks to
be combined with one or more process networks at the destinations.
At each combination step, the relationship resolution function 834
(of the fuzzy network operators 820--see FIG. 18) and the adaptive
recommendations function 240 may be invoked to create and update
process structure (and content) as appropriate.
[0249] According to some embodiments, FIG. 32 depicts possible
deployments of process networks within and across organizations or
business enterprises. In FIG. 32, two enterprises 1810, 1815 are
depicted, but it should be understood the following described
process and process network topologies can apply to any plurality
of organizations, individuals, or business enterprises. One
topology is represented by "Process 1" 1811 containing one process
network, 1812, within one enterprise, 1810. In another topology, a
process 1816 contains a plurality of process networks 1817, 1818
within one business enterprise, 1815. In another topology, a
process 1820 may extend across more than one enterprise 1810 and
1815, and may contain a plurality of process networks 1821, 1822,
and 1823. A process network 1823 may extend across business
enterprises 1810 and 1815. Process networks may have common
subsets, as exemplified by 1822 and 1823. Processes and process
networks may extend across an unlimited number of organizations or
business enterprises as depicted by process 1830 and process
network 1832.
[0250] According to some embodiments, FIG. 33 depicts a process
network topology in which a process network 1840 includes multiple
processes, each process contained partially or as a whole within
the process network 1840, and include a multiplicity of other
process networks, each process contained partially or as a whole,
where each contained process or process network may span a
plurality of organizations or business enterprises.
Process Lifecycle Framework
[0251] In some embodiments, as shown in FIG. 34, a process
lifecycle framework 3000 may be used as an implementation framework
for migrating to adaptive processes, based on the implementation of
adaptive recombinant processes, or other methods and
technologies.
[0252] The process lifecycle framework 3000 has two primary
dimensions. The horizontal dimension denotes how the organizing
topology 3010 of a process is managed--either in a centralized 3011
or decentralized 3012 manner. The vertical dimension relates to the
local differentiation 3020 of a process--how differentiated 3021 or
customized 3022 the process is for local applications or
implementations. The process may be standardized across all local
applications 3021, or customizable to local applications 3022. The
intersections of these dimensions denote fundamental process
lifecycle positions. For example, a centralized organizing
topology, coupled with standardization of processes across local
applications, may be called a "cost and control" quadrant 3030. The
focus in this quadrant is typically to ensure low cost processes
that enforce broad standards across organization and application
areas. This is the typical architecture of prior art processes
supported by Enterprise Resource Planning (ERP) software that are
implemented on a truly enterprise basis.
[0253] A decentralized organizing topology, coupled with
standardization of processes across local applications, may be
called the "ad hoc" quadrant 3040. The focus in this quadrant is to
enforce broad standards across organization and application areas,
but through a decentralized process management and infrastructure
approach. This quadrant often represents an inconsistency of
objectives, and may be the result of organizational combinations,
such as through a merger or acquisition. It is often desirable to
not remain in this quadrant in the long-term, as ad hoc
implementation typically generates more costs to deliver the same
results as the "cost and control" quadrant 3030.
[0254] A decentralized organizing topology, coupled with
customization of processes across local applications, may be called
the "Niche Advantages" quadrant 3050. The emphasis of this quadrant
is to maximize the value of the process in specific application
areas through a decentralized process management and infrastructure
approach that enables maximum flexibility and tailoring to local
needs. This quadrant represents a potentially high value, but also
high cost approach. It is often consistent with the development of
new processes that provide competitive advantages, where the
generation of value from the processes overrides inefficiencies
stemming from decentralized process management and heterogeneous
enabling infrastructure. Over time, however, as competitive
advantages potentially dissipate, the cost penalty associated with
this quadrant may be too high compared to the derived benefits.
[0255] A centralized organizing topology, coupled with
customization of processes across local applications, may be called
the "Adaptive Processes" quadrant 3060. The emphasis of this
quadrant is to maximize the value of the process in specific
application areas, but through an efficient, centralized process
management and infrastructure approach that enables maximum
flexibility and tailoring to local needs. This quadrant represents
a potentially high value and low cost approach, and provides
advantages versus the other three quadrants. An adaptive process
approach has been very difficult to achieve with prior art process
and supporting process infrastructure and systems. The adaptive
processes quadrant 3060 is the quadrant, in particular, that
adaptive recombinant processes advantageously addresses.
[0256] According to some embodiments, FIG. 35 is a framework 3100
that describes how processes typically include multiple
functionality layers 3110. For example, these layers may comprise
information technology layers, with the highest level corresponding
to process work flow and business logic, and lower layers
corresponding to more generalized information technology, such as
content management, database management systems, and communications
networks.
[0257] In a process implementation, then, different layers may have
different process lifecycle quadrants. For example, the top-most
layer may be a niche advantage quadrant 3120, the directly
supporting layer may be an adaptive processes quadrant 3130, and
the directly supporting layer of that layer may be a cost and
control quadrant 3140. In general, it is good practice that the
lower process layers should be at least as standardized as the
layers above.
[0258] According to some embodiments, FIG. 36 represents a process
lifecycle management framework 3200 that may be advantageously used
by businesses and institutions to ensure the highest possible value
from their processes over time. The framework 3200 may be
understood to represent one specific process lifecycle
functionality layer.
[0259] Business innovations 3210 may be the source of processes (or
process functionality layers) in the Niche Advantages quadrant.
Business combinations 3230 may be the source of processes in the Ad
Hoc Implementation quadrants. It is usually advantageous to migrate
from the Ad Hoc Implementation quadrant to the Cost and Control
quadrant through more effective leverage of scale 3240. It may be
advantageous to migrate from the Niche Advantages quadrant to the
Adaptive Processes quadrant through leverage of mass customization
techniques 3220. It may also be advantageous to migrate from the
Cost and Control quadrant to the Adaptive Processes quadrant
through leverage of mass customization techniques 3250.
Alternatively, it may also be advantageous to externalize the
process 3260 from the Cost and Control quadrant, where external
sources can provide process advantages, typically either through
cost effectiveness, or through more effective customization or
adaptation to local applications and the same cost.
Adaptive Process Application Areas
[0260] Recall from FIGS. 3, 4A, 4B, and 4C that adaptive
recombinant processes may be applied to improve the functionality
of any process 168 by integrating adaptive recommendations
functions into the process 168 and applying the adaptive
recommendations to facilitate the more effective use of the process
instance 930. The application of the adaptive recommendations may
be through delivery of adaptive recommendations 910 to process
participants 200 or by applying the adaptive recommendations to
modify the structure 905 and/or content 935 of computer-based
applications 175 supporting the process, or both.
[0261] The following pages include descriptions of several adaptive
processes 900 and adaptive recombinant processes 901. Table 3 lists
embodiments of the adaptive process 900, including an associated
figure and claim.
TABLE-US-00003 TABLE 3 Adaptive Process Embodiments Embodiment
Figure Claim Adaptive process 900 FIG. 4A Claim 1 Adaptive asset
management process 900A FIG. 37 Claim 8 Adaptive real-time learning
process 900B FIG. 38 Claim 25 Innovation network process 900C FIG.
39 Claim 34 Adaptive publishing process 900D FIG. 40 Claim 35
Adaptive commerce process 900E FIG. 41 Claim 27 Adaptive price
discovery process 900F FIG. 42 Claim 28 Adaptive commercial
solutions process 900G FIG. 43 Claim 29 Location-aware collectively
adaptive process 900H FIG. 44 Claim 37
Likewise, Table 4 lists embodiments of the adaptive recombinant
process, including an associated figure and claim.
TABLE-US-00004 TABLE 4 Adaptive Recombinant Process Embodiments
Embodiment Figure Claim Adaptive recombinant process 901 FIG. 4C
Claim 22 Recombinant process network process 901A FIGS. 30A-B Claim
23 Adaptive viral marketing process 901B FIGS. 49A-B Claim 31
Evolvable process 901E FIG. 50 Claim 24
Tables 3 and 4 are provided for convenience in understanding the
following passages, and are not meant as an exhaustive presentation
of the possible applications of the adaptive process 900 or the
adaptive recombinant process 901. Further, the cited figures and
claims are not exhaustive, but are meant as a guide to assist in
understanding the following exemplary embodiments.
[0262] FIGS. 37-43 depict specific applications of the adaptive
process 900 (processes 900A-900H) or adaptive recombinant process
901 (processes 901A, 901B, 901E). In some of these applications,
the adaptive process 900 will include an adaptive system 100 (FIG.
7), in which the adaptive system may include some non-adaptive
elements (FIG. 8), a fuzzy network structure (FIG. 14), a
combination of network and non-network-based structure (FIG. 15),
or a process network structure (FIG. 28). Further, the adaptive
recombinant process 901 in some of these applications may include
an adaptive recombinant system 800 (FIG. 16), which may include a
fuzzy network structure (FIG. 17), or a process network structure
(FIG. 29).
[0263] The following illustrations are specific process application
areas for which the adaptive process 900 or adaptive recombinant
process 901 may be advantageously applied, although it should be
understood that these application areas do not constitute all the
possible applications of the adaptive process 900 or adaptive
recombinant process 901.
[0264] Adaptive Asset Management
[0265] According to some embodiments, the adaptive process 900 may
be used to establish online asset management systems and processes.
An on-line asset is defined as any item of software or content, or
any tangible or intangible asset that the software or on-line
content represents. In other words, the asset to be managed may
also be derivative from the representations of the software or
content of adaptive process 900.
[0266] Recall from FIGS. 4A and 4B that the adaptive computer-based
application 925 may integrate with existing and/or new online
computer applications 175 to enable capture and analysis of usage
behavior information 920. This information may then be used to
determine the value of the online computer and software assets.
This determination of value of online assets can then be applied
beneficially to facilitate asset management processes associated
with the on-line assets, optionally including applying a function
to automatically or semi-automatically modify the one or more
computer applications 175 in alignment with the inferred value of
the online assets of computer applications 175 to process
participants 200.
[0267] FIG. 37 depicts an adaptive process 900A, including an
adaptive asset management system 1500. The asset management system
1500 includes the adaptive computer-based application 925 and an
asset management function 1510. Although in FIG. 37, the asset
management function 1510 is shown to be external to the adaptive
computer-based application 925, it should be understood that the
asset management function 1510 may be configured to be internal to
the adaptive computer-based application 925. Further, although not
shown in FIG. 37, the adaptive computer-based application 925 may
contain the adaptive system 100.
[0268] The asset management function 1510 receives information 1520
associated with data regarding the usage behaviors 920 of process
participants 200, or inferences of the preferences and interests of
online assets associated with the process participant usage
behaviors 920. The asset management function 1510 uses the
information 1520 to derive the value of online assets. The derived
value may be of different magnitudes for different individuals or
communities of process participants 200. The asset valuation
information determined by the asset management function 1510 may be
applied to decide near-term or long-term online asset changes and
directions. For example, a high-value on-line asset might be made
more prominently available for process participants 200, while less
valuable assets might be made less prominent, or eliminated from
the content and computer applications 175. New development projects
to deliver on-line assets that are expected to be of high value
based on the valuations of the asset management function 1510 may
be conducted. Further, in addition to on-line assets, features
associated with the assets may be evaluated by the asset management
function 1510, and appropriate asset modifications or development
projects initiated. For some modifications, the asset management
function 1510 may be used to support making the appropriate
changes.
[0269] The asset management function 1510 may automatically or
semi-automatically modify 1505 the adaptive computer-based
application 925. For alternative embodiments in which the asset
management function 1510 is internal to the adaptive computer-based
application 925, the adaptive self-modification operation 1505 is
analogous to the structural modifications 905 of the adaptive
system 100, the adaptive recombinant system 800, and the
generalized adaptive computer-based application 925, described
above. Likewise, the asset management function 1510 may
automatically or semi-automatically modify 1515 content within
adaptive computer-based application 925. For embodiments in which
the asset management function 1510 is internal to the adaptive
computer-based application 925, the adaptive self-modification of
content 1515 is analogous to the content-based modifications 935,
905 of the aforementioned systems 100, 800, 925 (represented in
parentheses). Further, other computer applications and content 175
may be automatically or semi-automatically modified 1525 by the
asset management function 1510 in accordance with valuations
derived by asset management function 1510. In such cases, even if
direct usage behavioral information 920 are not available for
non-adaptive computer application 181 and content 180, the asset
management function 1510 may make inferences based on analogy from
interactions of the process participants 200 with the adaptive
computer-based application 925 to generate appropriate
valuations.
[0270] Note that adaptive recommendations 910 delivered to process
participants 200 is not an essential feature for enabling process
application 900A.
[0271] Adaptive Real-Time Learning
[0272] The adaptive process 900 may be used to establish an
adaptive process environment 930 (FIGS. 4A and 4B) to promote
enhanced learning by process participants 200, including real-time
learning, for existing or new processes through the implementation
of adaptive recommendations 910 that are delivered directly to the
process participant or user 200, or indirectly through adaptive
modification of the process network structure 905 or content 935.
In some embodiments, the resulting environment may be
metaphorically termed an adaptive online "cockpit" of process
knowledge and activities that effectively "surrounds" the process
user. This approach facilitates the real-time learning of process
participants 200, rather than relying solely or primarily on
classroom or other episodic forms of education or training.
[0273] FIG. 38 illustrates an adaptive process 900B, or adaptive
real-time learning process, including an exemplary process
participant interface 1600 associated with a computing device 964
that is interacted with by process participants 200. It should be
understood that although FIG. 38 illustrates a visual,
display-oriented process participant interface, the interface could
be audio-based, tactile or kinesthetically-based, or the interface
could be comprised of combinations of visual, audio, or kinesthetic
elements. The process participant interface 1600 of the adaptive
process 900B may include one or more instances of displayed
adaptive recommendations 910 associated with the adaptive
computer-based application 925, in which the adaptive
recommendations 910 are formatted for viewing in a specified
manner. In FIG. 38, a first formatted instance 1610 and a second
formatted instance 1620 of adaptive recommendations 910 are shown.
The process participant interface 1600 may contain other
information 915 derived from the adaptive computer-based
application 925, formatted as appropriate for display. A formatted
instance 1630 of information 915 from the adaptive computer-based
application 925 is shown. A formatted instance 1630 may contain one
or more instances of adaptive information 1632 and/or non-adaptive
information 1634. Recall from FIG. 4A that adaptive information
1632 is content, structural elements, objects, information, or
computer software that has been adaptively self-modified 905, 935
by the adaptive computer-based application 925 based, at least in
part, on usage behaviors 920 of process participants 200.
Non-adaptive information 1634 denotes any other information,
content, objects, or computer software encompassed by the adaptive
computer-based application 925 that has not been adaptively
self-modified 905, 935.
[0274] The process participant interface 1600 may also contain
formatted instances 1640 of other information such as information
derived from other content 180a and other computer applications
181a that are relevant to process participants 200.
[0275] Formatted instances 1610, 1620 of adaptive recommendations
910 and formatted instances of adaptive computer application
information 915 may contain explicit educational or training
information or content, or relevant references or "help"
information, in addition to more general information or content
relevant to the associated process. In some embodiments, the
adaptive computer-based application 925 may include or interact
with a learning management system that may provide guidance on the
appropriate educational or training information to include in the
adaptive recommendations 910.
[0276] Innovation Networks
[0277] According to some embodiments, the adaptive process 900 may
be used to create adaptive "innovation networks" that may be
applied to facilitate collaborative research and development
processes. These processes may be applied within an organization,
or span an unlimited number of organizations or individuals. In
some embodiments, adaptive recombinant processes may utilize the
systems and methods of PCT Patent Application No. PCT/US05/001348,
entitled "Generative Investment Process," filed on Jan. 18, 2005,
which is hereby incorporated by reference as if set forth in its
entirety, to enable innovation networks and processes.
[0278] FIG. 39 illustrates an adaptive process 900C, or innovation
network process, including the adaptive computer-based application
925, which includes the adaptive system 100. The structural aspect
210 of the adaptive system 100 encompasses an innovation map 1700,
which associates opportunities 1710 to capability components 1730,
shown in FIG. 39 organized within capability component categories
or types 1720. Opportunities, capability component types, and
capability components may be collectively termed the "elements" of
innovation map 1700. It should be understood that although the
innovation map 1700 is depicted in FIG. 39 in a table format, the
innovation map 1700 may be organized in network structure,
including a fuzzy network structure. Further, the innovation map
1700 may be incorporated within a process network, such as in FIG.
25 (not explicitly shown in FIG. 39) within the structural aspect
210.
[0279] "Opportunities," as defined herein are ideas that can
potentially generate value and that involve investments of time,
resources, or financial commitments. These opportunities may be
within defined processes, such as business development and growth
processes, commercial venture capital, corporate venturing
processes, business incubation processes, marketing processes,
research and development processes, and innovation processes, or
the investment processes and associated activities may be more ad
hoc in nature.
[0280] Typically, opportunities 1710 consist of a bundle of two or
more capability components, such as "cc 5" and "cc 7" 1730. For
example, even if a business idea (opportunity) is based on a
technological break-through, the overall business venture idea is
likely to also include other differentiating components, such as
processes (e.g., marketing processes). It is the uniqueness of the
bundle of components that typically provides the economic
value-creating potential of the idea.
[0281] Capability components 1730 may include both tangible and
intangible aspects of an opportunity 1710. The capability
components 1730 may constitute a mutually exclusive, collectively
exhaustive set for each opportunity 1710. (The term collectively
exhaustive, as used herein, means that the elements of a set
comprise the totality of the set.) Or, the capability components
1730 may represent just a subset of the opportunity 1710 defined
and may simultaneously be represented in multiple opportunities
1710. A myriad of possibilities exist for representing
opportunities 1710 using capability components 1730.
[0282] The capability components 1730 of the innovation map 1700
are individual instances of capability component categories or
types 1720. Capability types 1720 may include, but are not limited
to, products (including prototypes), technologies, services,
skills, relationships, brands, mindshare, methods, processes,
financial capital and assets, intellectual capital, intellectual
property, physical assets, compositions of matter, life forms,
physical locations, and individual or collections of people.
[0283] The objective of any innovation process is to maximize the
volume of high value opportunities 1710 generated at the lowest
possible cost. Meeting this objective is a function of multiple
variables. One variable is the volume, breadth and quality of the
capability components 1730. Another variable is the ability to
combine capability components in a large variety and novel ways. A
third variable is the degree to which the greatest diversity of
human attention to be applied, and applied in the right places. The
adaptive process 900C can be used to enable processes that
beneficially affect these key variables of innovation process
success.
[0284] The adaptive computer-based application 925, together with
the innovation map 1700, enables more effective innovation-based
processes in several ways. First, elements of the innovation map
1700 may include adaptive recommendations 250 that are delivered to
process participants 200. This approach can help make process
participants 200 aware of particularly relevant elements of the
innovation map 170. Second, the adaptive recommendations function
240 may be applied to modify 905 the innovation map 1700 based on,
at least in part, inferences on process participant 200 preferences
or interests. This can facilitate the efficient development and
maintenance of a collective innovation map that can most
beneficially serve the interests of the process participants 200,
including maximizing the number of high value opportunities
generated within innovation map 1700. Third, elements of the
innovation map 1700 may be syndicated, modified, and recombined
among process participants 200 through the application of the
adaptive recombinant system 800, enabling multiple, distributed
innovation map instances. This structure can facilitate both shared
and private innovation maps, effectively balancing the advantages
of economies of scale and local interests. The adaptive recombinant
system approaches of FIGS. 47, 48, 49A, and 49B may be applied to
the syndication, modification, and recombination of elements of
innovation map 1700.
[0285] The adaptive computer-based application 925 may contain, or
interact with, auxiliary functions (not shown in FIG. 39) that may
additionally facilitate innovation processes. For example, the
adaptive computer-based application 925 may contain functions to
enable automatic or semi-automatic evaluation of opportunities
1710, to automatically or semi-automatically generate additional
opportunities 1710 through combinatorial operations on capability
components 1730, and/or to facilitate effective information
gathering or experimental design associated with uncertainties with
regard to capability components 1730 or other elements of
innovation map 1700. These additional functions may all be managed
within an adaptive process network, such as the adaptive process
network of FIG. 25 within the structural aspect 210 of the adaptive
system 100.
[0286] Adaptive Publishing
[0287] The adaptive process 900 may be applied to enable adaptive
publishing systems and processes. The adaptive process 900, when
applied to enable adaptive publishing systems and processes, may
generate adaptive analogs to non-adaptive "broadcasted" media such
as print publications, radio programs, music albums or soundtracks,
television programs, films, or interactive games; as well as
generating adaptive media that may not have specific broadcast
analogs. In some embodiments, the methods and systems defined by
U.S. patent application Ser. No. 10/715,174, entitled "A Method and
System for Customized Print Publication and Management," may be
integrated with adaptive recombinant processes to enable an
adaptive publishing process.
[0288] FIG. 40 depicts an adaptive process 900D, or adaptive
publishing process, according to some embodiments. An adaptive
publishing function 2000 that is included within the adaptive
computer-based application 925 (although in other embodiments, the
adaptive publishing function 2000 may be external to the adaptive
computer-based application 925) receives input from the adaptive
system 100. The input may be in the form of adaptive
recommendations 940 suitable for the adaptive publishing purposes,
generated from adaptive recommendations 250, or the input may be in
the form of informational content 2031 contained in the content
aspect 230 of the adaptive system 100. The content originating from
the content aspect 230 may have been modified 935 by the adaptive
recommendations function 240. In either case, the adaptive
publishing function 2000 uses the inputs from the adaptive system
100 to generate media that is appropriately customized for the
recipients of the media 200, 260. This customization of an adaptive
publication, or media instance, may include the specific elements
of content that will be contained in a media instance, and also the
arrangement of the elements of content in the media instance. Thus,
a media instance, as used herein, is a distinct set of objects or
information in combination with a unique arrangement of the objects
or information. The customization of media into specific media
instances is performed on the basis of inferred media recipient
200, 260 preferences and interests, which are in turn based on
recipient interactions with the adaptive system 100, or through
inferred affinities between the media instance recipient and other
individuals that have interacted with adaptive system 100.
[0289] As shown in FIG. 40, the adaptive publishing function 2000
generates one or more instances of media 2030, adapted
appropriately to the preferences or interests of the media
recipients 200, 260. Each media instance contains one or more
elements of content, some or all of which may be objects 212 or
information 232 (FIG. 9A) contained in the adaptive system 100.
Although not shown explicitly in FIG. 40, a media instance may also
explicitly or implicitly include relationships among objects 214
associated with the structural aspect 210 of the adaptive system
100.
[0290] As shown in the example of FIG. 40, media instance 2010
contains multiple objects in a particular configuration, including
"Object A" 2012 and "Object D" 2014. Recall that the objects 212 of
the adaptive system 100 may contain any form of digital
information, including text, graphics, audio, video, and executable
software. These objects may be transformed to alternative media
forms by the adaptive publishing function 2000. An individual media
instance can therefore be defined as a set of information objects
212 or information items 232 and a particular arrangement of the
objects of information items. So, as one example, on-line textual
objects 212 may be transformed into printed media by the adaptive
publishing function 2000. In the case of printed media, a specific
media instance is determined by not only the objects to be included
in a media instance, but also the arrangement or print layout of
the objects 212 and any other content included within the media
instance. The information objects 212 within a media instance may
be substantive in nature, or non-substantive (e.g., promotional or
advertising information).
[0291] In accordance with inferred preferences and interests of the
intended recipients, media instance 2020 contains a different set
of objects and a different arrangement of objects than media
instance 2010. For example, "Object A" 2012 exists in both media
instance 2010 and 2020, but for example, "Object D" 2014 is unique
to media instance 2010 and "Object E" 2024 is unique to media
instance 2020.
[0292] Although the adaptive media instances 2030 of FIG. 40 depict
differing arrangements of objects and other items of content in
accordance with a spatial orientation, consistent with, for
example, physical spatially-oriented media such as printed media,
including newsletters, newspapers, magazines, and books, it should
be understood that the customized object selection and arrangement
of the adaptive publishing function 2000 may apply to other media
types as well. In such cases, the arrangement of elements of the
media instance may be other than spatial in nature; for example,
the arrangement may be temporal-based for media containing
information than is typically "consumed" sequentially. For example,
for audio objects 212 or information 232 such as songs, the
specific songs selected, and arrangement of the songs in a sound
track may be different across media instances. For video or
multi-media objects 212 or information 232, customized media
instances may include applying the adaptive publishing function
2000 to choose different musical sound tracks for corresponding
elements of video, or even generating different media instances
containing different elements of, or a different sequence of, the
plot or story line of the video. For interactive media, such as
computer-based games, the game instance may be customized by the
adaptive publishing function 2000 through the selection of
different software modules of the game, or by arranging the
software modules of the game in different ways in different media
instances.
[0293] For audio and/or video-based objects 212 or information 232,
the adaptive publishing function 2000 may generate media instances
that constitute "programs," which are adaptive analogues of radio
programs, television programs or other broadcasted forms.
[0294] Media instances may be delivered or otherwise made available
2002 to process participants 200, or made available 2004 to
non-process participants 260. Media instances may take a purely
digital form that can be embodied in a variety of physical forms.
They may be available to recipients in the purely digital form, or
they may be available to process participants 200, or to
non-process participants 260 through other physical embodiments. A
media instance may be printed, for example. A media instance may be
stored on portable storage media such as CD-ROMs or DVD's.
[0295] The adaptive publishing function 2000 of the adaptive
process 900D may apply additional logic or information in
generating adaptive media instances 2030 that may not be available
from the usage aspect 220 of the adaptive system 100. For example,
a record of what objects 212 or information 232 have been contained
in media instances received by particular recipients may be used to
ensure that a recipient does not receive another media instance
that contains information the recipient is likely to have already
seen or heard. (This rule might be relaxed or adjusted, for
example, for non-substantive content that is included for
advertising or promotional purposes.) The adaptive publishing
function 2000 may also include special capabilities for managing
advertising or promotional information within each media instance.
These capabilities seek to optimize or to control advertising or
promotional content such that the content will be of the most value
to consumers or producers of the media instances 2030, while
aligning the frequency and prominence of the advertising or
promotional information with the terms and conditions agreed to by
suppliers of the advertising or promotional content. The
advertising or promotional content may exist within the adaptive
system 100, or may be managed within the adaptive publishing
function 2000.
[0296] The adaptive publishing function 2000 may apply other global
considerations or rules in generating adaptive media instances. For
example, limits on the amount of information within a media
instance may influence the composition of the media instances. The
informational limits may be measured, for example, in terms of the
number of words or number of pages for text-based media, or, for
example, by duration for audio or video-based media. Furthermore,
there may be limits on the number of unique media instances
generated, and in this case the adaptive publishing function 2000
may apply optimization algorithms to determine media instance
composition and arrangement so as to collectively benefit media
recipients 200, 260 while conforming to the limits on the number of
unique media instances.
[0297] The adaptive publishing function 2000 may also apply
specific formatting features to media instances. For example, for
text-based media instances, specific fonts, font-size, colors, line
spacing, and other format variations may be applied in accordance
with inferred preferences of media recipients 200, 260. The
adaptive publishing function 2000 may also apply alternative
languages to media instances in accordance with inferred
preferences of media recipients 200, 260.
[0298] Although not explicitly shown in FIG. 40, information
regarding media instances and the corresponding recipients within
the adaptive publishing function 2000 may be made available to the
adaptive system 100, and constitute another behavioral aspect
incorporated by the usage aspect 220, that can be used by the
adaptive recommendations function 240 in generating subsequent
recommendations.
[0299] Adaptive Commerce
[0300] Adaptive processes may be employed to recommend products or
services 910 based not only on customer 200 buying behaviors and
patterns, but also within the context of auxiliary information or
rules that may be specific to the customer or potential customer
200, the customer's organization, and/or the products and services
purchased.
[0301] According to some embodiments, FIG. 41 depicts an adaptive
process 900E, or adaptive commerce process, which includes the
functions of an adaptive commerce application. A commerce
contextualization function 2100 within the adaptive computer-based
application 925 provides additional contextualization to the
adaptive system 100 for use by the adaptive recommendations
function 240. The commerce contextualization function 2100 may
deliver information to the adaptive system 100 directly 2141 to the
adaptive recommendations function 240, or through information
transfer 2140 to the usage aspect 220. It should be understood that
the commerce contextualization function 2100 may be external to the
adaptive computer-based application 925, in some embodiments, and
transfer information to the adaptive computer-based application
925; which may, in turn, transfer the information to the adaptive
system 100. It should also be understood that although the commerce
contextualization function 2100 is shown in FIG. 41 to be external
to the adaptive system 100, some or all of the functions of
commerce contextualization function 2100 could alternatively be
internal to the adaptive system 100. For example, some or all of
the information associated with the commerce contextualization
function 2100 could be directly derived from process participant
behaviors 920 and stored and processed in usage aspect 220.
[0302] The commerce contextualization function 2100 of the adaptive
process 900E includes one or more functional elements, each of
which may include relevant information and procedures or
algorithms. As shown in FIG. 41, the commerce contextualization
function 2100 may include a customer context function 2110, a
purchase history function 2120, and a product/service attributes
function 2130. The customer context function 2110 includes
contextualization information associated with the commercial
process participants 200, or customers, that are not available
through inferences from customer behaviors 920. For example, for
business customers, the customer context function 2110 may include
information regarding office site and layout or other business
environment-related information. Such information may prove useful
in providing recommendations 910 that are most relevant given the
business environment of the customer. As another example,
pertaining to recommendations to consumers, the customer
contextualization function 2110 may contain information on family
members of a particular customer, including gender, age, etc.,
thereby enabling tuning of recommendations 910 (as one example, in
the case of gift recommendations) appropriately.
[0303] The commerce contextualization function 2100 may also, or
alternatively, include a purchase history function 2120. This
function includes a mapping of customers to purchases of products
or services over time. This information can be used by the adaptive
recommendations function 240 to deliver more effective
recommendations 910. For example, purchase patterns that are
embedded in the information associated with the purchase history
function 2120, combined with usage behaviors 920, may enable the
recommendation function 240 to generate improved recommendations
910 through incorporation of insights associated with purchase
timing patterns. For example, it may be determined by application
of the purchase history function 2120 that a certain business
customer buys certain products only twice a year, and always in
conjunction with another product type. The recommendations 910 may
then be appropriately aligned with this pattern.
[0304] The commerce contextualization function 2100 may also, or
alternatively, include a product or service attributes function
2130. This function includes additional information or context for
product or services. As an example, for durable products or goods,
a schedule of depreciation may be included in the product/service
attributes function 2130. Such information may enable the adaptive
recommendations function 240 to tune recommendations to be
consistent with the expected lifespan of previously purchased
products.
[0305] The customer context function 2110, the purchase history
function 2120, and the product/service attributes function 2130 may
be applied independently, or collectively, in providing additional
information to adaptive system 100 to be used by the adaptive
recommendations function 240.
[0306] Adaptive commerce applications may be applied to adaptive
price discovery processes, so as to more advantageously determine
prices for products or services. Thus, an adaptive process 900F, or
adaptive price discovery process, is depicted in FIG. 42, according
to some embodiments. In addition to the commerce contextualization
function 2100, the adaptive computer-based application 925 may
include, or have access to, a price discovery function 2150 that
provides input to the adaptive recommendations function 240 of the
adaptive system 100.
[0307] Process participant behaviors 920 may be used to infer
conscious or unconscious intensity of desire for a product or
service, or a collection of products or services. Based on these
inferences, as well as information or rules 2151 from the price
discovery function 2150, and optionally, information from the
commerce contextualization function 2100, the adaptive
recommendations function 240 generates adaptive recommendations 910
that include prices for products or services that, in some
embodiments, are optimized to yield the highest price that is
expected to achieve a sale of the associated product or service to
the process participant 200. In other words, the price may be set
at a level that is expected to maximize the seller's capture of the
buyer's economic rent. The process participant behaviors and
associated inferences may be transferred 2152 from the adaptive
recommendations function 240 to the price discovery function 2150.
Other contextual information may be applied by the combination of
the price discovery function 2150 and the adaptive recommendations
function 240 to price appropriately. For example, the price
optimization may be adjusted as appropriate based on whether the
sales transaction is expected to constitute a one-time
relationship, or whether future transactions may take place. The
results from the recommended prices 910 may be used to determine
inferred price sensitivities and elasticities 2155 for one or more
process participants 200. Thus, the price discovery function 2150
may supply useful information 2151 to the adaptive recommendations
function 240, enabling optimal product pricing; likewise, the
adaptive recommendations function 240 may supply useful information
2152 to the price discovery function 2150 for determining prices,
price elasticities, or other pricing functions.
[0308] The price discovery function 2150 may include a price
discovery experimental design function that is applied to optimize
the testing of prices through the adaptive system 100. Hence, the
combination of the price discovery function 2150 and the adaptive
system 100 can constitute a "closed" loop adaptive pricing function
that applies insights on process participant 200 behaviors 920 to
adjust pricing. In some embodiments, the price discovery function
2150 may apply the methods and systems described in U.S.
Provisional Patent Application Ser. No. 60/652,578, entitled
"Adaptive Decision Process."
[0309] The adaptive price discovery function 2150 may employ price
discovery and pricing methods other than setting a fixed price for
a product or service. For example, the function 2150 may apply a
bidding processes in which multiple process participants 200 bid on
the product or service, or other collective price formation that
utilize direct or indirect interactions among process participants
200.
[0310] The adaptive price discovery function 2150 may utilize other
supplier contextual information to establish prices. This
information may be accessed directly from the commerce
contextualization function (not shown), or from 2152 the adaptive
recommendations function 240. This information may include the
associated inventory level, production cost, production plan,
and/or other supply chain considerations that may be relevant in
establishing price levels for a product or service.
[0311] This adaptive pricing approach of the adaptive process 900F
may be particularly applicable in setting prices for collections,
combinations or "bundles" of products and services that may be
specific or even unique to a given customer or set of customers
200. The uniqueness of the bundle enables the provision of a
maximum value-add to the customer by fine-tuning a recommended
"solution" to a perceived customer need that is comprised of
multiple products or services. Such a customized solution can
increase the value, or economic rent, to the customer. But, the
uniqueness of the bundle also decreases the ability of the customer
to "comparison shop," and this reduced transparency enables the
supplier to potentially capture a greater portion of the value-add
of the customer. Hence, there is an opportunity for the supplier to
create more value for customers and to prominently share in the
increased value.
[0312] FIG. 43 depicts an adaptive process 900G, or adaptive
commercial solutions process. In addition to featuring the adaptive
system 100, commerce contextualization function 2100, and price
discovery function 2150, the adaptive process 900G includes a
product and/or service bundling function 2160 within the adaptive
computer-based application 925. (A specific product/service bundle
or combination may be termed a "solution.") The product/service
bundling function 2160 provides information 2161a to the adaptive
recommendations function 240 to enable adaptive recommendations 910
to include product/service bundles or solutions to process
participants 200 that are expected to be relevant or compelling to
the process participants 200. Likewise, the adaptive
recommendations function 240 provides information 2161b associated
with inferences on the preferences or interests of process
participants or customers 200. The product/service bundling
function 2160 may be applied in concert, or interact with 2162, the
price discovery function 2150; together comprising a solution
development and pricing process. The adaptive recommendations
function 240 may combine inputs from the product/service bundling
function 2160, the price discovery function 2150, and the commerce
contextualization function 2100, along with information from the
usage aspect 220 in generating recommendations that may include
solutions and associated pricing.
[0313] The product/service bundling function 2160 may provide
suggested product or service configurations 2161a, in addition to,
or instead of, product and service bundle suggestions or options
2161a. The term "configurations" as used herein in conjunction with
the product/service bundling function 2160 denotes a set of product
or service features. For example, various product components or
features may be combined on a customized basis for a specific
customer or customers 200. One example is the customization of the
configuration of a personal computer--a specific CPU, with specific
storage devices, peripherals, monitor type, etc., may be suggested
by the product/service bundling function 2160 based on information
2161b on inferred preferences from the adaptive recommendations
function 240.
[0314] Continuing the example, the suggested customized personal
computer may then be bundled by the product/service bundling
function 2160 with a digital camera and a special warranty that
encompasses both the personal computer and the camera. This bundle
of products and services may then be specially priced by the price
discovery function 2150, with the entire bundle of products and
services, the configurations of the products and services, and
bundle pricing being informed by the inferred preferences and
interests of process participants (customers) 200.
[0315] The product/service bundling function 2160 and adaptive
price discovery function 2150 may be applied together to create a
bidding process for product/service bundles. The product/service
bundling function 2160 may generate bundles or solutions applicable
to multiple process participants 200, and the adaptive price
discovery function 2150 is used to organize and manage the bids.
The adaptive computer-based application 925 may use the adaptive
system 100 and the product/service bundling function 2160 to
determine the best mix of bundles and process participants to
maximize the value of the auction.
[0316] The product/service bundling function 2160 and adaptive
price discovery function 2150 may utilize other supplier contextual
information to establish solutions and associated prices. This
information may be accessed directly from the commerce
contextualization function (not explicitly shown in FIG. 43), or
indirectly from 2152, 2161b the adaptive recommendations function
240. This supplier contextual information may include the
associated inventory level, production cost, production plan,
and/or other supply chain considerations that may be relevant in
establishing price levels for one or more products or services,
and/or configurations thereof.
[0317] Location-Aware Adaptive Sales and Marketing
[0318] Recall from Table 1 that process participant behaviors 920
may include behaviors associated with physical location, and the
movement among physical locations, of process participants 200.
According to some embodiments, the adaptive process 900 may be
applied to enable sales or procurement-related processes in which
the sales processes of a potential supplier monitor physical
locations of potential customers 200 and deliver adaptive
recommendations 910 that are appropriately contextualized for the
customer's location, or location history. Further, the customers or
potential customers 200 may themselves employ systems that interact
at varying levels of interaction and cooperation with the
supplier's sales processes. Where both the supplier and the
potential customers employ adaptive recombinant processes and the
potential customer and/or the potential supplier is mobile, a
location-aware collectively adaptive system and associated
location-aware collectively adaptive commercial process 900H is
enabled
[0319] FIG. 44 depicts a location-aware collectively adaptive
process 900H, including a location-aware collectively adaptive
system 2200. Four separate instances of adaptive computer
applications within system 2200 are shown; each instance
corresponds to an instance of the adaptive computer-based
application 925 of FIGS. 4A and 4B. Two of the instances are mobile
adaptive computer applications; a first mobile adaptive
computer-based application 925m1, and a second mobile adaptive
computer-based application 925m2. Two of the instances are
stationary adaptive computer applications, a first stationary
adaptive computer-based application 925s1, and a second stationary
adaptive computer-based application 925s2. Each of the adaptive
computer-based application instances may interact with any of the
other instances, as depicted by the flow of information 2210
between the first stationary adaptive computer-based application
instance 925s1 and the first mobile adaptive computer-based
application instance 925m1.
[0320] The information flow 2210 between any two adaptive
computer-based application instances of the location-aware
collectively adaptive system 2200 may include the following: [0321]
1) Polling and detection of a second adaptive computer-based
application instance by a first adaptive computer-based application
instance. [0322] 2) Identifying the detected second adaptive
computer-based application instance by the first adaptive
computer-based application instance. [0323] 3) Determining a mutual
contextual basis for further interaction--that is, either a) from
the potentially supplier-side adaptive computer-based application
instance, determining whether the potential receiving or
customer-side adaptive computer-based application instance
encompasses a customer context or set of inferred preferences or
interests that would enable one or more relevant recommendations
910 to be generated for the process participants 200 of the
customer-side adaptive computer-based application instance; or b)
from the potentially receiving or customer-side adaptive
computer-based application instance, determining whether the
supplier-side adaptive computer-based application instance
encompasses a supplier context and product or service attributes
that would enable an expected one or more relevant recommendations
910 to be generated for the process participants 200 of the
customer-side adaptive computer-based application instance. This
determination of a mutual contextual basis for further interaction
may be made by one or the other, or both instances. [0324] 4)
Receiving from, or supplying to, the second adaptive computer-based
application instance contextualized information that enables either
a) the adaptive recommendations 910 of the first adaptive
computer-based application instance to selectively utilize the
contextualized information of the second adaptive computer-based
application instance; or b) enables the adaptive recommendations
910 of the second adaptive computer-based application instance to
selectively utilize the contextualized information of the first
adaptive computer-based application instance. It should be noted
that the interactions 2210 may occur between any two adaptive
computer-based applications 925. For example, the interactions 2210
may be between two stationary adaptive computer-based application
instances, such as the information flow 2250 between instance 925s1
and instance 925s2. Or the information flow 2230 may be between two
mobile adaptive computer application instances, such as instance
925m1 and instance 925m2. Finally, the interactions 2220 may be
between a stationary adaptive computer-based application instance
925s1 and a mobile adaptive computer-based application instance
925m2.
[0325] According to some embodiments, FIGS. 45 and 46 depict two
examples of location-aware collectively adaptive systems 2200. FIG.
45 (2200A) provides additional details regarding the constituent
adaptive computer application instances, and the interactions among
the instances, of the location-aware collectively adaptive system
2200 of FIG. 44. A stationary adaptive computer application
instance 925s includes an adaptive system 100 and a supplier
commerce contextualization function 2300 (see FIG. 43). The
supplier commerce contextualization function 2300 is comprised of
one or more of 1) a supplier context function 2310, 2) a purchase
history function 2120, and 3) a product and service attribute
function 2130. Although not shown in FIG. 45, the supplier commerce
contextualization function 2300 may also include a customer context
function 2110. The supplier context function 2310 includes
contextual information about the potential supplier that is
utilizing or applying the adaptive computer-based application
instance 925s, that is not contained in product and service
attributes function 2130. For example, supplier context function
2310 may include the physical location of the supplier, the hours
of business, the history of the business, and any other information
that may be relevant to a customer or prospective customer. The
adaptive system 100 of the adaptive computer-based application 925s
interacts 2305 with the supplier commerce contextualization
function 2300, as desired, to deliver effective adaptive
recommendations 910s to process participants 200s.
[0326] The stationary adaptive computer-based application instance
925s interacts 2415 with the mobile adaptive computer-based
application instance 925m. The mobile adaptive computer-based
application instance 925m includes an adaptive system 100 and a
mobile customer commerce contextualization function 2400. The
mobile customer commerce contextualization function 2400 includes
one or both of a 1) customer context function 2110 and 2) a
preferences and interests function 2420. The preferences and
interests function 2420 contains inferred preferences and interests
of process participants 200m based on their interactions with
adaptive system 100.
[0327] The stationary adaptive computer-based application instance
925s initially interacts 2415 with the mobile adaptive
computer-based application instance 925m through an initial
detection by one or the other of the instances, or through mutual
detection. Next, an interaction 2425 is invoked that seeks to
establish a basis for commercial interaction between the two
instances. Information from mobile customer commerce
contextualization function 2400 is compared to information in the
supplier commerce contextualization function 2300. So for example,
a service station employing instance 925s detecting a mobile
process participant 200m that is a child riding a bicycle is
unlikely to have a basis for initiating a commercial interaction,
and therefore interactions would cease, whereas if the mobile
process participant 200m was a truck driver driving a truck that
was due for service, then a basis for commercial interaction may
exist.
[0328] The adaptive computer-based application instances 925s, 925m
may apply location information, or inferences derived from location
and time, in establishing a context for commercial interaction or
for generation of adaptive recommendations within the
location-aware collectively adaptive system 2200. The adaptive
computer-based application instances 925s, 925m may utilize
geographic-related context or information such as through access to
digitized maps in making inferences from location and time
information associated with process participants 200.
[0329] For example, the respective physical locations of two or
more instances may be a determinant of a basis for commercial
interaction or for generating adaptive recommendations. The
prospective customer or prospective supplier may have thresholds of
distance that may be applied to determine a basis for commercial
interaction. This threshold distance may be in absolute terms, or
in terms of expected transit time between a mobile adaptive
computer-based instance and a stationary instance or another mobile
instance. Inferred direction and speed of a mobile instance may be
calculated and used as input to establishing context for commercial
interaction or for generating adaptive recommendations. Further,
the inferred mode of transportation of the mobile process
participant 200 may be a determinant for commercial interaction or
generation of recommendations, as such information may affect the
expected transit time or ease of access to the supplier.
[0330] Assuming that a basis for commercial interaction is
established, a next level of interaction 2435 may be established
between the two instances 925m, 925s. The preferences and interests
2420 of the mobile adaptive computer-based instance 925m are
accessed by the stationary adaptive computer-based instance 925s to
determine if there is a basis for providing suggested products or
services to the mobile adaptive computer instance 925m. If the
supplier commerce contextualization function 2300 determines that
there is a basis for suggesting or recommending products, then
these are transmitted 2445 to mobile adaptive computer application
instance 925m.
[0331] The suggested products or services 2445 are incorporated by
the adaptive recommendations function 240 of the adaptive system
100 of mobile adaptive computer-based application 925m in
generating recommendations 910m to process participants 200m.
[0332] FIG. 46 (2200B) illustrates that the mobile adaptive
computer-based application instance 925m, along with the associated
process participants 200m, may be considered the process
participants 200sm of the stationary adaptive computer-based
application instance 925s. The interactions described in FIG. 45
are conducted through the process participant behaviors 920
transmission to the instance 925s, and through the adaptive
recommendations 910s generated by instance 925s and received by
process participants 200sm. Although in FIG. 46, the respective
adaptive application instances 925s, 925m are stationary and
mobile, respectively, it should be understood that the example may
be reversed, or two stationary or two mobile instances may utilize
the same topology for interactions, as depicted in FIG. 46.
[0333] The location-aware collectively adaptive system 2200 and
process 900H (FIG. 44) may be applied to a variety of sales and
procurement process areas. For example, restaurants can apply such
processes by providing prospective diners that are in the vicinity
of relevant recommended options, tuned to the prospective diner's
particular preferences and tastes.
[0334] The location-aware collectively adaptive system 2200 and
process 900H may further apply the adaptive price discovery systems
and processes of FIG. 42 or the adaptive commercial solutions
systems and process of FIG. 43.
[0335] A mobile adaptive computer application instance 82bm1 may be
embodied within a portable computing device, such as a mobile phone
or personal digital assistant (PDA). A mobile adaptive computer
application instance 82bm1 may be contained in mobile apparatus,
such as vehicles or other transportation devices. In some
embodiments, a mobile adaptive computer application instance 82bm1
may reside within a self-propelled device or appliance.
[0336] Adaptive Viral Marketing
[0337] In the prior art, viral marketing techniques are known that
promote the initial recipients of a sales or marketing-related
message to re-send the message to others. For example, viral
marketing through e-mail messages is a familiar technique. However,
prior art viral marketing techniques exhibit two significant
limitations: 1) there is little ability for a recipient to easily
modify the received message for the benefit of others he or she
will re-send the message to, and 2) the structure of the message is
typically a single item of information embodied in a single
computer file (such as a e-mail message, or a text document).
[0338] According to some embodiments, an application of adaptive
recombinant process 901, adaptive recombinant process 901B, may be
used to advantageously transform customer relationships, promote
sales, facilitate business development, enhance public relations or
generally increase "share of mind." In contrast to the prior art,
through the application adaptive recombinant process 901B, content
networks or process networks comprising multiple units of
interconnected information may be syndicated to potential customers
or individuals or institutions for whom influence is sought. The
content or process networks may then be syndicated to the
customer's customers or influence targets, and so on, potentially
without limit. At each stage of syndication and receipt, one or
more content or process networks may be modified or combined,
optionally enabled by an adaptive recommendations function 240. The
content within the syndicated content networks may be substantive
or non-substantive (e.g., advertising or promotional content). This
application of adaptive recombinant process 901B provides a much
more powerful and comprehensive approach to viral marketing and
public relations than is possible with prior art approaches.
[0339] FIG. 47 illustrates an adaptive recombinant systems
construct to manage syndication and recombination of network
structures for a variety of process purposes, including enabling
adaptive viral marketing process 901B. Recall from FIGS. 16 and 17
that the adaptive recombinant computer-based application 925R may
include the adaptive recombinant system 800C, which in turn, may
encompass the adaptive system 100C (FIG. 14). In the embodiment of
FIG. 47, the adaptive system 100C manages multiple networks within
the structural aspect 210C. These networks may be content networks
or process networks, and may be fuzzy networks. For example, some
or all of "network 1" 2510 may be syndicated 2515 to "network 2"
2520 and combined, followed by some or all of the resulting network
combination syndicated 2525 to "network 3" 2530 and combined with
"network 3" 2520. A closed loop may be formed, as some or all of
this last network combination may be syndicated 2535 back to the
original "network 1" 2510 and combined with "network 1" 2510. This
process may continue indefinitely. At each stage, it should be
understood that a network may be syndicated to a recipient that
does not possess a network. Such a recipient may nevertheless
modify the network and re-syndicate. For each stage, the selection,
syndication, modification, or combination is enabled by the
functions of the adaptive recombinant system 800C, as described
previously. Thus, the adaptive recommendations function 240 may be
applied to facilitate these syndications, modifications, and
combinations based, in part, on inferences of preferences and
interests from the usage behaviors 920 of process participants
200.
[0340] FIG. 48 illustrates an alternative adaptive recombinant
systems construct using an adaptive recombinant system 800i to
manage syndication and recombination of network structures for a
variety of process purposes, including enabling adaptive viral
marketing process 901B. Adaptive recombinant system 800i includes
multiple instances of adaptive system 100i. Although not shown in
FIG. 48, each adaptive system instance, such as adaptive system
100i1, may have its own independent set of process participants
200, or the process participants 200 of each adaptive system
instance may overlap.
[0341] In the embodiment of FIG. 48, each adaptive system instance
100i manages one or more networks within its structural aspect 210
(not shown). These networks may be content networks or process
networks, and may be fuzzy networks. As an example, some or all of
the structural aspect and/or usage aspect of the first adaptive
system instance 100i1 may be syndicated 2555 to a second adaptive
system instance 100i2, and the structural and/or usage aspects
optionally combined. Some or all of the structural and/or usage
aspects of the second adaptive system instance 100i2 may then be
syndicated 2565 to a third adaptive system instance 100i3, and the
structural and/or usage aspects optionally combined. A closed loop
may be formed, as some or all of the structural and/or usage
aspects of the third adaptive system instance 100i3 may be
syndicated 2575 back to the original adaptive system instance
100i1.
[0342] Thus, the process of syndication, modification, and
combination may continue indefinitely. At each stage, it should be
understood that an entire adaptive system instance 100i may be
syndicated to a recipient that does not have access to the adaptive
system instance 800i1. And at each stage, the selection,
syndication, modification, or combination is enabled by the
functions of the adaptive recombinant system 800, as described
previously. Thus, the adaptive recommendations function 240 of each
adaptive system instance 100i may be applied to facilitate these
syndications, modifications, and combinations based, in part, on
inferences of preferences and interests from usage behaviors 920 of
process participants 200.
[0343] The systems and methods described in FIG. 47 and FIG. 48 may
be applied to enabling adaptive viral marketing process 901B, in
some embodiments, as depicted in FIGS. 49A and 49B. In FIGS. 49A
and 49B, the syndication and recombination of content networks
across organization are described. It should be understood that the
content networks described may or may not be fuzzy networks, and
may or may not be process networks. It should also be understood
that the networks may include usage behavioral information
associated with the usage aspect 220, in addition to, or instead of
content networks associated with structural aspect 210c of the
adaptive system 100. Further, although the syndication is to
"organizations," it should be understood that the term as used
herein may include a single person.
[0344] FIG. 49A depicts a the selection or sub-setting of content
network "network 1" 2735 residing in "organization 1" 2650 to form
"network 1a" 2695. "Network 1a" 2695 may contain substantive or
non-substantive information (such as advertising or promotional
content), and is syndicated to "organization 2" 2655 for the
purposes of either direct promotion, with an option for indirect
promotion through re-syndication by "organization 2" 2655; or the
syndication to "organization 2" 2655 may be for the primary or sole
purpose of indirect promotion through "organization 2's" 2655
expected re-syndication of the network.
[0345] In this example, "network 1a" 2700 and the existing "network
2" 2705 in "organization 2" are combined 2710 to form "network 2a"
2715 in "organization 2" 2655. This combination may be either for
the direct benefit of "organization 2" 2655, or the purposes of
continuing the chain of promotion through re-syndication of a
network of substantive and/or non-substantive information that is
expected to be increasingly valuable to each new generation of
recipients.
[0346] Continuing the example, "network 2a" 2715 is then syndicated
to "organization 3" 2660, wherein "organization 3" 2660 does not
already possess or have access to a content network.
[0347] FIG. 49B represents a continuation of FIG. 49A to depict the
potentially closed-loop aspect of the adaptive viral marketing
process. "Network 2a" 2725 in "organization 3" 2660 is syndicated
to "organization 1" 2655. "Network 2a" 2725 is then combined with
the original "network 1" 2735 in "organization 1" 2650 to generate
"network 3" 2740 in "organization 1" 2650.
[0348] FIGS. 49A and 49B demonstrate that, in some embodiments, the
adaptive recombinant process 901B may, without limit, enable
sub-setting of networks of substantive and/or non-substantive
information, syndicating the subsets to one or more destinations,
and enabling the syndicated networks to be combined with one or
more process networks at the destinations. At each combination
step, functions of adaptive recombinant system 800C may be applied,
including the relationship resolution functions and the adaptive
recommendations function, to create and update process structure
(and content) as appropriate. The participants 200 in the adaptive
viral marketing process may or may not be directly conscious of
playing a role in marketing or promotion.
[0349] As a specific example of the economics of viral marketing,
the originator of the adaptive viral marketing process 901B may
supply a product or service for which there are complementary
products or services; by complementary, it is meant that the
supplier can sell more of its product or services to a customer if
the customer has access to, or can purchase, the complementary
products or services. So, for example, commentary by other process
participants, particularly process participants with special
expertise of relevant reputation, may be a complement to selling a
tangible or intangible product, such as a video. Through the
initiation of the viral marketing approach, delivery or targeted,
complementary commentary may be efficiently achieved that could
stimulate greater demand for the video itself.
[0350] The adaptive viral marketing process 901B of FIGS. 49A and
49B may also apply methods associated with location-aware
collectively adaptive system 2200 and process 900H, and may further
apply the systems and methods of the adaptive commercial solutions
process (900G) depicted in FIG. 43.
[0351] Evolvable Processes
[0352] According to some embodiments, the adaptive recombinant
process 901 may be used to deploy an evolvable process 901E across
one or more organizations or environments. FIG. 50 depicts an
embodiment of the adaptive recombinant computer-based application
925R of FIG. 4C, which includes an evolvable adaptive recombinant
system 800e, which itself includes the adaptive recombinant
function 850. The adaptive recombinant function 850 in turn
includes a syndication function 810, a fuzzy network operators
function 820, and an object evaluation function 830, all of which
were described previously. The evolvable adaptive recombinant
system 800e also contains one or more instances 100i of the
adaptive system 100. Process participants 200 generate process
usage behaviors 920 that are tracked and processed by the one or
more adaptive system instances 800i. In addition, the evolvable
adaptive recombinant system 800e contains a network evaluation
function 860, which is used to evaluate the "fitness" of one or
more content networks, which may include process networks, and
works in concert 2905 with the adaptive recombinant function 850 to
generate new generations of content networks from a previous
generation of content networks deemed to be most fit by the network
evaluation function 860.
[0353] Recall from FIG. 47 that an instance of the adaptive system
100 may contain multiple content networks. The network evaluation
function 860 may evaluate 2915 one or more networks within an
adaptive system instance 100i3. The adaptive recombinant function
850 may then be applied to create a new generation of recombinant
content networks within the adaptive system instance 100i3, based
on the individual fitness of the previous generation of content
networks.
[0354] Alternatively, the network evaluation function 860 may
evaluate 2935 content networks across adaptive systems instances
100i. The adaptive recombinant function 850 may then be applied to
create a new generation of recombinant content networks across
adaptive system instances 100i, based on the individual fitness of
the previous generation of content networks across system instances
100i.
[0355] The network evaluation function 860 may apply criteria
derived from inferences on preferences and interests of usage
behaviors 920 of process participants 200. These criteria may be
augmented by additional evaluation criteria and logic as
required.
[0356] The adaptive recombinant function 850 may generate new
generations of content networks based on purely the inheritance of
characteristics derived from combinations of previous generations
of content networks (Lamarkian approach to network evolution),
and/or the adaptive recombinant function 850 may apply random
changes to the content networks, so as to create network mutations,
which, in turn, increases network variation (Darwinian approach to
network evolution). Genetic algorithms may be applied to generate
network mutations and combinations.
[0357] Evolvable adaptive recombinant system 800e can therefore
enable the evolvable process 901E, which can serve as a means of
accelerating the development of the most adaptive possible
processes for a given organizational environment.
Computing Infrastructure
[0358] FIG. 51 depicts various hardware topologies that the
adaptive process 900, the adaptive recombinant process 901, the
adaptive computer-based application 925, the adaptive recombinant
computer-based application 925R, the adaptive system 100, or the
adaptive recombinant system 800 may embody. Further, the adaptive
asset management process 900A, the adaptive real-time learning
process 900B, the innovation network process 900C, the adaptive
publishing process 900D, the adaptive commerce process 900E, the
adaptive price discovery process 900F, the adaptive commercial
solutions process 900G, the location-aware collectively adaptive
process 900H, the recombinant process network process 901A, the
adaptive viral marketing process 901B, the evolvable process 901E,
or other applications of the adaptive process 900 or adaptive
recombinant process 901 not described herein may utilize the
hardware and computing topologies of FIG. 51. These various systems
are referred to as the "relevant systems," below.
[0359] Servers 950, 952, and 954 are shown, perhaps residing at
different physical locations, and potentially belonging to
different organizations or individuals. A standard PC workstation
956 is connected to the server in a contemporary fashion. In this
instance, the relevant systems, in part or as a whole, may reside
on the server 950, but may be accessed by the workstation 956. A
terminal or display-only device 958 and a workstation setup 960 are
also shown. The PC workstation 956 may be connected to a portable
processing device (not shown), such as a mobile telephony device,
which may be a mobile phone or a personal digital assistant (PDA).
The mobile telephony device or PDA may, in turn, be connected to
another wireless device such as a telephone or a GPS receiver.
[0360] FIG. 51 also features a network of wireless or other
portable devices 962. The relevant systems may reside, in part or
as a whole, on all of the devices 962, periodically or continuously
communicating with the central server 952, as required. A
workstation 964 connected in a peer-to-peer fashion with a
plurality of other computers is also shown. In this computing
topology, the relevant systems, as a whole or in part, may reside
on each of the peer computers 964.
[0361] Computing system 966 represents a PC or other computing
system, which connects through a gateway or other host in order to
access the server 952 on which the relevant systems, in part or as
a whole, reside. An appliance 968, includes software "hardwired"
into a physical device, or may utilize software running on another
system that does not itself host the relevant systems. The
appliance 968 is able to access a computing system that hosts an
instance of one of the relevant systems, such as the server 952,
and is able to interact with the instance of the system.
[0362] The relevant systems may utilize database management
systems, including relational database management systems, to
manage to manage associated data and information, including objects
and/or relationships among objects. The relevant systems may apply
intelligent "swarm" peer-to-peer file sharing techniques to
facilitate the syndication of large networks of content, by
enabling a plurality of peer computing devices to collectively
serve as file servers, thus acting to de-bottleneck the sharing of
large networks of information. Further, adaptive recombinant
processes may apply intelligent swarm peer-to-peer sharing to the
entire network of information (objects and relationships) that is
to be syndicated, rather than just individual files. The relevant
systems may apply special algorithms to optimally syndicate
elements of one or more networks of information across a plurality
of peer computing devices to enable the collective set of peer
computing devices to be utilized as servers in a manner to enable
the most efficient syndication of large-scale networks of
information.
[0363] While the present invention has been described with respect
to a limited number of embodiments, those skilled in the art will
appreciate numerous modifications and variations therefrom. It is
intended that the appended claims cover all such modifications and
variations as fall within the scope of this present invention.
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