U.S. patent application number 12/893958 was filed with the patent office on 2011-01-27 for adaptive knowledge lifecycle management methods.
This patent application is currently assigned to MANYWORLDS, INC.. Invention is credited to Steven Dennis Flinn, Naomi Felina Moneypenny.
Application Number | 20110022564 12/893958 |
Document ID | / |
Family ID | 38069231 |
Filed Date | 2011-01-27 |
United States Patent
Application |
20110022564 |
Kind Code |
A1 |
Flinn; Steven Dennis ; et
al. |
January 27, 2011 |
Adaptive Knowledge Lifecycle Management Methods
Abstract
In accordance with the embodiments described herein, methods for
adaptive knowledge lifecycle management and associated services
supplied to customers are described. Adaptive knowledge lifecycle
management provides a means for beneficially adapting knowledge
assets and their organizing structures, including workflow. The
adaptive features may be based on inferences of preferences derived
from user behaviors, or inferences of knowledge asset subject
matter. Fees may be established and charged for performing adaptive
knowledge lifecycle management services.
Inventors: |
Flinn; Steven Dennis;
(Houston, TX) ; Moneypenny; Naomi Felina;
(Houston, TX) |
Correspondence
Address: |
MANYWORLDS, INC.
510 BERING DRIVE, SUITE 470 (IP DEPARTMENT)
HOUSTON
TX
77057
US
|
Assignee: |
MANYWORLDS, INC.
Houston
TX
|
Family ID: |
38069231 |
Appl. No.: |
12/893958 |
Filed: |
September 29, 2010 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11552489 |
Oct 24, 2006 |
7831535 |
|
|
12893958 |
|
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60732810 |
Nov 2, 2005 |
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Current U.S.
Class: |
706/52 ; 705/400;
706/46 |
Current CPC
Class: |
G06Q 99/00 20130101;
G06Q 40/06 20130101; G06Q 30/0283 20130101 |
Class at
Publication: |
706/52 ; 706/46;
705/400 |
International
Class: |
G06N 7/02 20060101
G06N007/02; G06N 5/02 20060101 G06N005/02; G06Q 30/00 20060101
G06Q030/00 |
Claims
1. A method of knowledge lifecycle management comprising:
establishing a plurality of computer-implemented knowledge assets;
establishing a computer-implemented organizing structure of the
knowledge assets; implementing an adaptive knowledge lifecycle
management process; and modifying the computer-implemented
organizing structure in accordance with the knowledge lifecycle
process and inferred user preferences.
2. The method of claim 1, further comprising: performing an
adaptive knowledge lifecycle management process step, the step
being selected from a group consisting of knowledge acquisition,
knowledge architecture, knowledge distribution, and knowledge
delivery and management.
3. The method of claim 1, further comprising: establishing a
process workflow; and integrating the process workflow into the
organizing structure of the knowledge assets.
4. The method of claim 3, further comprising: adapting
automatically the process workflow based, at least in part, on
inferences of user preferences.
5. The method of claim 1, further comprising: accessing usage
behaviors of one or more users of the plurality of the knowledge
assets; assessing values for the plurality of knowledge assets
based, at least in part, on usage behaviors associated with the
plurality of knowledge assets; and adapting automatically the
organizing structure of the knowledge assets consistent with the
assessed values of the knowledge assets.
6. The method of claim 1, further comprising: modifying the
organizing structure in accordance with an inference of knowledge
asset subject matter.
7. The method of claim 1, further comprising: establishing a fuzzy
network-based organizing structure.
8. A method of knowledge management services comprising:
establishing a plurality of computer-implemented knowledge assets
and a corresponding computer-implemented organizing structure;
establishing a fee based, at least in part, on automatically
performing modifications to the plurality of knowledge assets and
the corresponding organizing structure; and charging the fee to a
customer in return for performing the modifications to the
knowledge assets and the organizing structure.
9. The method of claim 8, further comprising: establishing a
relationship topology among knowledge assets, the relationship
topology being selected from a group comprising a hierarchy, a
network, and a fuzzy network.
10. The method of claim 8, further comprising: performing an
adaptive knowledge asset lifecycle management process step.
11. The method of claim 8, further comprising: assessing a value
for a plurality of the knowledge assets based, at least in part, on
the number of the plurality of knowledge assets and the organizing
structure of the knowledge assets.
12. The method of claim 8, further comprising: performing
automatically the modifications to the plurality of knowledge
assets and the corresponding organizing structure based, at least
in part, on inferred preferences.
13. The method of claim 8, further comprising: performing
automatically the modifications to the plurality of knowledge
assets and the corresponding organizing structure based, at least
in part, on inferences of the knowledge asset subject matter.
14. A method comprising: establishing a plurality of
computer-implemented knowledge assets; establishing a
computer-implemented organizing structure of the knowledge assets
comprising workflow; determining automatically a value for a
knowledge asset; and modifying automatically the workflow based, at
least in part, on the value of the knowledge asset.
15. The method of claim 14, further comprising: determining the
value for the knowledge asset based on inferred preferences.
16. The method of claim 14, further comprising: determining the
value for the knowledge asset based on the inferred subject matter
of the knowledge asset.
17. The method of claim 14, further comprising: charging a fee for
modifying the workflow.
18. The method of claim 14, further comprising: performing an
adaptive knowledge lifecycle management process step.
19. The method of claim 14, further comprising: establishing a
fuzzy network organizing structure.
20. The method of claim 14, further comprising: generating an
adaptive recommendation.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application is a continuation of U.S. patent
application Ser. No. 11/552,489, which claimed priority under 35
U.S.C. .sctn.119(e) to U.S. Provisional Patent Application Ser. No.
60/732,810, entitled "Adaptive Knowledge Lifecycle Management,"
filed on Nov. 2, 2005.
FIELD OF INVENTION
[0002] This invention relates to the management of knowledge and
computer-based information assets.
BACKGROUND
[0003] Knowledge management has been a focus of businesses and
other types of organizations for the past several decades. There
has been a wide-spread recognition that more effective and
efficient management of knowledge is critical for most businesses.
Nevertheless, there has historically been a disappointment in the
actual value generated by knowledge management initiatives.
[0004] There have been several reasons for this disappointment.
First, the informational scope of most knowledge management
approaches has traditionally been limited to information that is in
the form of text-based and/or multi-media-based documents, or more
broadly, "unstructured" information. What this informational scope
omits is "structured" information such as, for example, financial
information. Further, this historical domain of knowledge
management has not typically encompassed other types of "knowledge
assets" such as computer-based interactive programs, and human
resource-based assets (i.e., people).
[0005] A second cause for disappointment with knowledge management
is that there has been a lack of useful quantification
methodologies associated with knowledge assets, and which
explicitly encompass the organizing structures of the knowledge
assets. This has made it difficult to establish a credible baseline
of knowledge asset and management value, and to thereby measure
improvement from the baseline. In other words, in the prior art
there has been little transparency with regard to which knowledge
assets are truly valuable and which are not, on either an absolute
or relative basis.
[0006] A third cause for disappointment with knowledge management
is that computer-based knowledge management systems have been
insufficiently automatically adaptive, requiring the need for
significant on-going manual effort to keep collections of knowledge
assets well organized for multiple purposes or applications. After
heroic initial manual efforts to effectively organize knowledge
assets, knowledge asset "entropy" inevitably increases over time,
and the knowledge assets and their structure becoming decreasingly
useful.
[0007] In addition to these causes of disappointment, knowledge
management initiatives have historically been primarily internally
managed by businesses and institutions. Web Services, or more
broadly, on-demand computing approaches, have generally not been
applied since knowledge management-related software has typically
not been available in Web services form, and collaborative
knowledge management among one or more knowledge management
suppliers and a knowledge management customer, including technology
and/or services, has been awkward to implement. This has limited
the value that third party suppliers could deliver to customers in
the area of knowledge management, and reduced the ability of
knowledge management customers to leverage third party
capabilities.
[0008] Hence, there is a need for an improved method and system for
managing collections of broadly defined knowledge assets, for
valuing alternative organizing approaches associated with the
knowledge assets, and for delivering knowledge management oriented
technology and services to customers.
SUMMARY OF INVENTION
[0009] In accordance with the embodiments described herein, a
method and system for adaptive knowledge lifecycle management is
disclosed. The present invention may integrate with ManyWorlds'
knowledge lifecycle methods, including process lifecycle methods,
as well as knowledge and content lifecycle methods. Adaptive
knowledge lifecycle management may furthermore integrate with the
ManyWorlds' Generative Investment.TM., Adaptive Decision Processes
and Adaptive Recombinant Processes methods and systems.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] FIG. 1A is a flow diagram of an adaptive knowledge lifecycle
process, according to some embodiments;
[0011] FIG. 1B is a block diagram of an adaptive knowledge
lifecycle management service model, according to some
embodiments;
[0012] FIG. 2 is a block diagram describing a knowledge lifecycle
management process and associated supporting content and computer
applications, according to some embodiments;
[0013] FIG. 3 is a block diagram describing an adaptive knowledge
lifecycle management process and associated supporting content and
computer applications, according to some embodiments;
[0014] FIG. 4 is a block diagram of an adaptive knowledge asset
management system and process, according to some embodiments;
[0015] FIG. 5 is a block diagram of an a real-time learning system
interface, according to some embodiments;
[0016] FIG. 6 is a diagram of alternative computing topologies of
adaptive recombinant processes, according to some embodiments;
[0017] FIG. 7 is a diagram of a web services-based adaptive
knowledge lifecycle management technical configuration according to
some embodiments;
[0018] FIG. 8 is a diagram of knowledge and content lifecycle
strategy including a knowledge and content lifecycle model
according to some embodiments;
[0019] FIG. 9 is a diagram of additional details of the knowledge
and content lifecycle model of FIG. 8, according to some
embodiments;
[0020] FIG. 10 is a diagram of additional details of the knowledge
and content lifecycle model of FIG. 8, according to some
embodiments; and
[0021] FIG. 11 is a diagram of mapping information consumer
segments to the knowledge and content lifecycle model of FIG. 8,
according to some embodiments.
DETAILED DESCRIPTION
[0022] In the following description, numerous details are set forth
to provide an understanding of the present invention, adaptive
knowledge lifecycle management. 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.
[0023] In accordance with the embodiments described herein, a
method and a system for adaptive knowledge lifecycle management,
third party services thereof, and valuation of knowledge assets is
disclosed.
DEFINITIONS
[0024] "Knowledge Assets" is defined herein to include any
computer-based information, including documents, Web sites,
graphics, audio, video, interactive computer applications, and any
other type of executable software. In some contexts herein,
"knowledge assets" may imply that the corresponding computer-based
information has some implicit or explicit value. Knowledge assets
may be nested, so that a specific knowledge asset may contain one
or more other knowledge assets.
[0025] "Business process" or "process" is defined herein as a set
of activities that collectively perform a business or
non-business-related 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. It should be understood that the descriptions of
processes and related features, and the application of adaptive
knowledge lifecycle management, extends to non-business
institutions and organizations.
[0026] "Semi-automatic" or "semi-automatically," as used herein, is
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.
[0027] "Process participants," as defined herein is synonymous with
"system users" or "users" and 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.
[0028] A process "activity" as defined herein typically represents
a unit of work to be conducted in a prescribed manner by one or
more participants in a process, and possibly according to a
prescribed workflow. However, as defined herein, an activity may
also simply constitute a process participant action or behavior.
For example, a process participant 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.
[0029] "Usage behaviors" is defined herein to include the
interactions of one or more users with a computer-based system, or
the monitoring of behaviors of one or more individuals by a
computer-based system. Usage behaviors may include, but are not
limited to the categories described in Table 1.
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 or 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
physiological responses direction of gaze brain patterns blood
pressure heart rate environmental conditions current location and
location location over time relative location to users/object
references current time current weather condition
[0030] Referring to Table 1 and FIG. 3 (which is described in more
detail in a later section), 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.
[0031] 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.
[0032] 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.
[0033] 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.
[0034] 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.
[0035] 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.
[0036] 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.
[0037] 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.
[0038] A sixth category of process usage behaviors is known as
physiological responses. These responses or behaviors are
associated with the focus of attention of users and/or the
intensity of the intention, or any other aspects of the
physiological responses of one or more users 200. For example, the
direction of the visual gaze of one or more users may be
determined. This behavior can inform inferences associated with
preferences and/or intentions or interests even when no physical
interaction with the one or more computer-based systems 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 users. Such patterns of brain
functions during participation in a process can inform inferences
on the preferences and/or intentions or interests of users, and the
intensity of the preferences and/or intentions 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.
[0039] Physiological responses may include any other type of
physiological response of a user 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, heart rate, blood pressure, or galvanic
response.
[0040] A seventh category of process usage behaviors is known as
environmental conditions and physical location behaviors. Physical
location behaviors identify physical location and mobility
behaviors of users. The location of a user may be inferred from,
for example, information associated with a Global Positioning
System or any other positionally or locationally aware system or
device, or may be inferred directly from location information input
by a user (e.g., a zip code or street address), or otherwise
acquired by the computer-based systems 925. The physical location
of physical objects referenced by elements or objects of one or
more computer-based systems 925 may be stored for future reference.
Proximity of a user to a second user, 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 users 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 or
calculations may be made from location and time data, such as the
direction of the user, 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.
Environmental conditions may include the time of day, the weather,
lighting levels, sound levels, and any other condition of the
environment around the one or more users 200.
[0041] 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.
[0042] 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.
Adaptive Knowledge Lifecycle Management Services and Solutions
[0043] In accordance with some embodiments of adaptive knowledge
lifecycle management, FIG. 1A depicts a process flow associated
with an adaptive knowledge lifecycle process 1000. In some
embodiments, an adaptive knowledge lifecycle process is defined to
have four phases 1000. The first phase 1010 is the knowledge
acquisition phase. This phase encompasses activities related to the
development or acquisition of information or knowledge. The second
phase is the knowledge architecture phase 1020. This phase
encompasses designing and implementing structures to effectively
manage information or knowledge. The third phase is the knowledge
distribution phase 1030. This phase encompasses distributing
information or knowledge to processes, systems, Web-sites, or other
media in which the information is accessed and/or processed by one
or more users. The fourth phase is the adaptive delivery and
management phase 1040. This phase encompasses contextualized
delivery of information or knowledge to one or more users or
process participants. This phase also encompasses managing the
lifecycle of information or knowledge based on the inferred value
of the information or knowledge, which may be derived, at least in
part, from explicit or inferred user preferences or interests. The
adaptive delivery and management phase 1040 may provide input 1041
to the previous phases, thus constituting a feedback loop.
[0044] It will be understood that in other embodiments that
knowledge lifecycle phases may be labeled or categorized
differently.
[0045] In accordance with some embodiments of adaptive knowledge
lifecycle management, FIG. 1B depicts an overall service
provisioning model associated with a first organization supplying
lifecycle management services to a second organization.
[0046] Adaptive knowledge lifecycle management solutions 1100
represent a set of services that one or more companies or
individuals (which may be designated hereinafter "supplier")
provide to support the management of knowledge lifecycle process
1000 of another company, organization, or individual (which may be
designated hereinafter "customer").
[0047] In some embodiments, adaptive knowledge lifecycle management
solutions 1100 is comprised of four categories of services, each
category corresponding to a phase of the knowledge lifecycle
1000.
[0048] A first category of services 1110 relates to the knowledge
acquisition phase 1010 of the knowledge lifecycle 1000. Knowledge
acquisition services 1110 may include customized external
information acquisition 1112, which maybe delivered to the customer
organization on a periodic or continuous basis. Knowledge
acquisition services 1102 may include scanning and/or monitoring of
content from one or more sources, may include filtering the content
for quality and/or relevancy, and may include adding corresponding
meta-information such as a review or overview, information on
source, author, publish date, etc., and rankings, based on
attributes such a quality, relevancy to the customer, etc.
[0049] Knowledge acquisition services 1110 may include "high
grading" the customer's internal content to knowledge assets 1114.
This may include scanning and/or monitoring of content from one or
more internal sources, may include filtering the content for
quality and/or relevancy, and may include adding corresponding
meta-information such as a review or overview, information on
source organization, author, publish date, etc., and rankings,
based on attributes such as quality, relevancy for various
purposes, etc.
[0050] Knowledge acquisition services 1110 may include facilitating
networks of customer internal knowledge contributors 1116. This may
include organizing the contributors, providing training,
guidelines, and/or editorial support to the internal knowledge
contributors.
[0051] A second category of services 1120 relates to the knowledge
architecture phase 1020 of the knowledge lifecycle 1000. Knowledge
architecture services 1120 may include design and management of
knowledge structures 1122. Design and management of knowledge
structures 1122 may include designing the topology of a plurality
of items of information or knowledge assets, including designing
relationships among the information or knowledge assets, and may
include determining physical storage locations for information or
knowledge assets. The topology or structure associated with a
plurality of knowledge assets may include hierarchical models,
relational models, or network models. Network-based topologies may
be fuzzy (the relationship or relationships between any two objects
may vary by degree) or non-fuzzy (the relationship or relationships
between any two objects either exist or do not exist).
[0052] Knowledge architecture services 1120 may include enhancing
or amplifying the value of existing customer information, content,
and/or knowledge assets 1124. Enhancing or amplifying the value of
existing customer information, content, and/or knowledge assets
1124 may include applying designs that seek to maximize
information, content, and/or knowledge asset value through
development of structures in which consumers of the knowledge
assets that would derive the most value from one or more knowledge
assets are able to most easily access or otherwise interact with
the one or more knowledge assets.
[0053] Knowledge architecture services 1120 may include flexibly
re-purposing content or knowledge assets for an unlimited number of
applications 1126. Flexibly re-purposing content or knowledge
assets for an unlimited number of applications 1126 may include
designing or managing a plurality of content or knowledge assets
and an associated topology so as to maximize content or knowledge
asset re-use for multiple purposes or application areas.
[0054] Knowledge architecture services 1120 may include integrating
relevant knowledge directly into business processes 1128.
Integrating relevant knowledge directly into business processes
1128 may include identifying knowledge requirements of a business
process and designing knowledge structures so that the required
knowledge is accessible during operation of the business
process.
[0055] A third category of services 1130 relates to the knowledge
distribution phase 1030 of the knowledge lifecycle 1000. The
knowledge distribution services 1130 may include dynamically
delivering and managing knowledge across multiple Intranet,
Extranet, and/or Internet sites 1132. Dynamically delivering and
managing knowledge across multiple Intranet, Extranet, and/or
Internet sites 1132 may include applying a computer-based system
that automatically or semi-automatically delivers knowledge assets
to Web-sites that may be accessed by a browser.
[0056] The knowledge distribution services 1130 may include
managing knowledge across organizations and/or businesses 1134.
Managing knowledge across organizations and/or businesses 1134 may
include applying a computer-based system to maintain appropriate
security and controls, and managing organization or
business-specific knowledge assets, and/or organization or
business-specific user interfaces.
[0057] The knowledge distribution services 1130 may include
delivering relevant knowledge during specific process activities
1136. Delivering relevant knowledge during specific process
activities 1136 may include applying a computer-based system to
deliver relevant knowledge assets to process participants, based,
at least in part, on system usage behaviors of one or more of the
process participants.
[0058] A fourth category of services 1140 relates to the adaptive
knowledge delivery and management phase 1040 of the knowledge
lifecycle 1000. The adaptive knowledge delivery and management
services may include applying computer-based adaptive
recommendations that include built-in learning capabilities 1142.
Applying computer-based adaptive recommendations that include
built-in learning capabilities 1142 may include applying a search
and/or recommendation engine that learns to become more effective
over time through the inferencing of customer user preferences and
interests based, at least in part, on the historical usage
behaviors of one or more users. In some embodiments, the
recommendation engine may be applied to update computer-based
systems and/or content, thereby making adaptive, and persistent,
modifications to the customers' systems and/or content.
[0059] The adaptive knowledge delivery and management services 1140
may include enabling adaptive business processes 1144. Enabling
adaptive business processes 1144 may include delivering adaptive
recommendations to process participants, and/or may include making
adaptive, and persistent, modifications to the systems and/or
content that support the processes.
[0060] The adaptive knowledge delivery and management services 1140
may include aligning asset lifecycle management with knowledge
asset values derived, at least in part, from user preferences or
interests 1146. In some embodiments, the user preferences or
interests are inferred, at least in part, from system usage
behaviors.
Knowledge Asset Valuation
[0061] In accordance with some embodiments of adaptive knowledge
lifecycle management, valuation of knowledge assets and the
corresponding organizing topology knowledge assets may be
conducted. The valuation may be conducted as an element of adaptive
lifecycle management solutions 1100.
[0062] In some embodiments the valuation of knowledge assets may be
conducted through application of "network effect" modeling. That
is, the knowledge assets and their organizing topology are modeled
as a network, where knowledge assets represent nodes in the
network, and the organizing topology is manifested as relationships
among the knowledge assets (nodes). In the prior art, network
effect models have been applied to obvious networks such as telecom
networks, the Internet, etc., to provide rough estimates of value,
or at least, relative value. The present invention goes beyond the
prior art by modeling general computer-based assets of an
organization as a network, where the nodes represent any
computer-based unit of information, such as documents, multi-media,
interactive applications, models, and transactional information.
The level of abstraction may be varied for different valuation
purposes. For example, a database may be considered a node among a
network of other databases. Alternatively, or in addition, the data
elements of a database may be considered nodes.
[0063] Network effect modeling provides a means of estimating value
of networks as additional linked nodes are added. The classic
network effect model is Metcalfe's law, which estimates the value
of a network to be roughly proportional N.sup.2 (or more precisely
((N.sup.2-N)/2)), where N is the number of nodes in the network.
Metcalfe's law implicitly assumes the value of all relationships
within the network are equal. This will infrequently be a good
assumption for networks of general knowledge assets--for most
applications it will be an over-estimate.
[0064] Rather, information locality effects will inevitably be
important for most types of networks; that is, a node that in some
sense or dimension is "closer" is likely to be more valuable. In
such cases, value will increase more than linearly as nodes are
added to the network, but less than for Metcalfe's law. A preferred
network effect model for application to general collections of
knowledge assets is:
Network value=Nln(N), (1)
where N is the number of nodes, and "ln" is the natural logarithm
(the logarithm may be any other base without loss of generality).
It will be appreciated that variations of formula (1) may be
applied by the present invention as well. The rationale for the
Nln(N) network effect model is described in the white paper "A
Refutation of Metcalfe's Law and a Better Estimate for the Value of
Networks and Network Interconnections," Odlyzko and Tilly, 2005.
This model provides more sensible results for valuing alternative
configurations or topologies on general knowledge assets. Network
value models of the NInN type, or variants thereof, are consistent
with a network locality factor consistent with Zipf's law, which
has been found to effectively model the locality factors of a
variety of real-world networks.
[0065] In some embodiments, an automated system may be applied to
determine the knowledge assets and organizing topology. This
automated system may then compute a value for the collection of
knowledge assets directly, or serve as input to a second system
that computes the value. The system may be applied to generate a
base line value, and may also be applied to generate a new value
after the organizing topology has been modified.
[0066] An alternative or additional valuation modeling approach
that may be applied by the present invention is the application of
modeling techniques derived from experimental design. This method
makes the underlying assumption that knowledge assets have value to
the extent they provide information that can influence one or more
decisions.
[0067] A simple model experimental design model presented in the
book "Experimentation Matters", Thomke, 2003, and with more detail
provided in the paper "Sequential Testing in Product Development",
Thomke and Bell, 2001, is:
Optimal Number of Test Rounds=(a/t).sup.0.5 (2)
where "a" is the avoidable cost (or alternatively, value) if
problems (or alternatively, opportunities) are found earlier, and
"t" is the cost of one round of tests.
[0068] In other words, this formula balances the cost of tests or
information gathering versus the expected benefits of the test
results (or more generally, information) in improving decision
making. Therefore, formula (2) may be extended to derive the
expected value of test information by assuming that since the cost
of the tests at the margin must equal the benefits or value of the
testing given rationale investment in testing (or more generally,
information), then:
Value of Information=((a/t.sub.0).sup.0.5)*t.sub.0 (2a)
where "t.sub.0" represents the initial unit cost of a test,
experiment, or most broadly, an item of information.
[0069] The present invention extends beyond the prior art by
extending the formula (2), or variations thereof, to any collection
of knowledge assets through application of formula (2a). If the
cost of accessing and making use of the knowledge assets can be
modeled as contributing to decisions, then organizing topologies,
access and/or processing methods, and analytical methods that
effectively decrease the cost of applying the knowledge assets to
decisions enables use of formula (2a) to derive estimates of
aggregate knowledge asset value.
[0070] To derive the impact of information or knowledge asset cost
reductions on knowledge asset valuation, it is first assumed that
the unit cost of accessing and/or applying an item of information
is reduced to t.sub.1. Using formula (2a) as a baseline, we can
compute the value of information cost reduction by decomposing
information cost reduction into an efficiency effect and an
effectiveness effect as follows:
Efficiency Value Ratio of Information Cost Reduction t 1 = ( ( ( a
/ t 0 ) 0.5 ) * t 1 ) ( ( ( a / t 0 ) 0.5 ) * t 0 ) = t 1 / t 0 ( 2
b ) ##EQU00001##
Therefore, if information costs are reduced by 50%, then the
efficiency value is increased by about 50%, as the same original
value is generated at half the cost.
[0071] However, there is also an increased effectiveness value that
relates to an increased number of tests (or use of information)
that is applied as the cost of the tests or information is
decreased. That value is calculated as follows:
Effectiveness Value Ratio of Information Cost Reduction
t.sub.1=((((a/t.sub.1).sup.0.5)*t.sub.1)-(((a/t.sub.0).sup.0.5)*t.sub.1))-
/(((a/t.sub.0).sup.0.5)*t.sub.0) (2c)
So, in other words, if information costs are reduced by 50%, then
the effectiveness value is increased by about 20%. Therefore the
total value of information cost reduction of 50% per unit of
information generates about an extra 70% of value.
[0072] The network effect models, as exemplified by formula (1)
represent an information relationship-based approach to aggregate
knowledge asset valuations. The experimental design models, as
exemplified by formula (2a), represent a decision analytic/cost of
information approach to aggregate knowledge asset valuations. The
present invention provides novel benefits versus the prior art in
applying either of these two approaches to general knowledge asset
valuation. Further, the present invention may use these approaches
in combination to generate knowledge asset valuations.
[0073] Both approaches are most effective in generating a relative
change in value versus a baseline value. In some embodiments, the
baseline value of a collection of knowledge assets may be
determined from financial analysis of the organization associated
with the knowledge assets. For example, the financial returns,
measured as a net present value or other financial return metric,
of a collection of knowledge assets may be determined, either
retrospectively or prospectively. This may serve as a baseline on
which the results of knowledge asset valuation models based on
network effects and/or experimental design are applied.
Computer-Based Implementations of Adaptive Knowledge Lifecycle
Management
[0074] FIG. 2 illustrates a general approach to information and
computing infrastructure support for implementation of a general
business process by a computer application-supported process. Some
or all of the elements of the adaptive knowledge lifecycle
management solutions 1100 may be applied to support a customer's
business process. The elements of the adaptive knowledge lifecycle
management solutions 1100 may include activities, procedures,
frameworks, models, algorithms, and sub-processes, and may map to
customer process activities, sub-processes, processes, and/or
workflow. It should be understood that FIG. 2 represents an
exemplary process instantiation of a customer's process.
[0075] In FIG. 2, 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.
[0076] For example, content 180 may be accessed 180a (a content
access 180a) as an activity 170 is executed. The term "content", or
alternatively, knowledge assets, 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 participants 200blm in the knowledge lifecycle
management process 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.
[0077] FIG. 3 depicts the application of adaptive knowledge
lifecycle management solutions 1100 to support a process or
sub-process, according to some embodiments. Adaptive knowledge
lifecycle management solutions 1100 may apply the methods and
systems disclosed in PCT Patent Application No. PCT/US2005/011951,
entitled "Adaptive Recombinant Processes," filed on Apr. 8, 2005,
which is hereby incorporated by reference as if set forth in its
entirety.
[0078] In FIG. 3, the adaptive knowledge lifecycle management
process 900 may include many of the features of the
computer-supported process in FIG. 2. 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.
[0079] One or more participants 200blm 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.
[0080] Process participants 200blm 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.
[0081] The adaptive knowledge lifecycle management 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. 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.
[0082] FIG. 3 also depicts adaptive recommendations 910 being
generated and delivered by the adaptive computer-based application
925 to process participants 200blm. The adaptive recommendations
910 are shown being delivered to one or more process participants
200blm 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 200blm
during any activity or any other point during participation in a
process or sub-process.
[0083] 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 knowledge
lifecycle management process 900 can adapt over time to become
increasingly effective.
[0084] 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. 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
200blm derived from process usage behaviors, or the modifications
may be based on inferences of preferences or interests of process
participants 200blm 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.
[0085] 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.
[0086] 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.
[0087] 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.
[0088] In addition to adaptive recommendations 910 being delivered
to process participants 200blm, process participants 200blm 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 200blm is analogous to the interactions 182a with
computer application 182 of FIG. 2. 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 200blm 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.
[0089] As shown in FIG. 3, it should be noted that at least some,
but not necessarily all, of computer-based applications and content
175 supporting process 930 are included in adaptive knowledge
lifecycle management solutions 1100.
[0090] Recall from FIG. 3 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, or
most broadly, knowledge 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.
[0091] FIG. 4 depicts an aspect of adaptive knowledge lifecycle
management solutions 1100 in generating values of individual or
collections of knowledge assets, and automatically managing the
knowledge assets consistent with their valuation. The asset
management system 1500 includes the adaptive computer-based
application 925 and a knowledge asset management function 1510.
Although in FIG. 4, the knowledge 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.
[0092] The knowledge 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 knowledge 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 knowledge 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 knowledge asset management
function 1510, and appropriate asset modifications or development
projects initiated. For some modifications, the knowledge asset
management function 1510 may be used to support making the
appropriate changes.
[0093] The knowledge 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 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 system 925 (represented in parentheses).
Further, other computer applications and content 175 may be
automatically or semi-automatically modified 1525 by the knowledge
asset management function 1510 in accordance with valuations
derived by knowledge 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.
[0094] Note that adaptive recommendations 910 delivered to process
participants 200 is not an essential feature for adaptive knowledge
lifecycle management solutions 1100.
Adaptive Real-Time Learning
[0095] The adaptive knowledge lifecycle management solutions 1100
may be used to establish an adaptive process environment 930 to
promote enhanced learning by process participants or users 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 knowledge and activities that effectively "surrounds"
the process user. This approach facilitates the real-time learning
of process participants or users 200, rather than relying solely or
primarily on classroom or other episodic forms of education or
training.
[0096] FIG. 5 illustrates 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. 5 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. 5, 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. 3 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.
[0097] 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.
[0098] 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.
Computing Infrastructure
[0099] FIG. 6 depicts various hardware topologies that the adaptive
knowledge lifecycle management solutions 1100 may embody. 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 adaptive
knowledge lifecycle management solutions, 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.
[0100] FIG. 6 also features a network of wireless or other portable
devices 962. The relevant systems of adaptive knowledge lifecycle
management solutions 1100 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.
[0101] 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.
[0102] The adaptive knowledge lifecycle management solutions 1100
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.
[0103] FIG. 7 represents an exemplary Web Services-based
implementation of adaptive lifecycle management solutions 1100. One
or more supplier environments include adaptive knowledge lifecycle
management solutions 1100, which in turn includes an adaptive
computer application 925, supplier-owned knowledge assets 925k,
knowledge architects 1121, and knowledge architects that directly
support customers 1121s. One or more customer environments include
users that access customer knowledge assets 2010 and/or supplier
knowledge assets 925k through an adaptive interface 1600 that
accesses adaptive computer application 925.
[0104] Within adaptive knowledge lifecycle management solutions
1100, adaptive computer-based application 925 is connected 925c to
supplier knowledge assets 925k, which may be on the same or
different computers, and some or all of the supplier knowledge
assets 925k may reside in the customer environment, within the
customer firewall 2000. Adaptive computer-based application 925 is
comprised of application logic 925l and knowledge asset
descriptions and statistics 925s. The knowledge asset descriptions
and statistics may include descriptions and/or usage behavior
statistics associated with supplier knowledge assets 925k and/or
customer knowledge assets 2010. Although in FIG. 7, the knowledge
asset descriptions and statistics 925k is shown residing within the
supplier environment, some or all of the knowledge asset
descriptions and statistics 925k may reside in the customer
environment, within the customer firewall 2000.
[0105] Knowledge architects 1121 interact with adaptive computer
application 925, and thereby manage supplier knowledge assets 925k.
Managing the supplier knowledge assets 925k may include adding new
knowledge assets, modifying existing knowledge assets, or deleting
knowledge assets. Managing supplier knowledge assets may also
include modifying the structure, topology and/or relationships
among the supplier knowledge assets 925k.
[0106] Within the customer environment, users interact with an
adaptive interface 1600, which interacts 2001, 2002 with adaptive
computer application 925. In some embodiments, the one or more
customers access 2002 adaptive computer application 925 through a
firewall 2000. In some embodiments, users are authenticated prior
to an establishment of a connection 2001 to internal 2003 and/or
external computing systems 925 or knowledge assets.
[0107] Supporting knowledge architects 1121s within adaptive
knowledge lifecycle management 1100 may access customer knowledge
assets directly 2004 as shown in FIG. 7 (using, for example, a
virtual private access method to access customer knowledge assets
2010 through the customer firewall 2000), or, through adaptive
computer application 925. For either access approach, supporting
knowledge architects may manage customer knowledge assets 2010.
Managing the customer knowledge assets 2010 may include adding new
knowledge assets, modifying existing knowledge assets, or deleting
knowledge assets. Managing supplier knowledge assets may also
include modifying the structure, topology and/or relationships
among the customer knowledge assets 2010.
[0108] Customer knowledge assets 2010 and supplier knowledge assets
925k may be virtually combined via adaptive computer application
925 to create a combined collection of knowledge assets for
customer users. Supporting knowledge architects 1121s may manage
this combined collection of knowledge assets.
Knowledge and Content Lifecycle Process Management
[0109] Ins some embodiments, adaptive lifecycle management
solutions 1100 may apply the methods and systems disclosed in U.S.
patent application Ser. No. 11/153,632, entitled "Method for
Business Lifecycle Management," filed on Jun. 15, 2005, and in U.S.
patent application Ser. No. 11/154,068, entitled "Business
Lifecycle Management System," filed on Jun. 15, 2005, which are
hereby incorporated by reference as if set forth in their
entirety.
[0110] Knowledge management, content management, learning processes
and communications are all related concepts, and all important to
business performance. Communications--the transmission and receipt
of information, knowledge or content--underpins most of human
affairs.
[0111] According to some embodiments, consumers of information or
knowledge desire two fundamental qualities: 1) an increasingly
large amount of information should be available to them, and 2)
they want to be increasingly selective in their consumption of the
information. Unfortunately, these two desires generally compete--in
other words, there is a trade-off. This simply follows from the
fact that, although information is highly valuable, the time of
information consumers have is also highly valuable.
[0112] The following describes the basic elements of information
quantity and selectivity according to some embodiments. First, from
an information quantity standpoint, information consumers desire
two concurrent qualities: 1) they want breadth of information, and
2) they want depth of information. By breadth, it is meant a
boundarylessness of information across categories or domains. By
depth, it is meant the ability to get more and more details of
information within a category or domain. The key point of both of
these information volumetric dimensions is that the fewer
boundaries there are, the better--because boundaries are costly for
information consumers. When there are boundaries, either
information cannot be found at all, or time is wasted in having to
jump the boundary to seek related information. Taken together, we
can refer to the breadth and depth as the comprehensiveness of a
set of information.
[0113] According to some embodiments, from the standpoint of
information selectivity, information consumers also want two
qualities: 1) they want the highest possible quality of
information, and 2) they want information that is most relevant to
their particular requirements. By quality of information, it is
meant that which is the most recent, most authoritative on the
subject, and most free of extraneous information. By relevant, it
is meant information that is most focused on the consumers'
particular requirements--customized for information consumers'
particular situation, preferences or interests. Taken together, the
combination of quality and relevance can be referred to as the
signal-to-noise ratio, echoing communications theory.
[0114] Information consumers desire both comprehensiveness and high
signal-to-noise rations; however, for any given transmission and/or
delivery mode, there is a trade-off between these communications
attributes. Given a transmission mode, or more broadly, a
communications, knowledge, or content management infrastructure, a
choice can be made regarding the best trade-off between
comprehensiveness and signal-to-noise. It is only possible to make
dual improvements in the attributes by applying more advanced
technologies or infrastructures. The fields of publishing,
broadcasting, telecommunications and computing are all examples of
elements of our communications infrastructure in its broadest
sense.
Knowledge and Content Lifecycle Management
[0115] According to some embodiments, FIG. 8 depicts a knowledge
and content lifecycle model 6000 that applies the dimensions of
signal/noise 6010 and comprehensiveness of information 6020, and
may be applied as part of adaptive lifecycle management solutions
1100. There have always been trade-offs between comprehensiveness
and signal-to-noise ratios, with different information delivery
modes optimized for a particular trade-off choice. For example, in
publishing, periodicals 6040 are often focused on a particular
domain for a particular customer segment, so that the
signal-to-noise ratio is high. On the other hand, the
comprehensiveness is relatively low due to a periodical's focus and
non-continuous format. Newspapers 6050, alternatively, typically
optimize more for comprehensiveness as they are less focused with
regard to both content and customer segment, and they are delivered
more frequently than periodicals. For on-line infrastructure,
domain-specific news alerts (perhaps delivered by e-mail) 6030
represent a high signal/noise ration, but low comprehensiveness. On
the other hand, general computer-based flat file systems 6060
exhibit potentially very high comprehensiveness, but the
signal/noise ration is likely to be low. A trade-off frontier 6015
is collectively determined by the available publishing,
broadcasting, telecommunications, and computer infrastructure
available at a given time to a given organization or
application.
[0116] Changes in infrastructure may enable the trade-off frontier
6015 to beneficially shift outward in model 6000--improving to some
degree in both directions. The Web-based Internet represents such a
break-through in shifting the trade-off curve between
comprehensiveness and signal-to-noise.
[0117] It is true that prior to the Internet, computer-based
applications certainly played an important role in the
communications infrastructure. E-mail was certainly one obvious
example. However, the Internet Protocol (IP) was a significant
advance in that it enabled virtually universal connectivity. And
the advent of the web browser enabled nearly universal publishing
of information or knowledge.
[0118] Nevertheless, even with the application of Internet-based
infrastructure, as shown in FIG. 9, there is still a trade-off 6115
between comprehensiveness and signal-to-noise ratio dimensions,
although a more favorable trade-off than with previous
communications infrastructures.
[0119] For example, highly focused, in-depth and/or exclusive web
sites 6130 can deliver high signal-to-noise, but are relatively low
in comprehensiveness. At the other end of the spectrum, on-line
communities 6160 are typically very comprehensive, but the
signal-to-noise ratio is generally quite low, due to most "content"
being generated by those with a relatively low cost of time--which
indirectly implies limited general demand for their information. In
between these extremes are, for example, general web portals 6140,
and generalized content aggregation services 6150.
[0120] According to some embodiments, as shown in FIG. 10, applying
adaptive lifecycle management solutions 1100 may enable a
beneficial shifting of the trade-off frontier 6215.
[0121] According to some embodiments, the knowledge and content
lifecycle model 6000 implies that it is important to segment
communications and knowledge management approaches according to how
various information consumers at specific times or within specific
contexts prefer to be positioned on the trade-off curve 6215. For
example, for business executives, who have the highest cost of
time, a customized structure that maximizes signal-to-noise at the
expense of some comprehensiveness is critical, while for knowledge
worker communities, optimizing for comprehensiveness is generally
more appropriate. A portfolio of communications approaches and
media may be managed according to information consumer segments.
Information consumer segmentation may be applied as part of
adaptive lifecycle management solutions 1100.
[0122] As an example, FIG. 11 depicts two consumer segments mapped
to the knowledge and content lifecycle model 6000. The first
segment, segment A 6310, is consistent with a set of consumers of
information whose opportunity cost of time is high. Therefore the
portfolio of knowledge management and content delivery approaches
is oriented with high signal/noise ratios at the expense of
comprehensiveness. The knowledge management and content delivery
approaches may span multiple infrastructure types, e.g., publishing
and on-line approaches. Segment B 6320 is consistent with an
information consumer segment that values comprehensiveness
relatively more than high signal/noise rations, presumably due to a
relative low opportunity cost of time.
Business Models
[0123] In some embodiments, the revenue model for adaptive
knowledge lifecycle management solutions 1100 may be based on a
subscription or service fee. Referring to FIG. 7, service fees may
include the activities or results of supplier knowledge architects
1121, and/or direct customer support knowledge architects 1121s.
Revenue models may include licensing fees for supplier
computer-based software 925l, supplier proprietary knowledge assets
925k, and licensing for specific topologies, structures, or other
organizing means of a plurality of supplier knowledge assets 925k
and/or customer knowledge assets 2010. For example, a fuzzy network
model may be applied as an organizing structure or ontology
associated with supplier and/or knowledge assets. The specific
categorizations, relationships and degree of relationships among a
plurality of knowledge assets may be a proprietary asset that is
licensed by suppliers of adaptive knowledge lifecycle management
solutions 1100 to customers.
[0124] 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. For
instance, it may be appreciated that adaptive knowledge lifecycle
management solutions of the present invention may furthermore
integrate with the ManyWorlds Generative Investment.TM., Adaptive
Decision Process and Adaptive Recombinant Processes methodologies.
It is intended that the appended claims cover all such
modifications and variations as fall within the true spirit and
scope of this present invention.
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