U.S. patent application number 12/545851 was filed with the patent office on 2009-12-17 for personalized modeling system.
Invention is credited to Jeffrey Scott Eder.
Application Number | 20090313041 12/545851 |
Document ID | / |
Family ID | 46304257 |
Filed Date | 2009-12-17 |
United States Patent
Application |
20090313041 |
Kind Code |
A1 |
Eder; Jeffrey Scott |
December 17, 2009 |
Personalized modeling system
Abstract
A method, program storage device and system for developing a
Personalized Modeling System (100) for an individual or group of
individuals that automates the operation, customization and
coordination of computer systems, software, products, services,
data and/or devices.
Inventors: |
Eder; Jeffrey Scott; (Mill
Creek, WA) |
Correspondence
Address: |
ASSET TRUST, INC.
2020 MALTBY ROAD, SUITE 7362
BOTHELL
WA
98021
US
|
Family ID: |
46304257 |
Appl. No.: |
12/545851 |
Filed: |
August 23, 2009 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11094171 |
Mar 31, 2005 |
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12545851 |
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10717026 |
Nov 19, 2003 |
7401057 |
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11094171 |
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60432283 |
Dec 10, 2002 |
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60566614 |
Apr 29, 2004 |
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60464837 |
Apr 23, 2003 |
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Current U.S.
Class: |
705/2 ; 705/1.1;
705/36R |
Current CPC
Class: |
Y10S 707/99945 20130101;
G16H 15/00 20180101; G16H 50/20 20180101; G06Q 40/06 20130101; G16H
50/50 20180101; G06N 5/022 20130101 |
Class at
Publication: |
705/2 ; 705/1;
705/36.R |
International
Class: |
G06Q 50/00 20060101
G06Q050/00; G06Q 40/00 20060101 G06Q040/00 |
Claims
1. A personalized planning method, comprising: preparing data from
a plurality of subject related systems for use in processing,
defining a subject using at least a portion of said data and a
plurality of user input, analyzing said data as required to define
and store a context for a health of said subject, using said
context to forecast a sustainable longevity for the subject and a
resource requirement forecast for the subject given said longevity,
and output said forecast longevity and resource requirement
forecast.
2. The method of claim 1, wherein a context for the health of a
subject comprises two or more aspects of a complete context for a
health of said subject selected from the group consisting of a
reference frame context, a resource context, an element context, an
environment context, a measure context, a lexical context, a
relationship context, a transaction context and combinations
thereof.
3. The method of claim 1, wherein a subject comprises an
individual, an individual and his or her immediate family or an
individual and his or her extended family.
4. The method of claim 1, wherein the method further comprises:
obtaining data from a plurality of sources that identify one or
more securities that are available for purchase from one or more
security markets in a format suitable for use in processing where
said data identifies a price history of each of the one or more
securities and a financial performance history for each of the one
or more entities that issued each security, creating a context
model for each security in each of the one or more markets where
said price history data and financial performance history data is
available by analyzing the data related to each security and
market, and identifying and presenting a list of optimal
investments for meeting the resource requirements of the subject
under different scenarios by using the security context models to
simulate future market conditions under each scenario.
5. The method of claim 4, wherein a list of optimal investments is
adjusted to reflect a risk tolerance or an investment preference
provided by the subject.
6. The method of claim 4, wherein a context model for each security
comprises a context model for a market sentiment contribution to a
security value.
7. The method of claim 4, wherein a list of optimal investments is
adjusted to reflect a risk tolerance and an investment preference
provided by the subject.
8. A program storage device readable by a computer, tangibly
embodying a program of instructions executable by a computer to
perform a personalized planning method, comprising: preparing data
from a plurality of subject related systems for use in processing,
defining a subject using at least a portion of said data and a
plurality of user input, analyzing said data as required to define
and store a context for a health of said subject, using said
context for the health of said subject to forecast a sustainable
longevity for the subject and a resource requirement forecast for
the subject given said longevity, and output said forecast
longevity and resource requirement forecast.
9. The program storage device of claim 8, wherein a context for the
health of a subject comprises three or more aspects of a complete
context for a health of said subject selected from the group
consisting of a reference frame context, a resource context, an
element context, an environment context, a measure context, a
lexical context, a relationship context, a transaction context and
combinations thereof.
10. The program storage device of claim 8, wherein a subject
comprises an individual, an individual and his or her immediate
family or an individual and his or her extended family.
11. The program storage device of claim 8, wherein the method
further comprises: obtaining data from a plurality of sources that
identify one or more securities that are available for purchase
from one or more security markets in a format suitable for use in
processing where said data identifies a price history of each of
the one or more securities and a financial performance history for
each of the one or more entities that issued each security,
creating a context model for each security in each of the one or
more markets where said price history data and financial
performance history data is available by analyzing the data related
to each security and market, and identifying and presenting a list
of optimal investments for meeting the resource requirements of the
subject under different scenarios by using the security context
models to simulate future market conditions under each
scenario.
12. The program storage device of claim 11, wherein a list of
optimal investments is adjusted to reflect a risk tolerance or an
investment preference provided by the subject.
13. The program storage device of claim 11, wherein a context model
for each security comprises a dynamic relationship layer.
14. A system for translational research analysis, comprising: a
computer with a processor having circuitry to execute instructions;
a storage device available to said processor with sequences of
instructions stored therein, which when executed cause the
processor to: prepare data from a plurality of subject related
systems for use in processing, define a subject using at least a
portion of said data and a plurality of user input, analyze at
least a portion of said data as required to define and store a
context for a health of said subject, obtain data identifying an
expected impact of a research discovery or a best practice on the
health of a subject, use said context for the health of said
subject to simulate the impact of said research discovery or best
practice on the sustainable longevity of the subject and the
resource requirements for the subject given said longevity, and
report the results of said simulation.
15. The system of claim 14, wherein a causal predictive model for
one or more health measures of a subject comprises one or more
aspects of a complete context for a health of said subject selected
from the group consisting of a reference frame context, a resource
context, an element context, an environment context, a measure
context, a lexical context, a relationship context, a transaction
context and combinations thereof.
16. The system of claim 14, wherein a subject is a patient, two or
more patients or a plurality of patients.
17. The system of claim 14, wherein identifying an expected impact
of a research discovery or a best practice on the health of a
subject comprises providing data regarding the expected impact
using a universal context specification.
18. The system of claim 14, wherein the method further comprises:
obtain data identifying an expected impact of each of a plurality
of research discoveries and each of a plurality of best practices
on the health of a subject, use said context to simulate the impact
of said research discoveries and best practices on the sustainable
longevity of the subject and the resource requirements for the
subject given said longevity, and analyze the results of said
simulation in order to identify and display an optimal set of
research discoveries and best practices that should be translated
and put into practice.
19. The system of claim 18, wherein a subject is a patient, two or
more patients or a plurality of patients.
20. The system of claim 18, wherein identifying an expected impact
of a plurality of research discoveries and a plurality of best
practices on the health of a subject comprises providing data
regarding the expected impacts using a universal context
specification.
Description
RELATED PROVISIONAL APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 11/094,171 filed Mar. 31, 2005 the disclosure
of which is incorporated herein by reference. Application Ser. No.
11/094,171 is a continuation in part of U.S. patent application
Ser. No. 10/717,026 which matured into U.S. Pat. No. 7,401,057 and
a non provisional application of U.S. Provisional Patent
Application No. 60/566,614 filed on Apr. 29, 2004 the disclosures
of which are all also incorporated herein by reference. Application
Ser. No. 10/717,026 claimed priority from U.S. Provisional Patent
Application No. 60/432,283 filed on Dec. 10, 2002 and U.S.
Provisional Patent Application No. 60/464,837 filed on Apr. 23,
2003 the disclosures of which are also incorporated herein by
reference. This application is also related to U.S. Pat. No.
6,018,722, U.S. patent application Ser. No. 10/748,890 filed Jun.
3, 2004 and U.S. patent application Ser. No. 11/142,785 filed May
31, 2005 the disclosures of which are all incorporated herein by
reference. U.S. patent application Ser. No. 10/748,890 is a
continuation of U.S. patent application Ser. No. 10/124,237 filed
Apr. 18, 2002 the disclosure of which is also incorporated herein
by reference.
BACKGROUND OF THE INVENTION
[0002] This invention relates to methods, program storage devices
and systems for developing a Personalized Modeling System (100) for
an individual or group of individuals that supports the operation,
customization and coordination of computer systems, software,
products, services, data, entities and/or devices.
SUMMARY OF THE INVENTION
[0003] It is a general object of the present invention to provide a
novel, useful system that develops and maintains one or more
individual and/or group contexts in a systematic fashion and uses
the one or more contexts to develop a Personalized Modeling System
(100) that supports the operation and coordination of software
including a Complete Context.TM. Suite of services (625), a
Complete Context.TM. Development System (610) and a plurality of
Complete Context.TM. Bots (650), one or more external services (9),
one or more narrow systems (4), entities and/or one or more devices
(3).
[0004] The innovative system of the present invention supports the
development and integration of any combination of data, information
and knowledge from systems that analyze, monitor, support and/or
are associated with entities in three distinct areas: a social
environment area (1000), a natural environment area (2000) and a
physical environment area (3000). Each of these three areas can be
further subdivided into domains. Each domain can in turn be divided
into a hierarchy or group. Each member of a hierarchy or group is a
type of entity.
[0005] The social environment area (1000) includes a political
domain hierarchy (1100), a habitat domain hierarchy (1200), an
intangibles domain group (1300), an interpersonal domain group
(1400), a market domain hierarchy (1500) and an organization domain
hierarchy (1600). The political domain hierarchy (1100) includes a
voter entity type (1101), a precinct entity type (1102), a caucus
entity type (1103), a city entity type (1104), a county entity type
(1105), a state/province entity type (1106), a regional entity type
(1107), a national entity type (1108), a multi-national entity type
(1109) and a global entity type (1110). The habitat domain
hierarchy includes a household entity type (1202), a neighborhood
entity type (1203), a community entity type (1204), a city entity
type (1205) and a region entity type (1206). The intangibles domain
group (1300) includes a brand entity type (1301), an expectations
entity type (1302), an ideas entity type (1303), an ideology entity
type (1304), a knowledge entity type (1305), a law entity type
(1306), a intangible asset entity type (1307), a right entity type
(1308), a relationship entity type (1309), a service entity type
(1310) and a securities entity type (1311). The interpersonal group
includes (1400) includes an individual entity type (1401), a
nuclear family entity type (1402), an extended family entity type
(1403), a clan entity type (1404), an ethnic group entity type
(1405), a neighbors entity type (1406) and a friends entity type
(1407). The market domain hierarchy (1500) includes a multi entity
type organization entity type (1502), an industry entity type
(1503), a market entity type (1504) and an economy entity type
(1505). The organization domain hierarchy (1600) includes team
entity type (1602), a group entity type (1603), a department entity
type (1604), a division entity type (1605), a company entity type
(1606) and an organization entity type (1607). These relationships
are summarized in Table 1.
TABLE-US-00001 TABLE 1 Social Environment Domains Members (lowest
level to highest for hierarchies) Political (1100) voter (1101),
precinct (1102), caucus (1103), city (1104), county (1105),
state/province (1106), regional (1107), national (1108),
multi-national (1109), global (1110) Habitat (1200) household
(1202), neighborhood (1203), community (1204), city (1205), region
(1206) Intangibles brand (1301), expectations (1302), ideas (1303),
ideology Group (1300) (1304), knowledge (1305), law (1306),
intangible assets (1307), right (1308), relationship (1309),
service (1310), securities (1311) Interpersonal individual (1401),
nuclear family (1402), extended family Group (1400) (1403), clan
(1404), ethnic group (1405), neighbors (1406), friends (1407)
Market (1500) multi-entity organization (1502), industry (1503),
market (1504), economy (1505) Organization team (1602), group
(1603), department (1604), division (1600) (1605), company (1606),
organization (1607)
[0006] The natural environment area (2000) includes a biology
domain hierarchy (2100), a cellular domain hierarchy (2200), an
organism domain hierarchy (2300) and a protein domain hierarchy
(2400) as shown in Table 2. The biology domain hierarchy (2100)
contains a species entity type (2101), a genus entity type (2102),
a family entity type (2103), an order entity type (2104), a class
entity type (2105), a phylum entity type (2106) and a kingdom
entity type (2107). The cellular domain hierarchy (2200) includes a
macromolecular complexes entity type (2202), a protein entity type
(2203), a rna entity type (2204), a dna entity type (2205), an
x-ylation** entity type (2206), an organelles entity type (2207)
and cells entity type (2208). The organism domain hierarchy (2300)
contains a structures entity type (2301), an organs entity type
(2302), a systems entity type (2303) and an organism entity type
(2304). The protein domain hierarchy contains a monomer entity type
(2400), a dimer entity type (2401), a large oligomer entity type
(2402), an aggregate entity type (2403) and a particle entity type
(2404). These relationships are summarized in Table 2.
TABLE-US-00002 TABLE 2 Natural Environment Domains Members (lowest
level to highest for hierarchies) Biology (2100) species (2101),
genus (2102), family (2103), order (2104), class (2105), phylum
(2106), kingdom (2107) Cellular* (2200) macromolecular complexes
(2202), protein (2203), rna (2204), dna (2205), x-ylation** (2206),
organelles (2207), cells (2208) Organism (2300) structures (2301),
organs (2302), systems (2303), organism (2304) Protein (2400)
monomer (2400), dimer (2401), large oligomer (2402), aggregate
(2403), particle (2404) *includes viruses **x = methyl, phosphor,
etc.
The physical environment area (3000) contains a chemistry group
(3100), a geology domain hierarchy (3200), a physics domain
hierarchy (3300), a space domain hierarchy (3400), a tangible goods
domain hierarchy (3500), a water group (3600) and a weather group
(3700) as shown in Table 3. The chemistry group (3100) contains a
molecules entity type (3101), a compounds entity type (3102), a
chemicals entity type (3103) and a catalysts entity type (3104).
The geology domain hierarch contains a minerals entity type (3202),
a sediment entity type (3203), a rock entity type (3204), a
landform entity type (3205), a plate entity type (3206), a
continent entity type (3207) and a planet entity type (3208). The
physics domain hierarchy (3300) contains a quark entity type
(3301), a particle zoo entity type (3302), a protons entity type
(3303), a neutrons entity type (3304), an electrons entity type
(3305), an atoms entity type (3306), and a molecules entity type
(3307). The space domain hierarchy contains a dark matter entity
type (3402), an asteroids entity type (3403), a comets entity type
(3404), a planets entity type (3405), a stars entity type (3406), a
solar system entity type (3407), a galaxy entity type (3408) and
universe entity type (3409). The tangible goods hierarchy contains
a money entity type (3501), a compounds entity type (3502), a
minerals entity type (3503), a components entity type (3504), a
subassemblies entity type (3505), an assembly's entity type (3506),
a subsystems entity type (3507), a goods entity type (3508) and a
systems entity type (3509). The water group (3600) contains a pond
entity type (3602), a lake entity type (3603), a bay entity type
(3604), a sea entity type (3605), an ocean entity type (3606), a
creek entity type (3607), a stream entity type (3608), a river
entity type (3609) and a current entity type (3610). The weather
group (3700) contains an atmosphere entity type (3701), a clouds
entity type (3702), a lightning entity type (3703), a precipitation
entity type (3704), a storm entity type (3705) and a wind entity
type (3706).
TABLE-US-00003 TABLE 3 Physical Environment Domains Members (lowest
level to highest for hierarchies) Chemistry Group molecules (3101),
compounds (3102), chemicals (3100) (3103), catalysts (3104) Geology
(3200) minerals (3202), sediment (3203), rock (3204), landform
(3205), plate (3206), continent (3207), planet (3208) Physics
(3300) quark (3301), particle zoo (3302), protons (3303), neutrons
(3304), electrons (3305), atoms (3306), molecules (3307) Space
(3400) dark matter (3402), asteroids (3403), comets (3404), planets
(3405), stars (3406), solar system (3407), galaxy (3408), universe
(3409) Tangible Goods money (3501), compounds (3502), minerals
(3503), (3500) components (3504), subassemblies (3505), assemblies
(3506), subsystems (3507), goods (3508), systems (3509) Water Group
pond (3602), lake (3603), bay (3604), sea (3605), (3600) ocean
(3606), creek (3607), stream (3608), river (3609), current (3610)
Weather Group atmosphere (3701), clouds (3702), lightning (3703),
(3700) precipitation (3704), storm (3705), wind (3706)
Individual entities are items of one or more entity type. The
analysis of the health of an individual or group can be linked
together with a plurality of different entities to support an
analysis that extends across several domains. Entities and patients
can also be linked together to follow a chain of events that
impacts one or more patients and/or entities. These chains can be
recursive. The domain hierarchies and groups shown in Tables 1, 2
and 3 can be organized into different areas and they can also be
expanded, modified, extended or pruned in order to support
different analyses.
[0007] Data, information and knowledge from these seventeen
different domains can be integrated and analyzed in order to
support the creation of one or more health contexts for the subject
individual or group. The one or more contexts developed by this
system focus on the function performance (note the terms behavior
and function performance will be used interchangeably) of a single
patient as shown in FIG. 2A, a group of two or more patients as
shown in FIG. 2B and/or a patient-entity system in one or more
domains as shown in FIG. 2C. FIG. 2A shows an entity (900) and a
function impact network diagram for a location (901), a project
(902), an event (903), a virtual location (904), a factor (905), a
resource (906), an element (907), an action/transaction (908/909),
a function measure (910), a process (911), a subject mission (912),
constraint (913) and a preference (914). FIG. 2B shows a
collaboration (925) between two entities and the function impact
network diagram for locations (901), projects (902), events (903),
virtual locations (904), factors (905), resources (906), elements
(907), action/transactions (908/909), a joint measure (915),
processes (911), a joint mission (916), constraints (913) and
preferences (914). For simplicity we will hereinafter use the terms
patient or subject with the understanding that they refer to a
patient (900) as shown in FIG. 2A, a group of two or more patients
(925) as shown in FIG. 2B or a patient-entity system (950) as shown
in FIG. 2C. While only two entities are shown in FIG. 2B and FIG.
2C it is to be understood that the subject can contain more than
two patients and/or entities.
[0008] After one or more contexts are developed for the subject,
they can be combined, reviewed, analyzed and/or applied using one
or more of the context-aware services in a Complete Context.TM.
Suite (625) of services. These services are optionally modified to
meet user requirements using a Complete Context.TM. Development
System (610). The Complete Context.TM. Development System (610)
supports the maintenance of the services in the Complete
Context.TM. Suite (625), the creation of newly defined stand-alone
services, the development of new services and/or the programming of
context-aware bots.
[0009] The system of the present invention systematically develops
the one or more complete contexts for distribution in a
Personalized Modeling System (100). These contexts are in turn used
to support the comprehensive analysis of subject performance,
develop one or more shared contexts to support collaboration,
simulate subject performance and/or turn data into knowledge.
Processing in the Personalized Modeling System (100) is completed
in three steps: [0010] 1. subject definition and measure
specification; [0011] 2. context and contextbase (50) development,
and [0012] 3. Complete Context.TM. service development and
distribution. The first processing step in the Personalized
Modeling System (100) defines the subject that will be analyzed,
prepares the data from devices (3), entity narrow system databases
(5), partner narrow system databases (6), external databases (7),
the World Wide Web (8), external services (9) and/or the Complete
Context.TM. Input System (601) for use in processing and then uses
these data to specify subject functions as well as function and/or
mission measures.
[0013] As part of the first stage of processing, the user (40)
identifies the subject by using existing hierarchies and groups,
adding a new hierarchy or group or modifying the existing
hierarchies and/or groups in order to fully define the subject. As
discussed previously, each subject comprises one of three types.
These definitions can be supplemented by identifying actions,
constraints, elements, events, factors, preferences, processes,
projects, risks and resources that impact the subject. For example,
a white blood cell entity is an item with the cell entity type
(2208) and an element of the circulatory system and auto-immune
system (2303). In a similar fashion, entity Jane Doe could be an
item within the organism entity type (2300), an item within the
voter entity type (1101), an element of a team entity (1602), an
element of a nuclear family entity (1402), an element of an
extended family entity (1403) and an element of a household entity
(1202). This individual would be expected to have one or more
functions and function and/or mission measures for each entity type
she is associated with. Separate systems that tried to analyze the
six different roles of the individual in each of the six
hierarchies would probably save some of the same data six separate
times and use the same data in six different ways. At the same
time, all of the work to create these six separate systems might
provide very little insight because the complete context for
behavior of this subject at any one period in time is a blend of
the context associated with each of the six different functions she
is simultaneously performing in the different domains. Predefined
templates for the different entity types can be used at this point
to facilitate the specification of the subject (these same
templates can be used to accelerate learning by the system of the
present invention). This specification can include an
identification of other subjects that are related to the entity.
For example, the individual could identity her friends, family,
home, place of work, church, car, typical foods, hobbies, favorite
malls, etc. using one of these predefined templates. The user could
also indicate the level of impact of each of these entities has on
different function and/or mission measures. These weightings can in
turn be verified by the system of the present invention.
[0014] After the subject definition is completed, structured data
and information, transaction data and information, descriptive data
and information, unstructured data and information, text data and
information, geo-spatial data and information, image data and
information, array data and information, web data and information,
video data and video information, device data and information,
and/or service data and information are made available for analysis
by converting data formats before mapping these data to a
contextbase (50) in accordance with a common schema or ontology.
The automated conversion and mapping of data and information from
the existing devices (3) narrow computer-based system databases (5
& 6), external databases (7), the World Wide Web (8) and
external services (9) to a common schema or ontology significantly
increases the scale and scope of the analyses that can be completed
by users. This innovation also gives users (40) the option to
extend the life of their existing narrow systems (4) that would
otherwise become obsolete. The uncertainty associated with the data
from the different systems is evaluated at the time of integration.
Before going further, it should be noted that the Personalized
Modeling System (100) is also capable of operating without
completing some or all narrow system database (5 & 6)
conversions and integrations as it can directly accept data that
complies with the common schema or ontology. The Personalized
Modeling System (100) is also capable of operating without any
input from narrow systems (4). For example, the Complete
Context.TM. Input Service (601) (and any other application capable
of producing xml documents) is fully capable of providing all data
directly to the Personalized Modeling System (100).
[0015] The Personalized Modeling System (100) supports the
preparation and use of data, information and/or knowledge from the
"narrow" systems (4) listed in Tables 4, 5, 6 and 7 and devices (3)
listed in Table 8.
TABLE-US-00004 TABLE 4 Biomedical affinity chip analyzer, array
systems, biochip systems, bioinformatic Systems systems, biological
simulation systems, blood chemistry systems, blood pressure
systems, body sensors, clinical management systems, diagnostic
imaging systems, electronic patient record systems, electrophoresis
systems, electronic medication management systems, enterprise
appointment scheduling, enterprise practice management,
fluorescence systems, formulary management systems, functional
genomic systems, galvanic skin sensors, gene chip analysis systems,
gene expression analysis systems, gene sequencers, glucose test
equipment, information based medical systems, laboratory
information management systems, liquid chromatography, mass
spectrometer systems, microarray systems, medical testing systems,
microfluidic systems, molecular diagnostic systems, nano-string
systems, nano-wire systems, peptide mapping systems,
pharmacoeconomic systems, pharmacogenomic data systems, pharmacy
management systems, practice management systems, protein biochip
analysis systems, protein mining systems, protein modeling systems,
protein sedimentation systems, protein sequencer, protein
visualization systems, proteomic data systems, stentennas,
structural biology systems, systems biology applications,
x*-ylation analysis systems *x = methyl, phosphor.
TABLE-US-00005 TABLE 5 Personal appliance management systems,
automobile management Systems systems, blogs, contact management
applications, credit monitoring systems, gps applications, home
management systems, image archiving applications, image management
applications, folksonomies, lifeblogs, media archiving
applications, media applications, media management applications,
personal finance applications, personal productivity applications
(word processing, spreadsheet, presentation, etc.), personal
database applications, personal and group scheduling applications,
social networking applications, tags, video applications
TABLE-US-00006 TABLE 6 Scientific accelerometers, atmospheric
survey systems, geological Systems survey systems, ocean sensor
systems, seismographic systems, sensors, sensor grids, sensor
networks, smart dust
TABLE-US-00007 TABLE 7 Management accounting systems**, advanced
financial systems, alliance management Systems systems, asset and
liability management systems, asset management systems, battlefield
systems, behavioral risk management systems, benefits
administration systems, brand management systems,
budgeting/financial planning systems, building management systems,
business intelligence systems, call management systems, cash
management systems, channel management systems, claims management
systems, command systems, commodity risk management systems,
content management systems, contract management systems,
credit-risk management systems, customer relationship management
systems, data integration systems, data mining systems, demand
chain systems, decision support systems, device management systems
document management systems, email management systems, employee
relationship management systems, energy risk management systems,
expense report processing systems, fleet management systems,
foreign exchange risk management systems, fraud management systems,
freight management systems, geological survey systems, human
capital management systems, human resource management systems,
incentive management systems, information lifecycle management
systems, information technology management systems, innovation
management systems, instant messaging systems, insurance management
systems, intellectual property management systems, intelligent
storage systems, interest rate risk management systems, investor
relationship management systems, knowledge management systems,
litigation tracking systems, location management systems,
maintenance management systems, manufacturing execution systems,
material requirement planning systems, metrics creation system,
online analytical processing systems, ontology systems, partner
relationship management systems, payroll systems, performance
dashboards, performance management systems, price optimization
systems, private exchanges, process management systems, product
life- cycle management systems, project management systems, project
portfolio management systems, revenue management systems, risk
management information systems, sales force automation systems,
scorecard systems, sensors (includes RFID), sensor grids (includes
RFID), service management systems, simulation systems, six-sigma
quality management systems, shop floor control systems, strategic
planning systems, supply chain systems, supplier relationship
management systems, support chain systems, system management
applications, taxonomy systems, technology chain systems, treasury
management systems, underwriting systems, unstructured data
management systems, visitor (web site) relationship management
systems, weather risk management systems, workforce management
systems, yield management systems and combinations thereof **these
typically include an accounts payable system, accounts receivable
system, inventory system, invoicing system, payroll system and
purchasing system
TABLE-US-00008 TABLE 8 Devices personal digital assistants, phones,
watches, clocks, lab equipment, personal computers, televisions,
radios, personal fabricators, personal health monitors,
refrigerators, washers, dryers, ovens, lighting controls, alarm
systems, security systems, hvac systems, gps devices, smart
clothing (aka clothing with sensors), personal biomedical
monitoring devices, personal computers
After data conversions have been identified the user (40) is asked
to specify entity functions. The user can select from pre-defined
functions for each subject or define new functions using narrow
system data. Examples of predefined subject functions are shown in
Table 9.
TABLE-US-00009 TABLE 9 Entity type Example Functions Organism
(2300) reproduction, killing germs, maintaining blood sugar
levels
Pre-defined quantitative measures can be used if pre-defined
functions were used in defining the entity. Alternatively, new
measures can be created using narrow system data for one or more
subjects and/or the Personalized Modeling System (100) can identify
the best fit measures for the specified functions. The quantitative
measures can take any form. For example, Table 10 shows three
measures for a medical organization entity--patient element health,
patient element longevity and organization financial break even.
The Personalized Modeling System (100) incorporates the ability to
use other pre-defined measures including each of the different
types of risk--alone or in combination--as well as
sustainability.
[0016] After the data integration, subject definition and measure
specification are completed, processing advances to the second
stage where context layers for each subject are developed and
stored in a contextbase (50). Each context for a subject can be
divided into eight or more types of context layers. Together, these
eight layers identify: actions, constraints, elements, events,
factors, preferences, processes, projects, risks, resources and
terms that impact entity performance for each function; the
magnitude of the impact actions, constraints, elements, events,
factors, preferences, processes, projects, risks, resources ad
terms have on entity performance of each function; physical and/or
virtual coordinate systems that are relevant to entity performance
for each function and the magnitude of the impact location relative
to physical and/or virtual coordinate systems has on entity
performance for each function. These eight layers also identify and
quantify subject function and/or mission measure performance. The
eight types of layers are: [0017] 1. A layer that defines and
describes the element context over time, i.e. we store widgets (a
resource) built (an action) using the new design (an element) with
the automated lathe (another element) in our warehouse (an
element). The lathe (element) was recently refurbished (completed
action) and produces 100 widgets per 8 hour shift (element
characteristic). We can increase production to 120 widgets per 8
hour shift if we add complete numerical control (a feature). This
layer may be subdivided into any number of sub-layers along user
specified dimensions such as tangible elements of value, intangible
elements of value, processes, agents, assets and combinations
thereof; [0018] 2. A layer that defines and describes the resource
context over time, i.e. producing 100 widgets (a resource) requires
8 hours of labor (a resource), 150 amp hours of electricity
(another resource) and 5 tons of hardened steel (another resource).
This layer may be subdivided into any number of sub-layers along
user specified dimensions such as lexicon (what resources are
called), resources already delivered, resources with delivery
commitments and forecast resource requirements; [0019] 3. A layer
that defines and describes the environment context over time (the
entities in the social (1000), natural (2000) and/or physical
environment (3000) that impact entity function and/or mission
measure performance, i.e. the volatility in the market for steel
increased 50% last year, standard deviation on monthly shipments is
24% and analysts expect 30% growth in revenue this quarter. This
layer may be subdivided into any number of sub-layers along user
specified dimensions; [0020] 4. A layer that defines and describes
the transaction context (also known as tactical/administrative
context) over time, i.e. Acme owes us $30,000 for prior sales, we
have made a commitment to ship 100 widgets to Acme by Tuesday and
need to start production by Friday. This layer may be subdivided
into any number of sub-layers along user specified dimensions such
as historical transactions, committed transactions, forecast
transactions, historical events, forecast events and combinations
thereof; [0021] 5. A layer that defines and describes the
relationship context over time, i.e. Acme is also a key supplier
for the new product line, Widget X, that is expected to double our
revenue over the next five years. This layer may be subdivided into
any number of sub-layers along user specified dimensions; [0022] 6.
A layer that defines and describes the measurement context over
time, i.e. the price per widget is $100 and the cost of
manufacturing widgets is $80 so we make $20 profit per unit (for
most businesses this would be a short term profit measure for the
value creation function). Also, Acme is one of our most valuable
customers and they are a valuable supplier to the international
division (value based measures). This layer may be subdivided into
any number of sub-layers along user specified dimensions. For
example, the instant, five year and lifetime impact of certain
medical treatments may be of interest. In this instance, three
separate measurement layers could be created to provide the desired
context. The risks associated with each measure can be integrated
within each measurement layer or they can be stored in separate
layers. For example, value measures for organizations integrate the
risk and the return associated with measure performance. Measures
associated with other entities can be included in this layer. This
capability enables the use of the difference between the subject
measure and the measures of other entities as measures; [0023] 7. A
layer that optionally defines the relationship of one or more of
the first six layers of entity context to one or more reference
systems over time. A spatial reference coordinate system will be
used for most entities. Pre-defined spatial reference coordinates
available for use in the system of the present invention include
the major organs in a human body, each of the continents, the
oceans, the earth and the solar system. Virtual reference
coordinate systems can also be used to relate each entity to other
entities. For example, a virtual coordinate system could be a
network such as the Internet, an intranet, a local are network, a
wi-fi network, a wimax network and/or social network. The genome of
different entities can also be used as a reference coordinate
system. This layer may also be subdivided into any number of
sub-layers along user specified dimensions and would identify
system or application context if appropriate; [0024] 8. A layer
that defines and describes the lexicon of the subject--this layer
may be broken into sub-layers to define the lexicon associated with
each of the previous context layers. Different combinations of
context layers from different subjects and/or entities are relevant
to different analyses and decisions. For simplicity, we will
generally refer to eight types of context layers or eight context
layers while recognizing that the number of context layers can be
greater or less than eight. It is worth noting at this point that
the layers may be combined for ease of use, to facilitate
processing and/or as entity requirements dictate. Before moving on
to discuss context frames--which are defined by one or more entity
function and/or mission measures and the portion of each of the
eight context layers that impacts the one or more entity function
and/or mission measures--we need to define each context layer in
more detail. Before we can do this, we need to define key terms
that we will use in more fully defining the Personalized Modeling
System (100) of the present invention: [0025] 1. Entity type--any
member or combination of members of a hierarchy or group (see
Tables 1, 2 and 3 for examples of hierarchies and groups); [0026]
2. Entity--a discrete unit of an entity type that has one or more
functions, these functions can support the completion of a mission;
[0027] 3. Context--defines and describes the situation of an entity
vis a vis the drivers of subject function performance as shown in
FIG. 2A, FIG. 2B or FIG. 2C. It includes but is not limited to the
data, information and knowledge that defines and describes the
eight context layers identified previously for a valid context
space; [0028] 4. User context--defines and describes the users
situation vis a vis drivers of user function performance--note:
user may or may not be the subject; [0029] 5. Subject--patient
(900), combination of patients (925) or a patient--entity system
(950) as shown in FIG. 2A, FIG. 2B or FIG. 2C respectively with one
or more defined functions; [0030] 6. Function--behavior or
performance of the subject, can include creation, production,
growth, improvement, destruction, diminution and/or maintenance of
a component of context and/or one or more entities. Examples:
maintaining body temperature at 98.6 degrees Fahrenheit, destroying
cancer cells, improving muscle tone and producing insulin; [0031]
7. Mission--what an entity intends to do or achieve (i.e. a goal),
functions can support the completion of an entity mission; [0032]
8. Characteristic--numerical or qualitative indication of entity
status--examples: temperature, color, shape, distance weight, and
cholesterol level (descriptive data are the typical source of data
about characteristics) and the acceptable range for these
characteristics (aka a subset of constraints); [0033] 9.
Event--something that takes place in a defined point in space time,
the events of interest are generally those that are recorded and
have an impact on the components of context and/or measure
performance of a subject and/or change the characteristics of an
entity; [0034] 10. Project--action or series of actions that
produces one or more lasting changes. Change can include: changes a
characteristic, changes a constraint, produces one or more new
components of context, changes one or more components of context,
and produces one or more new entities or some combination thereof.
Said changes impact entity function performance/mission and are
analyzed using same method, system and media described for event
and extreme event analysis; [0035] 11. Action--acquisition,
consumption, destruction, production or transfer of resources,
elements and/or entities in a defined point in space
time--examples: blood cells transfer oxygen to muscle cells and an
assembly line builds a product. Actions are a subset of events and
are generally completed by a process; [0036] 12. Data--anything
that is recorded--includes transaction data, descriptive data,
content, information and knowledge; [0037] 13. Information--data
with context of unknown completeness; [0038] 14. Knowledge--data
with the associated complete context--all eight types of layers are
defined and complete to the extent possible given uncertainty;
[0039] 15. Transaction--anything that is recorded that isn't
descriptive data. Transactions generally reflect events and/or
actions for one or more entities over time (transaction data are
generally the source); [0040] 16. Measure--quantitative indication
of one or more subject functions and/or missions--examples: cash
flow, patient survival rate, bacteria destruction percentage, shear
strength, torque, cholesterol level, and pH maintained in a range
between 6.5 and 7.5; [0041] 17. Element--also known as a context
element these are tangible and intangible entities that participate
in and/or support one or more subject actions and/or functions
without normally being consumed by the action--examples: land,
heart, Sargasso sea, relationships, wing and knowledge; [0042] 18.
Element combination--two or more elements that share performance
drivers to the extent that they need to be analyzed as a single
element; [0043] 19. Item--an item is an instance within an element.
For example, an individual salesman would be an "item" within the
sales department element (or entity). In a similar fashion a gene
would be an item within a dna entity. While there are generally a
plurality of items within an element, it is possible to have only
one item within an element; [0044] 20. Item variables are the
transaction data and descriptive data associated with an item or
related group of items; [0045] 21. Indicators (also known as item
performance indicators and/or factor performance indicators) are
data derived from data related to an item or a factor; [0046] 22.
Composite variables for a context element or element combination
are mathematical combinations of item variables and/or indicators,
logical combinations of item variables and/or indicators and
combinations thereof; [0047] 23. Element variables or element data
are the item variables, indicators and composite variables for a
specific context element or sub-context element; [0048] 24.
Subelement--a subset of all items in an element that share similar
characteristics; [0049] 25. Asset--subset of elements that support
actions and are usually not transferred to other entities and/or
consumed--examples: brands, customer relationships, information and
equipment; [0050] 26. Agent--subset of elements that can
participate in an action. Six distinct kinds of agents are
recognized--initiator, negotiator, closer, catalyst, regulator,
messenger. A single agent may perform several agent
functions--examples: customers, suppliers and salespeople; [0051]
27. Resource--entities that are routinely transferred to other
entities and/or consumed--examples: raw materials, products,
information, employee time and risks; [0052] 28. Subresource--a
subset of all resources that share similar characteristics; [0053]
29. Process--combination of elements actions and/or events that are
used to complete an action or event--examples: sales process,
cholesterol regulation and earthquake. Processes are a special
class of element; [0054] 30. Commitment--an obligation to complete
a transaction in the future--example: contract for future sale of
products and debt; [0055] 31. Competitor--subset of factors, an
entity that seeks to complete the same actions as the subject,
competes for elements, competes for resources or some combination
thereof; [0056] 32. Priority--relative importance assigned to
actions and measures; [0057] 33. Requirement--minimum or maximum
levels for one or more elements, element characteristics, actions,
events, processes or relationships, may be imposed by user (40),
laws (1306) or physical laws (i.e. force=mass times acceleration);
[0058] 34. Surprise--variability or events that improve or increase
subject performance; [0059] 35. Risk--variability or events that
reduce or degrade subject performance; [0060] 36. Extreme
risk--caused by variability or extreme events that reduce subject
performance by producing permanent changes in the impact of one or
more components of context on the subject; [0061] 37. Critical
risk--extreme risks that can terminate a subject; [0062] 38.
Competitor risk--risks that are a result of actions by an entity
that competes for resources, elements, actions or some combination
thereof; [0063] 39. Factor--entities external to subject that have
an impact on subject performance--examples: commodity markets,
weather, earnings expectation--as shown in FIG. 2A factors are
associated with subjects that are outside the box. All higher
levels in the hierarchy of a subject are also defined as factors.
[0064] 40. Composite factors are numerical indicators of: external
entities that influence performance, conditions external to the
subject that influence performance, conditions of the entity
compared to external expectations of entity conditions or the
performance of the entity compared to external expectations of
entity performance; [0065] 41. Factor variables are the transaction
data and descriptive data associated with context factors; [0066]
42. Factor performance indicators (also known as indicators) are
data derived from factor related data;
[0067] 43. Composite factors (also known as composite variables)
for a context factor or factor combination are mathematical
combinations of factor variables and/or factor performance
indicators, logical combinations of factor variables and/or factor
performance indicators and combinations thereof; [0068] 44.
External Services (9) are services available from systems that are
not part of the system of the present invention (100) via a network
(wired or wireless) connection. They include search services
(google, yahoo!, etc.), map services (mapquest, yahoo!, etc.),
rating services (zagat's, fodor's, etc.), weather services and
services particular to a location or site (projection services,
presence detection services, voice transcription services, traffic
status reports, tour guide information, etc.); [0069] 45. A layer
is software and/or information that gives an application, system,
service, device or layer the ability to interact with another
layer, device, system, service, application or set of information
at a general or abstract level rather than at a detailed level;
[0070] 46. Context frames include all information relevant to
function measure performance for a defined combination of context
layers, subject and subject functions. In one embodiment, each
context frame is a series of pointers that are stored within a
separate table; [0071] 47. Complete context is a shorthand way of
noting that all eight types of context layers have been defined for
an subject function (note: it is also used as a proprietary
trade-name designation for applications or services with a context
quotient of 200); [0072] 48. Complete Entity Context--complete
context for all entity functions; [0073] 49. Components of
Context--any combination of location (901), projects (902), events
(903), virtual location (904), factors (905), resources (906,
elements (907), actions (908), transactions (909), function
measures (910), processes (911), mission measures (912),
constraints (913), preferences (914) and factors (1000, 2000 and
3000) that have a relationship to and/or impact on a subject;
[0074] 50. Contextbase is a database that organizes data and
information by context for one or more subject entities. In one
embodiment the contextbase is a virtual database. The contextbase
can also be a relational database, a flat database, a storage area
network and/or some combination thereof; [0075] 51. Total risk is
the sum of all variability risks and event risks for a subject.
[0076] 52. Variability risk is a subset of total risk. It is the
risk of reduced or impaired performance caused by variability in
one or more components of context. Variability risk is quantified
using statistical measures like standard deviation. The covariance
and dependencies between different variability risks are also
determined because simulations use quantified information regarding
the inter-relationship between the different risks to perform
effectively; [0077] 53. Event risk is a subset of total risk. It is
the risk of reduced or impaired performance caused by the
occurrence of an event. Event risk is quantified by combining a
forecast of event frequency with a forecast of event impact on
subject components of context and the entity itself. [0078] 54.
Contingent liabilities are a subset of event risk where the impact
of an event occurrence is known; [0079] 55. Uncertainty measures
the amount of subject function measure performance that cannot be
explained by the components of context and their associated risk
that have been identified by the system of the present invention.
Sources of uncertainty include model error and data error. [0080]
56. Real options are defined as options the entity may have to make
a change in its behavior/performance at some future date--these can
include the introduction of new elements or resources, the ability
to move processes to new locations, etc. Real options are generally
supported by the elements of an entity; [0081] 57. The efficient
frontier is the curve defined by the maximum function and/or
mission measure performance an entity can expect for a given level
of total risk; and [0082] 58. Services are self-contained,
self-describing, modular pieces of software that can be published,
located, queried and/or invoked across a World Wide Web, network
and/or a grid. In one embodiment all services are SOAP compliant.
Bots and agents can be functional equivalents to services. In one
embodiment all applications are services, However, the system of
the present invention can function using: bots (or agents), client
server architecture, and integrated software application
architecture and/or combinations thereof. We will use the terms
defined above and the keywords that were defined previously when
detailing one embodiment of the present invention. In some cases
key terms may be defined by the Upper Ontology or an industry
organization such as the Plant Ontology Consortium, the Gene
Ontology Consortium or the ACORD consortium (for insurance). In a
similar fashion the Global Spatial Data Infrastructure organization
and the Federal Geographic Data Committee are defining a reference
model for geographic information that can be used to define the
spatial reference standard for geographic information. The United
Nations is similarly defining the United Nations Standard Product
and Services Classification which can also be used for reference.
The element definitions, descriptive data, lexicon and reference
frameworks from these sources can supplement or displace the
pre-defined metadata included within the contextbase (50) as
appropriate. Because the system of the present invention identifies
and quantifies the impact of different actions, constraints,
elements, events, factors, preferences, processes, projects, risks
and resources as part of its normal processing, the relationships
defined by standardized ontologies are generally not utilized.
However, they can be used as a starting point for system processing
and/or to supplement the results of processing.
[0083] In any event, we can now use the key terms to better define
the eight types of context layers and identify the typical source
for the data and information as shown below. [0084] 1. The element
context layer identifies and describes the entities that impact
subject function and/or mission measure performance by time period.
The element description includes the identification of any
sub-elements and preferences. Preferences may be important
characteristics for process elements that have more than one option
for completion. Elements are initially identified by the chosen
subject hierarchy (elements associated with lower levels of a
hierarchy are automatically included) whereas transaction data
identifies others as do analysis and user input. These elements may
be identified by item or sub-element. The sources of data can
include devices (3), narrow system databases (5), partner narrow
system databases (6), external databases (7), the World Wide Web
(8), external services (9), xml compliant applications, the
Complete Context.TM. Input Service (601) and combinations thereof.
[0085] 2. The resource context layer identifies and describes the
resources that impact subject function and/or mission measure
performance by time period. The resource description includes the
identification of any sub-resources. The sources of data can
include narrow system databases (5), partner narrow system
databases (6), external databases (7), the World Wide Web (8),
external services (9), xml compliant applications, the Complete
Context.TM. Input Service (601) and combinations thereof. [0086] 3.
The environment context layer identifies and describes the factors
in the social, natural and/or physical environment that impact
subject function and/or mission measure performance by time period.
The relevant factors are determined via analysis. The factor
description includes the identification of any sub-factors. The
sources of data can include devices (3), narrow system databases
(5), partner narrow system databases (6), external databases (7),
the World Wide Web (8) and external services (9), xml compliant
applications, the Complete Context.TM. Input Service (601) and
combinations thereof. [0087] 4. The transaction context layers
identifies and describes the events, actions, action priorities,
commitments and requirements of the subject and each entity in the
element context layer by time period. The description identifies
the elements and/or resources that are associated with the event,
action, action priority, commitment and/or requirement. The sources
of data can include narrow system databases (5), partner narrow
system databases (6), external databases (7), the World Wide Web
(8), external services (9), xml compliant applications, the
Complete Context.TM. Input Service (601) and combinations thereof.
[0088] 5. The relationship context layer defines the relationships
between the first three layers (elements, resources and/or factors)
and the fourth layer (events and/or actions) by time period. These
impacts can be identified by user input (i.e. process maps and
procedures), analysis, narrow system databases (5), partner narrow
system databases (6), external databases (7), the World Wide Web
(8), external services (9), xml compliant applications, the
Complete Context.TM. Input Service (601) and combinations thereof.
[0089] 6. The measure context layer(s) identifies and quantifies
the impact of actions, events, elements, factors, resources and
processes (combination of elements) on each entity function measure
by time period. The impact of risks and surprises can be kept
separate or integrated with other element/factor measures. The
impacts are generally determined via analysis. However, the
analysis can be supplemented by input from simulation programs, the
user (40), a subject matter expert (42) and/or a collaborator (43),
narrow system databases (5), partner narrow system databases (6),
external databases (7), the World Wide Web (8), external services
(9), xml compliant applications, the Complete Context.TM. Input
Service (601) and combinations thereof. [0090] 7. Reference context
layer (optional)--the relationship of the first six layers to a
specified real or virtual coordinate system. These relationships
can be identified by user input (i.e. maps), input from a subject
matter expert (42) and/or a collaborator (43), narrow system
databases (5), partner narrow system databases (6), external
databases (7), the World Wide Web (8), external services (9), xml
compliant applications, the Complete Context.TM. Input Service
(601), analysis and combinations thereof; and [0091] 8. Lexical
context layer--defines the terminology used to define and describe
the components of context in the other seven layers. These lexicon
can be identified by user input, input from a subject matter expert
(42) and/or a collaborator (43), narrow system databases (5),
partner narrow system databases (6), external databases (7), the
World Wide Web (8), external services (9), xml compliant
applications, the Complete Context.TM. Input Service (601),
analysis and combinations thereof. The eight context layers define
a complete context for entity performance for a specified function
by time period. We can use the more precise definition of context
to define what it means to be knowledgeable. Our revised definition
would state that an individual that is knowledgeable about a
subject has information from all eight context layers for the one
or more functions he, she or it is considering. This is important
because, once the complete context is known and modeled any disease
can be managed and/or cured. The knowledgeable individual would be
able to use the information from the eight context layers to:
[0092] 1. identify the range of contexts where models of subject
function performance are applicable; and [0093] 2. accurately
predict subject actions in response to events and/or actions in
contexts where the context is applicable. The accuracy of the
prediction created using the eight types of context layers reflects
the level of knowledge. For simplicity we will use the R squared
(R.sup.2) statistic as the measure of knowledge level. R.sup.2 is
the fraction of the total squared error that is explained by the
model--other statistics can be used to provide indications of the
entity model accuracy including entropy measures and root mean
squared error. The gap between the fraction of performance
explained by the model and 100% is uncertainty, errors in the model
and errors in the data. Table 10 illustrates the use of the
information from six of the eight layers in analyzing a sample
personalized medicine context.
TABLE-US-00010 [0093] TABLE 10 1. Mission: patient health &
longevity, financial break even measures 2. Environment:
malpractice insurance is increasingly costly 3. Measure: survival
rate is 99% for procedure A and 98% for procedure B; treatment in
first week improves 5 year survival 18%, 5 year recurrence rate is
7% higher for procedure A 4. Relationship: Dr. X has a commitment
to assist on another procedure Monday 5. Resource: operating room A
time available for both procedures 6. Transaction: patient should
be treated next week, his insurance will cover operation 7.
Element: operating room, operating room equipment, Dr. X
In addition to defining context, context layers are useful in
developing management tools. One use of the layers is establishing
budgets and/or alert levels for data within a layer or combinations
of layers. Using the sample situation illustrated in Table 10, an
alert could be established for survival rates that drop below 99%
in the measure layer. Control can be defined and applied at the
transaction and measure levels by assigning priorities to actions
and measures. Using this approach the system of the present
invention has the ability to analyze and optimize performance using
user specified priorities, historical measures or some combination
of the two.
[0094] Some analytical applications are limited to optimizing the
instant (short-term) impact given the elements, resources and the
transaction status. Because these systems generally ignore
uncertainty and the impact, reference, environment and long term
measure portions of a complete context, the recommendations they
make are often at odds with common sense decisions made by line
managers that have a more complete context for evaluating the same
data. This deficiency is one reason some have noted that "there is
no intelligence in business intelligence applications". One reason
some existing systems take this approach is that the information
that defines three important parts of complete context
(relationship, environment and long term measure impact) are not
readily available and must generally be derived. A related
shortcoming of some of these systems is that they fail to identify
the context or contexts where the results of their analyses are
valid.
[0095] In one embodiment, the Personalized Modeling System (100)
provides the functionality for integrating data from all narrow
systems (4), creating a contextbase (50), developing a Personalized
Modeling System (100) and supporting the Complete Context.TM. Suite
(625) as shown in FIG. 13. Over time, the narrow systems (4) can be
eliminated and all data can be entered directly into the
Personalized Modeling System (100) as discussed previously. In an
alternate mode, the Personalized Modeling System (100) would work
in tandem with a Process Integration System (99) such as an
application server, laboratory information management system,
middleware application, extended operating system, data exchange or
grid to integrate data, create the contextbase (50), develop a
Personalized Modeling System (100) and support the Complete
Context.TM. Suite (625) as shown in FIG. 14. In either mode, the
system of the present invention supports the development and
storage of all eight types of context layers in order to create a
contextbase (50).
[0096] The contextbase (50) also enables the development of new
types of analytical reports including a sustainability report and a
controllable performance report. The sustainability report combines
the element lives, factor lives, risks and an entity context to
provide an estimate of the time period over which the current
subject performance level can be sustained. There are three paired
options for preparing the report--dynamic or static mode, local or
indirect mode, risk adjusted or pre-risk mode. In the static mode,
the current element and factor mix is "locked-in" and the
sustainability report shows the time period over which the current
inventory will be depleted. In the dynamic mode the current element
and factor inventory is updated using trended replenishment rates
to provide a dynamic estimate of sustainability. The local
perspective reflects the sustainability of the subject in isolation
while the indirect perspective reflects the impact of the subject
on another entity. The indirect perspective is derived by mapping
the local impacts to some other entity. The risk adjusted (aka
"risk") and pre-risk modes (aka "no risk") are self explanatory as
they simply reflect the impact of risks on the expected
sustainability of subject performance. The different possible
combinations of these three options define eight modes for report
preparation as shown in Table 11.
TABLE-US-00011 TABLE 11 Mode Static or Dynamic Local or Indirect
Risk or No Risk 1 Static Local Risk 2 Static Local No Risk 3 Static
Indirect Risk 4 Static Indirect No Risk 5 Dynamic Local Risk 6
Dynamic Local No Risk 7 Dynamic Indirect Risk 8 Dynamic Indirect No
Risk
The sustainability report reflects the expected impact of all
context elements and factors on subject performance over time. It
can be combined with the Complete Context.TM. Forecast Service
(603), described below, to produce unbiased reserve estimates.
Context elements and context factors are influenced to varying
degrees by the subject. The controllable performance report
identifies the relative contribution of the different context
elements and factors to the current level of entity performance. It
then puts the current level of performance in context by comparing
the current level of performance with the performance that would be
expected if some or all of the elements and factors were all at the
mid-point of their normal range--the choice of which elements and
factors to modify could be a function of the control exercised by
the subject. Both of these reports are pre-defined for display
using the Complete Context.TM. Review Service (607) described
below.
[0097] The Complete Context.TM. Review Service (607) and the other
services in the Complete Context.TM. Suite (625) use context frames
and sub-context frames to support the analysis, forecast, review
and/or optimization of entity performance. Context frames and
sub-context frames are created from the information provided by the
Personalized Modeling System (100) created by the system of the
present invention (100). The ID to frame table (165) identifies the
context frame(s) and/or sub-context frame(s) that will be used by
each user (40), manager (41), subject matter expert (42), and/or
collaborator (43). This information is used to determine which
portion of the Personalized Modeling System (100) will be made
available to the devices (3) and narrow systems (4) that support
the user (40), manager (41), subject matter expert (42), and/or
collaborator (43) via the Complete Context.TM. API (application
program interface). As detailed later, the system of the present
invention can also use other methods to provide the required
context information.
[0098] Context frames are defined by the entity function and/or
mission measures and the context layers associated with the entity
function and/or mission measures. The context frame provides the
data, information and knowledge that quantify the impact of
actions, constraints, elements, events, factors, preferences,
processes, projects, risks and resources on entity performance.
Sub-context frames contain information relevant to a subset of one
or more function measure/layer combinations. For example, a
sub-context frame could include the portion of each of the context
layers that was related to an entity process. Because a process can
be defined by a combination of elements, events and resources that
produce an action, the information from each layer that was
associated with the elements, events, resources and actions that
define the process would be included in the sub-context frame for
that process. This sub-context frame would provide all the
information needed to understand process performance and the impact
of events, actions, element change and factor change on process
performance.
[0099] The services in the Complete Context.TM. Suite (625) are
"context aware" (with context quotients equal to 200) and have the
ability to process data from the Personalized Modeling System (100)
and its contextbase (50). Another novel feature of the services in
the Complete Context.TM. Suite (625) is that they can review entity
context from prior time periods to generate reports that highlight
changes over time and display the range of contexts under which the
results they produce are valid. The range of contexts where results
are valid will be hereinafter be referred to as the valid context
space.
[0100] The services in the Complete Context.TM. Suite (625) also
support the development of customized applications or services.
They do this by: [0101] 1. providing ready access to the internal
logic of the service while at the same time protecting this logic
from change; and [0102] 2. using the universal context
specification (see FIG. 17) to define standardized Application
Program Interfaces (API's) for all Complete Context.TM.
Services--these API's allow the specification of the different
context layers using text information, numerical information and/or
graphical representations of subject context in a format similar to
that shown in FIG. 2A, FIG. 2B. and FIG. 2C.
[0103] The first features allow users (40), partners and external
services to get information tailored to a specific context while
preserving the ability to upgrade the services at a later date in
an automated fashion. The second feature allows others to
incorporate the Complete Context.TM. Services into other
applications and/or services. It is worth noting that this
awareness of context is also used to support a true natural
language interface (714)--one that understands the meaning of the
identified words--to each of the services in the Suite (625). It
should be also noted that each of the services in the Suite (625)
supports the use of a reference coordinate system for displaying
the results of their processing when one is specified for use by
the user (40). The software for each service in the suite (625)
resides in an applet or service with the context frame being
provided by the Personalized Modeling System (100). This software
could also reside on the computer (110) with user access through a
browser (800) or through the natural language interface (714)
provided by the Personalized Modeling System (100). Other features
of the services in the Complete Context.TM. Suite (625) are briefly
described below: [0104] 1. Complete Context.TM. Analysis Service
(602)--analyzes the impact of user (40) specified changes on a
subject for a given context frame or sub-context frame by mapping
the proposed change to the appropriate context layer(s) in
accordance with the schema or ontology and then evaluating the
impact of said change on the function and/or mission measures.
Context frame information may be supplemented by simulations and
information from subject matter experts (42) as appropriate. This
service can also be used to analyze the impact on changes on any
"view" of the entity that has been defined and pre-programmed for
review. For example, accounting profit using three different
standards or capital adequacy can be analyzed using the same rules
defined for the Complete Context.TM. Review Service (607) to
convert the context frame analysis to the required reporting
format. [0105] 2. Complete Context.TM. Auditing Service (624)--is a
modified Complete Context.TM. Review Service (607) that uses a
rules engine to completely re-process all relevant transactions and
compare the resulting values with the information in a report
presented by management. The Complete Context.TM. Auditing Service
then combines this information with the information stored in the
Context Base (50) to complete an automated audit of all the numbers
in a report--including reserve estimates--as well as producing a
list of risk factors in order of importance. After the various
calculations are completed, the system of the present invention
produces a discrepancy report where the reported values in a report
is compared to the value computed using the method and system
detailed above. [0106] 3. Complete Context.TM. Bridge Service
(624)--is a service that identifies the differences between two
context frames and the best mode for bringing the frames into
alignment or congruence. This service can be very useful in
breaking down barriers to communication and facilitating
negotiations. [0107] 4. Complete Context.TM. Browser
(628)--supports browsing through the contextbase (50) with a focus
on one or more dimensions of the Universal Context Specification
for the user (40) and/or a subject. [0108] 5. Complete Context.TM.
Capture and Collaboration Service (622)--guides one or more subject
matter experts (42) and/or collaborators (43) through a series of
steps in order to capture information, refine existing knowledge
and/or develop plans for the future using existing knowledge. The
one or more subject matter experts (42) and/or collaborators (43)
will provide information and knowledge by selecting from a template
of pre-defined elements, resources, events, factors, actions and
entity hierarchy graphics that are developed from the subject
schema table (157). The one or more subject matter experts (42)
and/or collaborators (43) also have the option of defining new
elements, events, factors, actions and hierarchies. The one or more
subject matter experts (42) and/or collaborators (43) are first
asked to define what type of information and knowledge will be
provided. The choices will include each of the eight types of
context layers as well as element definitions, factor definitions,
event definitions, action definition, impacts, processes,
uncertainty and scenarios. On this same screen, the one or more
subject matter experts (42) and/or collaborators (43) will also be
asked to decide whether basic structures or probabilistic
structures will provided in this session, if this session will
require the use of a time-line and if the session will include the
lower level subject matter. The selection regarding type of
structures will determine what type of samples will be displayed on
the next screen. If the use of a time-line is indicated, then the
user will be prompted to: select a reference point--examples would
include today, event occurrence, when I started, etc.; define the
scale being used to separate different times--examples would
include seconds, minutes, days, years, light years, etc.; and
specify the number of time slices being specified in this session.
The selection regarding which type of information and knowledge
will be provided determines the display for the last selection made
on this screen. There is a natural hierarchy to the different types
of information and knowledge that can be provided by a one or more
subject matter experts (42) and/or collaborators (43). For example,
measure level knowledge would be expected to include input from the
impact, element, transaction and resource context layers. If the
one or more subject matter experts (42) and/or collaborators (43)
agrees, the service will guide the one or more subject matter
experts (42) and/or collaborators (43) to provide knowledge for
each of the "lower level" knowledge areas by following the natural
hierarchies. Summarizing the preceding discussion, the one or more
subject matter experts (42) and/or collaborators (43) has used the
first screen to select the type of information and knowledge to be
provided (measure layer, impact layer, transaction layer, resource
layer, environment layer, element layer, reference layer, event
risk or scenario). The one or more subject matter experts (42)
and/or collaborators (43) have also chosen to provide this
information in one of four formats: basic structure without
timeline, basic structure with timeline, relational structure
without timeline or relational structure with timeline. Finally,
the one or more subject matter experts (42) and/or collaborators
(43) have indicated whether or not the session will include an
extension to capture "lower level" knowledge. Each selection made
by the one or more subject matter experts (42) and/or collaborators
(43) will be used to identify the combination of elements, events,
actions, factors and entity hierarchy chosen for display and
possible selection. This information will be displayed in a manner
that is somewhat similar to the manner in which stencils are made
available to Visio.RTM. users for use in the workspace. The next
screen displayed by the service will depend on which combination of
information, knowledge, structure and timeline selections that were
made by the one or more subject matter experts (42) and/or
collaborators (43). In addition to displaying the sample graphics
to the one or more subject matter experts (42) and/or collaborators
(43), this screen will also provide the one or more subject matter
experts (42) and/or collaborators (43) with the option to use
graphical operations to change impacts, define new impacts, define
new elements, define new factors and/or define new events. The
thesaurus table (164) in the contextbase (50) provides graphical
operators for: adding an element or factor, acquiring an element,
consuming an element, changing an element, factor or event risk
values, adding a impact, changing the strength of a impact,
identifying an event cycle, identifying a random impact,
identifying commitments, identifying constraints and indicating
preferences. The one or more subject matter experts (42) and/or
collaborators (43) would be expected to select the structure that
most closely resembles the knowledge that is being communicated or
refined and add it to the workspace being displayed. After adding
it to the workspace, the one or more subject matter experts (42)
and/or collaborators (43) will then edit elements, factors,
resources and events and add elements, factors, resources events
and descriptive information in order to fully describe the
information or knowledge being captured from the context frame
represented on the screen. If relational information is being
specified, then the one or more subject matter experts (42) and/or
collaborators (43) will be given the option of using graphs,
numbers or letter grades to communicate the information regarding
probabilities. If a timeline is being used, then the next screen
displayed will be the screen for the same perspective from the next
time period in the time line. The starting point for the next
period knowledge capture will be the final version of the knowledge
captured in the prior time period. After completing the knowledge
capture for each time period for a given level, the Service (622)
will guide the one or more subject matter experts (42) and/or
collaborators (43) to the "lower level" areas where the process
will be repeated using samples that are appropriate to the context
layer or area being reviewed. At all steps in the process, the
information in the contextbase (50) and the knowledge collected
during the session will be used to predict elements, resources,
actions, events and impacts that are likely to be added or modified
in the workspace. These "predictions" are displayed using flashing
symbols in the workspace. The one or more subject matter experts
(42) and/or collaborators (43) are given with the option of turning
the predictive prompting feature off. After the information and
knowledge has been captured, the graphical results are converted to
data base entries and stored in the appropriate tables (141, 142,
143, 144, 145, 149, 154, 156, 157, 158, 162 or 168) in the
contextbase (50). Data from simulation programs can also be added
to the contextbase (50) to provide similar information or
knowledge. This Service (622) can also be used to verify the
veracity of some new assertion by mapping the new assertion to the
subject model and quantifying any reduction in explanatory power
and/or increase in uncertainty of the entity performance model.
[0109] 6. Complete Context.TM. Customization Service (621)--service
for analyzing and optimizing the impact of data, information,
products, projects and/or services by customizing the features
included in or expressed by an offering for a subject for a given
context frame or sub-context frame. The context frame or
sub-context frame may be provided by the Complete Context.TM.
Summary Service (617). Some of the products and services that can
be customized with this service include medicine, medical
treatments, medical tests, software, technical support, equipment,
computer hardware, devices, services, telecommunication equipment,
living space, buildings, advertising, data, information and
knowledge. Other customizations may rely on the Complete
Context.TM. Optimization Service (604) working alone or in
combination with the Complete Context.TM. Search Service (609).
Context frame information may be supplemented by simulations and
information from subject matter experts (42) as appropriate. [0110]
7. Complete Context.TM. Display Service (614)--manages the
availability and display of data, information, and knowledge
related to one or more context frames and/or sub context frames to
a user (40), manager (41), subject matter expert (42), and/or
collaborator (43) on a continuous basis using a portal (11),
service (9), device (3), computer (110) and/or other display. To
support this effort the Complete Context.TM. Display Service (614)
supports RSS feeds, manages one or more caches (119) that support
projections and display(s) utilizing the caches and/or data feeds.
The priority assigned to the data and information made available is
determined via a randomized algorithm that blends frequency of use,
recency of use, cost to retrieve and time to retrieve measures with
a relevance measure for each of the one or more context frames
and/or sub context frames being supported (see Complete Context.TM.
Scout Service (616) for a discussion of relevance measure
computation). As the user (40), manager (41), subject matter expert
(42), and/or collaborator (43) context changes (for example when
location changes or the World Trade Center collapses), the
relevance measure will change which will in turn drive this Service
(614) to change the mix in the cache, RSS feed or projection in
order to ensure that data and/or information that is most relevant
to the new context is readily available. This Service (614) can be
combined with the Complete Context.TM. Optimization Service (604)
to ensure that messages, emails, network traffic, computer
resources and related devices are providing the optimal support for
a given context. In a similar fashion it can be combined with the
Complete Context.TM. Capture and Collaboration Service (622) to
ensure that the one or more subject matter experts (42) and/or
collaborators (43) have the data, information and knowledge they
need to complete their input to the system of the present
invention. The service can be used to purge data, information and
knowledge that is no longer relevant to the given context. In an
interactive commerce setting this application can be used to:
identify the content that is most relevant to a customer's context
and/or display an ad or technical support information relevant to
said context. In this same setting it can be combined with other
services in the suite (625) complete a sale using the Complete
Context.TM. Exchange Service (608), purchase content that has a
value in excess of its cost in the current context using the
Complete Context.TM. Exchange Service (608), customize and buy an
offering using the Complete Context.TM. Customization Service (621)
in conjunction with the Complete Context.TM. Exchange Service
(608), and/or customize and sell an offering using the Complete
Context.TM. Customization Service (621) in conjunction with the
Complete Context.TM. Exchange Service (608). [0111] 8. Complete
Context.TM. Exchange Service (608)--identifies desirable exchanges
of resources, elements, commitments, data and information with
other entities in an automated fashion. This service calls on
Complete Context.TM. Analysis Service (602) in order to review
proposed prices. In a similar manner the service calls on the
Complete Context.TM.
Optimization Service (604) to determine the optimal parameters for
an exchange before completing a transaction. For partners or
customers that provide access to their data that is sufficient to
define a shared context, the exchange service can use the other
services from the Complete Context.TM. Suite (625) to analyze and
optimize the exchange for the combined parties. The actual
transactions are completed by the Complete Context.TM. Input
Service (601). [0112] 9. Complete Context.TM. Forecast Service
(603)--forecasts the value of specified variable(s) using data from
all relevant context layers. Completes a tournament of forecasts
for specified variables and defaults to a multivalent combination
of forecasts from the tournament using methods similar to those
first described in cross referenced U.S. Pat. No. 5,615,109. In
addition to providing the forecast, this service will provide the
confidence interval associated with the forecast and provide the
user (40) with the ability to identify the data that needs to be
collected in order improve the confidence associated with a given
forecast which will make the process of refining forecasts more
efficient. [0113] 10. Complete Context.TM. Indexing Service
(619)--service for developing composite and covering indices for
data, information and knowledge in contextbase (50) using the
impact cutoff and node depth specified by the user (40) in the
system settings table (162) for contexts and combination of
contexts. [0114] 11. Complete Context.TM. Input Service
(601)--service for recording actions and commitments into the
contextbase (50). The interface for this service is a template
accessed via a browser (800) or the natural language interface
(714) provided by the Personalized Modeling System (100) that
identifies the available element, transaction, resource and measure
data for inclusion in a transaction. After the user has recorded a
transaction the service saves the information regarding each action
or commitment to the contextbase (50). Other services such as
Complete Context.TM. Analysis (602), Planning (605) or Optimization
(604) Services can interface with this service to generate actions,
commitments and/or transactions in an automated fashion. Complete
Context.TM. Bots (650) can also be programmed to provide this
functionality. [0115] 12. Complete Context.TM. Journal Service
(630) (aka the "daily me")--uses natural language generation to
automatically develop and deliver a prioritized summary of news and
information in any combination of formats covering a specified time
period (hourly, daily, weekly, etc.) that is relevant to a given
subject context or context frame. Relevance is determined in a
manner identical to that described previously for the Complete
Context.TM. Scout Service (616) save for the fact that the user
(40) is free to modify the node depth, subject entity definition
and/or impact cutoff used for evaluating relevance using a wizard.
[0116] 13. Complete Context.TM. Metrics and Rules Service
(611)--tracks and displays the causal performance indicators for
context elements, resources and factors for a given context frame
as well as the rules used for segmenting context components into
smaller groups for more detailed analysis. Rules and patterns can
be discovered using an algorithm tournament that includes the
Apriori algorithm, the sliding window algorithm; differential
association rule mining, beam-search, frequent pattern growth and
decision trees. [0117] 14. Complete Context.TM. Optimization
Service (604)--simulates entity performance and identifies the
optimal mix of actions, events and/or context components for
operating a specific context frame or sub context frame given the
constraints, uncertainty and the defined function and/or mission
measures. A tournament is used to select the best algorithm from
the group consisting of genetic algorithms, the calculus of
variations, constraint programming, game theory, mixed integer
linear programming, multi-criteria maximization, linear
programming, semi-definite programming, smoothing and highly
optimized tolerance. Because most entities have more than one
function (and more than one measure), the genetic algorithm and
multi-criteria maximizations are used most frequently. This service
can also be used to optimize Complete Context.TM. Review Service
(607) measures using the same rules defined for the Complete
Context.TM. Review Service (607) to define context frames in the
required format before optimization. [0118] 15. Complete
Context.TM. Planning Service (605)--service that is used to:
establish measure priorities, establish action priorities, and
establish expected performance levels (aka budgets) for actions,
events, elements resources and measures. These priorities and
performance level expectations are saved in the corresponding layer
in the contextbase (50). For example, measure priorities are saved
in the measure layer table (145). This service also supports
collaborative planning when context frames that include one or more
partners are created (see FIG. 2B). [0119] 16. Complete Context.TM.
Profiling Service (615)--service for developing the best estimate
of complete entity context from available subject related data and
information. If a complete context has been developed for a similar
entity, then the Complete Context.TM. Profiling Service (615) will
identify: the portion of behavior that is generally explained by
the level of detail in the profile, differences from the similar
entity, expected ranges of behavior and sources of data that are
generally used to produce a more complete context before completing
an analysis of the available data. The contexts developed by this
service (615) can be used to. [0120] 17. Complete Context.TM.
Project Service (606)--service for analyzing and optimizing the
impact of a project or a group of projects on a context frame.
Project is broadly defined to include any development or diminution
of any components of context and/or entities. Context frame
information may be supplemented by simulations and information from
subject matter experts (42) as appropriate. [0121] 18. Complete
Context.TM. Review Service (607)--service for reviewing components
of context and measures alone or in combination. These reviews can
be completed with or without the use of a reference layer. This
service uses a rules engine to transform contextbase (50)
historical information into standardized reports that have been
defined by different entities. Other standardized, non-financial
performance reports have been developed for medical entities,
military operations and educational institutions. The
sustainability and controllable performance reports described
previously are also pre-defined for calculation and display. The
rules engine produces each of these reports on demand for review
and optional publication. [0122] 19. Complete Context.TM. Scout
Service (616)--service that works with the Complete Context.TM.
Indexing Service (619) to proactively identify data, information
and/or knowledge regarding choices the subject will be making in
the near future using the time frame or time frames defined by user
(40) in system settings table (162). The Complete Context.TM. Scout
(616) uses process maps, preferences and the Complete Context.TM.
Forecast Service (603) to identify the choices that it expects the
subject to make in the near future. It then uses weight of
evidence/satisfaction algorithms including banburismus to determine
which choices need additional data, information and/or knowledge to
support an informed decision within parameters selected by the user
(40) in the system settings table (162). It of course, also
determines which choices are already supported by sufficient data,
information and/or knowledge. The relative priority given to the
data, information and/or knowledge selected by the Complete
Context.TM. Scout (616) is a blended function of the relevance
rankings produced by several measures of relevance including
ontology alignment measures, semantic alignment measures, cover
density rankings, vector space model measurements, okapi similarity
measurements, node rankings (as described in U.S. Pat. No.
6,285,999, which is incorporated herein by reference) which can be
obtained from Google, three level relevance scores and hypertext
induced topic selection algorithm scores. The relevance measure
detailed in cross referenced application Ser. No. 10/237,021 can
also be used to identify relevance. The Complete Context.TM. Scout
Service (616) evaluates relevance by utilizing the relationships
and impacts that define a complete entity context to the node depth
and impact cutoff specified by the user in the system settings
table (162) as the basis for scoring using the techniques outlined
above. The node depth identifies the number of node connections
that are used to identify components of context to be considered in
determining the relevance score. For example, if a single entity
(as shown in FIG. 2A) was expected to need information about a
resource (906) and a node depth of one had been selected, then the
relevance rankings would consider the components of context that
are linked to resources by a single link. Using this approach data,
information and/or knowledge that contains and/or is closely linked
to a similar mix of context components will receive a higher
ranking. As shown in FIG. 2A, this would include locations (901),
projects (902), events (903), virtual locations (904), elements
(907), actions (908), transactions (909) and processes (911) that
had an impact greater than or equal to the impact cutoff. The
Complete Context.TM. Scout Service (616) has the ability to use
word sense disambiguation algorithms to clarify the terms being
selected for search, normalizes the terms selected for search using
the Porter Stemming algorithm or an equivalent and uses
collaborative filtering to learn the combination of ranking methods
that are generally preferred for identifying relevant data,
information and/or knowledge given the choices being faced by the
subject for each context and/or context frame. [0123] 20. Complete
Context.TM. Search Service (609)--service for locating the most
relevant data, information, services and/or knowledge for a given
context frame or sub context frame in one of two modes--direct or
indirect. In the direct mode, the relevant data, information and/or
services are identified and presented to the user (40). In the
indirect mode, candidate data, information and/or services are
identified using publicly available search engine results that are
re-analyzed before presentation to the user (40). This service can
be combined with the Complete Context.TM. Customization Service
(621) to identify and provide customized ads and/or other
information related to a given context frame as relevance increases
(through movement relative to a reference frame, external changes,
etc.). Relevance is determined in a manner identical to that
described previously for the Complete Context.TM. Scout (616) save
for the fact that the user (40) is free to modify the node depth,
subject definition and/or impact cutoff used for evaluating
relevance using a wizard. Any indices associated with the revised
subject definitions would automatically be changed by the Complete
Context.TM. Index Service (619) as required to support the changed
definition. The user (40) could choose to change the subject
definition for any number of reasons. For example, he or she may
wish to focus on only one entity context for a vertical search.
Another reason for changing the definition would be to incorporate
one or more contexts from other entities in a new definition. For
example, an employee could choose to search for information
relevant to a combination of one or more of his or her contexts
(for example, his or her employee context) and one or more contexts
of the employer/company (for example, the context of his project or
division). As part of its processing, the Complete Context.TM.
Search Engine (609) identifies the relationship between the
requested information and other information by using the
relationships and measure impacts identified in the contextbase
(50). It uses this information to display the related data and/or
information in a graphical format similar to the formats used in
FIG. 2A, FIG. 2B and/or FIG. 2C. Again, the node depth cutoff is
used to determine how "deep" into the graph the search is
performed. The user (40) has the option of focusing on any block in
a graphical summary of relevant information using the Complete
Context.TM. Browser (628), for example the user (40) could choose
to retrieve information about the resources (906) that support an
entity (900). As discussed previously (see definitions), the
subject may not be the user (40). If this is the case, then the
user's context is considered as part of normal processing.
Information obtained from the natural language interface (714)
could be part of this context; [0124] 21. Complete Context.TM.
Summary Service (617)--develops a summary of entity context using
the Universal Context Specification (see FIG. 17) in an rdf format
that contains the portion of the specification approved for release
by the user (40) for use by other applications, services and/or
entities. For example, the user (40) could send a summary of two
contexts (family member and church-member) to a financial planner
for use in establishing a portfolio that will help the user (40)
realize his or her goals with respect to these two contexts. This
Complete Context.TM. Summary can be used by others providing goods,
services and information (such as other search engines) to tailor
their offerings to the portion of context that has been revealed.
[0125] 22. Complete Context.TM. Underwriting Service
(620)--analyzes a context frame or sub-context frame for an entity
in order to: evaluate entity liquidity, evaluate entity
creditworthiness, evaluate entity risks and/or complete valuations.
It can then use this information to support the: transfer of
liquidity to or from said entity, transfer of risks to or from said
entity, securitization one or more entity risks, underwriting of
entity related securities, packaging of entity related securities
into funds or portfolios with similar characteristics (i.e.
sustainability, risk, uncertainty equivalent, value, etc.) and/or
package entity related securities into funds or portfolios with
dissimilar characteristics (i.e. sustainability, risk, uncertainty
equivalent, value, etc.). As part of securitizing entity risks the
Complete Context.TM. Underwriting Service (620) identifies an
uncertainty equivalent for the risks being underwritten. This
innovative analysis combines quantified uncertainty by type with
the securitized risks to give investors a more complete picture of
the risk they are assuming when they buy a risk security. All of
these analyses can rely on the measure layer information stored in
the contextbase (
50), the sustainability reports, the controllable performance
reports and any pre-defined review format. Context frame
information may be supplemented by simulations and information from
subject matter experts as appropriate. The services within the
Complete Context.TM. Suite (625) can be combined in any combination
and/or joined together in any combination in order to complete a
specific task. For example, the Complete Context.TM. Review Service
(607), the Complete Context.TM. Forecast Service (603) and the
Complete Context.TM. Planning Service (605) can be joined together
to process a series of calculations. The Complete Context.TM.
Analysis Service (602) and the Complete Context.TM. Optimization
Service (604) are also joined together frequently to support
performance improvement activities. In a similar fashion the
Complete Context.TM. Optimization Service (604) and the Complete
Context.TM. Capture and Collaboration Service (622) are often
combined to support knowledge transfer and simulation based
training. The services in the Complete Context.TM. Suite (625) will
hereinafter be referred to as the standard services or the services
in the Suite (625).
[0126] The Personalized Modeling System (100) utilizes a novel
software and system architecture for developing the complete entity
context used to support entity related systems and services. Narrow
systems (4) generally try to develop and use a picture of how part
of an entity is performing (i.e. supply chain, heart functionality,
etc.). The user (40) is then left with an enormous effort to
integrate these different pictures--often developed from different
perspectives--to form a complete picture of entity performance. By
way of contrast, the Personalized Modeling System (100) develops
complete pictures of entity performance for every function using a
common format (i.e. see FIG. 2A, FIG. 2B and FIG. 2C) before
combining these pictures to define the complete entity context and
a contextbase (50) for the subject. The detailed information from
the complete entity context is then divided and recombined in a
context frame or sub-context frame that is used by the standard
services in any variety of combinations for analysis and
performance management.
[0127] The contextbase (50) and entity contexts are continually
updated by the software in the Personalized Modeling System (100).
As a result, changes are automatically discovered and incorporated
into the processing and analysis completed by the Personalized
Modeling System (100). Developing the complete picture first,
instead of trying to put it together from dozens of different
pieces can allow the system of the present invention to reduce IT
infrastructure complexity by orders of magnitude while dramatically
increasing the ability to analyze and manage subject performance.
The ability to use the same software services to analyze, manage,
review and optimize performance of entities at different levels
within a domain hierarchy and entities from a wide variety of
different domains further magnifies the benefits associated with
the simplification enabled by the novel software and system
architecture of the present invention.
[0128] The Personalized Modeling System (100) provides several
other important features, including: [0129] 1. the system learns
from the data which means that it supports the management of new
aspects of entity performance as they become important without
having to develop a new system; [0130] 2. the user is free to
specify any combination of functions and measures for analysis; and
[0131] 3. support for the automated development and use of bots and
other independent software applications (such as services) that can
be used to, among other things, initiate actions, complete actions,
respond to events, seek information from other entities and provide
information to other entities in an automated fashion. To
illustrate the use of the Personalized Modeling System (100), a
description of the use of the services in the Complete Context.TM.
Suite (625) to support a small clinic (an organization entity) in
treating a patient (an organism entity that becomes an element of
the clinic entity) will be provided. The clinic has the same
measures described in table 10 for a medical facility. An overview
of the one embodiment of a system to support this clinic is
provided in FIG. 16. The patient comes to the clinic complaining of
blood in the urine. After arriving at the clinic, he fills out a
form that details his medical history. After the form is filled
out, the patient has his weight and blood pressure checked by an
aide before seeing a doctor. The doctor reviews the patient's
information, examines the patient and prescribes a treatment before
moving on to see the next patient. In the narrative that follows,
the support provided by the Personalized Modeling System (100) for
each step in the patient visit and the subsequent follow up will be
described. The narrative assumes that the system was installed some
time ago and has completed the processing used to develop a
complete ontology and contextbase (50) for the clinic along with
the associated process maps. Process maps define the expected
sequence and timing of events, commitments and actions as treatment
progresses. If the timing or sequence of events fail to follow the
expected path, then the alerts built into the tactical layer will
notify designated staff (element). Process maps also identify the
agents, assets and resources that will be used to support the
treatment process. FIG. 15 shows a sample process map. Process maps
can be established centrally in accordance with guidelines or they
can be established by individual clinicians in accordance with
organization policy. In all cases they are stored in the
relationship layer. Before selecting a process map, the doctor
could activate the Complete Context.TM. Analysis Service (602) to
review the expected instant impacts and outcomes from different
combinations of procedures and treatments that are available under
the current formulary. This information could be used to support
the development of a new process map (if organization policy
permits this). In any event, after the doctor selects a process map
for the treatment of the specified diagnosis, the associated
process commitments and alerts are associated with the patient and
stored in the tactical layer. The required paperwork is
automatically generated by the process map and signed as required
by the doctor.
[0132] If the clinic is small, the history information from the
clinic can be supplemented with data provided by external sources
(such as the AMA, NIH, insurance companies, HMOs, drug companies,
etc.) to provide data for a sufficient population to complete the
processing to establish expected ranges for the expected mix of
patients and diseases.
[0133] Data entry can be completed in a number of ways for each
step in the visit. The most direct route would be to use the
Complete Context.TM. Input Service (601) or any xml compliant
application (such as newer Microsoft Office and Adobe applications)
with a device such as a pc or personal digital assistant to capture
information obtained during the visit using the natural language
interface (714) or a pre-defined form. Once the data are captured
it is integrated with the contextbase (50) in an automated fashion.
A paper form could be used for facilities that do not have the
ability to provide pc or pda access to patients. This paper form
can be transcribed or scanned and converted into an xml document
where it could be integrated with the contextbase (50) in an
automated fashion. If the patient has used a Personalized Modeling
System (100) that stored data related to his or her health, then
this information could be communicated to the Personalized Modeling
System (100) in an automated fashion via wireless connectivity,
wired connectivity or the transfer of files from the patient's
Personalized Modeling System (100) to a recordable media.
Recognizing that there are a number of options for completing data
entry we will simply say that "data entry is completed" when
describing each step.
Step 1--the patient details prior medical history and data entry is
completed. Because the patient is new, a new element for the
patient will automatically be created within the ontology and
contextbase (50) for the clinic. The medical history will be
associated with the new element for the patient in the element
layer. Any information regarding insurance will be tagged and
stored in the tactical layer which would determine eligibility by
communicating with the appropriate insurance provider. The measure
layer will in turn use this information to determine the expected
margin and/or generate a flag if the patient is not eligible for
insurance. Step 2--weight and blood pressure are checked by an aide
and data entry is completed. The medical history data are used to
generate a list of possible diagnoses based on the proximity of the
patient's history to previously defined disease clusters and
pathways by the analytics that support the instant impact and
outcome layers. Any data that is out of the normal range for the
cluster will be flagged for confirmation by the doctor. The
Personalized Modeling System (100) would also query external data
providers to see if the out of range data correlates with any new
clusters that may have been identified since the clinic's
contextbase (50) and ontology were established. The analytics in
the relationship layer would then identify the tests that should be
conducted to validate or invalidate possible diagnoses. Preference
would be given to the tests that provide information that is
relevant to the highest number of potential diagnoses for the
lowest cost. If the patient's history documented the diagnostic
imaging history, then consideration would also be given to
cumulative radiation levels when recommending tests. Step 3--the
doctor refers the patient to a diagnostic imaging center using the
process map for a pet scan (to look for tumors on the patient's
kidneys). He also refers the patient for genetic testing with a new
process map that assesses the patient's likely response to a new
type of chemotherapy. Step 4--The images and genetic tests are
completed in accordance with the specified process maps. As part of
this process, the Personalized Medicine Service (101) in the
imaging center highlights any probable tumors before displaying the
image to the radiologist for diagnosis. The Personalized Medicine
Service (102) in the genetic testing center would determine if the
test array displayed the biomarkers (indicators) that indicated a
likely favorable response to the new chemotherapy before having the
results analyzed by a technician. In both cases the results of the
analyses are sent to the Personalized Modeling System (100) in the
clinic for automated integration with the patient's medical
history. At this point, the Personalized Modeling System (100) in
the clinic would automatically update the list of likely diagnoses
to reflect the newly gathered information. Step 5--the doctor
reviews the information for the patient from the contextbase (50)
using the Complete Context.TM. Review Service (607) on a device (3)
such as a pda or personal computer. The doctor will have the
ability to define the exact format of the display by choosing the
mix of graphical and text information that will be displayed. At
this point, the doctor determines that the patient probably has
kidney cancer and refers the patient to a surgeon for further
treatment. He activates the process map for a surgical referral,
among other things this process map sends the patients medical
history to the surgeon's context service system (103) in an
automated fashion. Step 6--the surgeon examines the medical records
and the patient before scheduling surgery for a hospital where he
has privileges. He then activates the kidney surgery process map
which forwards the medical records to the hospital context service
system (104). Step 7--the surgeon completes a biopsy that confirms
the presence of a malignant tumor before scheduling and completing
the required surgery. After the surgery is completed, the surgeon
then activates the pre-defined process map for the new chemotherapy
(as noted previously, the patient's genetic biomarkers indicated
that he would likely respond well to this new treatment). As
information is added to the patient's medical history in the
hospital context service (104), it is also communicated back to the
Personalized Modeling System (100) in the clinic for inclusion in
the patient's medical history in an automated fashion and to the
relevant insurance company. Step 8--follow up. The chemotherapy
process map the doctor selected is used to identify the expected
sequence of events that the patient will use to complete his
treatment. If the patient fails to complete an event within the
specified time range or in the specified order, then the alerts
built into the tactical layer will generate email messages to the
doctor and/or case worker assigned to monitor the patient for
follow-up and possible corrective action. Bots could be used to
automate some aspects of routine follow-up like sending reminders
or requests for status via email or regular mail. This
functionality could also be used to collect information about
long-term outcomes from patients in an automated fashion. The
process map follow-up processing continues automatically until the
process ends, a clinician changes the process map for the patient
or the patient visits the facility again and the process described
above is repeated. In short, the services in the Complete
Context.TM. Suite (625) work together with the Personalized
Modeling System (100) to provide knowledgeable support to anyone
trying to analyze, manage and/or optimize actions, processes and
outcomes for any subject. The contextbase (50) supports the
services in the Complete Context.TM. Suite (625) as described
above. The contextbase (50) provides six important benefits: [0134]
1. By directly supporting entity performance, the system of the
present invention guarantees that the contextbase (50) will provide
a tangible benefit to the entity. [0135] 2. The measure focus
allows the system to partition the search space into two areas with
different levels of processing. Data and information that is known
to be relevant to the defined functions and/or measures as well as
data that are not thought to be relevant. The system does not
ignore data that is not known to be relevant; however, it is
processed less intensely. This information can also be used to
identify data for archiving or disposal. [0136] 3. The processing
completed in contextbase (50) development defines and maintains the
relevant schema or ontology for the entity. This schema or ontology
can be flexibly matched with other ontologies in order to interact
with other entities that have organized their information using a
different ontology. This functionality also enables the automated
extraction and integration of data from the semantic web. [0137] 4.
Defining the complete subject context allows every piece of data
that is generated to be placed "in context" when it is first
created. Traditional systems generally treat every piece of data in
an undifferentiated fashion. As a result, separate efforts are
often required to find the data, define a context and then place
the data in context. [0138] 5. The contextbase (50) includes robust
models of the components of context that drive action and event
frequency as well as levels to vary. This capability is very useful
in developing action plans to improve measure performance. [0139]
6. The focus on primary subject functions also ensures the
longevity of the contextbase (50) as entity primary functions
rarely change. For example, the primary function of each cell in
the human body has changed very little over the last 10,000
years.
[0140] Some of the important features of the patient centric
approach are summarized in Table 13.
TABLE-US-00012 TABLE 13 Characteristic Personalized Modeling System
(100) Tangible benefit Built-in Computation/ Partitioned Search
Space Ontology development Automated and maintenance Ability to
analyze new Automatic - learns from data element, resource or
factor Measures in alignment Automatic Data in context Automatic
System Longevity Equal to longevity of definable measure(s)
[0141] To facilitate its use as a tool for improving performance,
the Personalized Modeling System (100) produces reports in formats
that are graphical and highly intuitive. By combining this
capability with the previously described capabilities (developing
context, flexibly defining robust performance measures, optimizing
performance, reducing IT complexity and facilitating collaboration)
the Personalized Modeling System (100) gives individuals, groups
and clinicians the tools they need to model, manage and improve the
performance of any subject.
BRIEF DESCRIPTION OF DRAWINGS
[0142] These and other objects, features and advantages of the
present invention will be more readily apparent from the following
description of one embodiment of the invention in which:
[0143] FIG. 1 is a block diagram showing the major processing steps
of the present invention;
[0144] FIG. 2A, FIG. 2B and FIG. 2C are block diagrams showing a
relationship between constraints, elements, events, factors,
locations, measures, missions, processes and subject
actions/behavior;
[0145] FIG. 3 shows a relationship between an entity and other
entities, processes and groups;
[0146] FIG. 4 is a diagram showing the tables in the contextbase
(50) of the present invention that are utilized for data storage
and retrieval during the processing;
[0147] FIG. 5 is a block diagram of an implementation of the
present invention;
[0148] FIG. 6A, FIG. 6B and FIG. 6C are block diagrams showing the
sequence of steps in the present invention used for specifying
system settings, preparing data for processing and specifying the
subject measures;
[0149] FIG. 7A, FIG. 7B, FIG. 7C, FIG. 7D, FIG. 7E, FIG. 7F, FIG.
7G and FIG. 7H are block diagrams showing the sequence of steps in
the present invention used for creating a contextbase (50) for a
subject;
[0150] FIG. 8A and FIG. 8B are block diagrams showing the sequence
in steps in the present invention used in propagating a
Personalized Medicine Service, creating bots, services and
performance reports;
[0151] FIG. 9 is a diagram showing the data windows that are used
for receiving information from and transmitting information via the
interface (700);
[0152] FIG. 10 is a block diagram showing the sequence of
processing steps in the present invention used for identifying,
receiving and transmitting data with narrow systems (4);
[0153] FIG. 11 is a diagram showing how the Personalized Modeling
System (100) develops and supports a natural language interface
(714);
[0154] FIG. 12 is a sample report showing the efficient frontier
for Entity XYZ and the current position of XYZ relative to the
efficient frontier;
[0155] FIG. 13 is a diagram showing one embodiment of a
Personalized Modeling System (100) for a clinic;
[0156] FIG. 14 is a diagram showing how the Personalized Modeling
System (100) for a clinic can be used in conjunction with an
integration platform or exchange (99);
[0157] FIG. 15 is a diagram showing a portion of a process map for
treating a mental health patient;
[0158] FIG. 16 is a diagram showing an embodiment of the
Personalized Medicine Service (100) for a clinic that is connected
with a Personalized Medicine Service (107) for a patient, a
Personalized Medicine Service (106) for a health plan and an
exchange (99); and
[0159] FIG. 17 shows a universal context specification format.
DETAILED DESCRIPTION OF ONE PREFERRED EMBODIMENT
[0160] FIG. 1 provides an overview of the processing completed by
the innovative system for developing a Personalized Modeling System
(100). In accordance with the present invention, an automated
system and method for developing a contextbase (50) that supports
the development of a Personalized Modeling System (100) is
provided. In one preferred embodiment the contextbase (50) contains
context layers for each subject measure. Processing starts in this
Personalized Modeling System (100) when the data preparation
portion of the application software (200) extracts data from a
narrow system database (5); an external database (7); a world wide
web (8), external services (9) and optionally, a partner narrow
system database (6) via a network (45). The connection to the
network (45) can be via a wired connection, a wireless connection
or a combination thereof. It is to be understood that the World
Wide Web (8) also includes the semantic web that is being
developed. Data may also be obtained from a Complete Context.TM.
Input Service (601) or other applications that can provide xml
output. For example, newer versions of Microsoft.RTM. Office and
Adobe.RTM. Acrobat.RTM. can be used to provide data input to the
Personalized Modeling System (100) of the present invention.
[0161] After data are prepared, entity functions are defined and
subject measures are identified, as part of contextbase (50)
development in the second part of the application software (300).
The contextbase (50) is then used to create a Personalized Modeling
System (100) in the third stage of processing. The processing
completed by the Personalized Modeling System (100) may be
influenced by a user (40) or a manager (41) through interaction
with a user-interface portion of the application software (700)
that mediates the display, transmission and receipt of all
information to and from the Complete Context.TM. Input Service
(601) or browser software (800) such as the Mozilla or Opera
browsers in an access device (90) such as a phone, personal digital
assistant or personal computer where data are entered by the user
(40). The user (40) and/or manager (41) can also use a natural
language interface (714) provided by the Personalized Modeling
System (100).
[0162] While only one database of each type (5, 6 and 7) is shown
in FIG. 1, it is to be understood that the Personalized Modeling
System (100) can process information from all narrow systems (4)
listed in Tables 4, 5, 6 and/or 7 as well as the devices (3) listed
in Table 8 for each entity being supported.
[0163] In one embodiment, all functioning narrow systems (4)
associated with each entity will provide data access to the
Personalized Modeling System (100) via the network (45). It should
also be understood that it is possible to complete a bulk
extraction of data from each database (5, 6 and 7), the World Wide
Web (8) and external service (9) via the network (45) using peer to
peer networking and data extraction applications. In one
embodiment, the data extracted via the network (45) are tagged in a
virtual database that leaves all data in the original databases
where it can be retrieved and optionally converted for use in
calculations by the analysis bots over a network (45). In alternate
embodiments, the data could also be stored in a database, datamart,
data warehouse, a cluster (accessed via GPFS), a virtual repository
or a storage area network where the analysis bots could operate on
the aggregated data.
[0164] The operation of the system of the present invention is
determined by the options the user (40) and manager (41) specify
and store in the contextbase (50). As shown in FIG. 4, the
contextbase (50) contains tables for storing data by context layer
including: a key terms table (140), a element layer table (141), a
transaction layer table (142), an resource layer table (143), a
relationship layer table (144), a measure layer table (145), a
unassigned data table (146), an internet linkages table (147), a
causal link table (148), an environment layer table (149), an
uncertainty table (150), a context space table (151), an ontology
table (152), a report table (153), a reference layer table (154), a
hierarchy metadata table (155), an event risk table (156), a
subject schema table (157), an event model table (158), a
requirement table (159), a context frame table (160), a context
quotient table (161), a system settings table (162), a bot date
table (163), a Thesaurus table (164), an id to frame table (165),
an impact model table (166), a bot assignment table (167), a
scenarios table (168), a natural language table (169), a phoneme
table (170), a word table (171) and a phrase table (172). The
system of the present invention has the ability to accept and store
supplemental or primary data directly from user input, a data
warehouse, a virtual database, a data preparation system or other
electronic files in addition to receiving data from the databases
described previously. The system of the present invention also has
the ability to complete the necessary calculations without
receiving data from one or more of the specified databases.
However, in the embodiment described herein all information used in
processing is obtained from the specified data sources (5, 6, 7, 8,
9 and 601) for the subject and made available using a virtual
database.
[0165] As shown in FIG. 5, one embodiment of the present invention
is a computerized Personalized Modeling System (100) illustratively
comprised of a computer (110). The computer (110) is connected via
the network (45) to an Internet browser appliance (90) that
contains Internet software (800) such as a Mozilla browser or an
Opera browser. The browser (800) will support RSS feeds.
[0166] In one embodiment, the computer (110) has a read/write
random access memory (111), a hard drive (112) for storage of a
contextbase (50) and the application software (200, 300, 400 and
700), a keyboard (113), a communication bus (114), a display (115),
a mouse (116), a CPU (117), a printer (118) and a cache (119). As
devices (3) become more capable, they be used in place of the
computer (110). Larger entities may require the use of a grid or
cluster in place of the computer (110) to support Complete
Context.TM. Service processing requirements. In an alternate
configuration, all or part of the contextbase (50) can be
maintained separately from a device (3) or computer (110) and
accessed via a network (45) or grid.
[0167] The application software (200, 300, 400 and 700) controls
the performance of the central processing unit (117) as it
completes the calculations used to support Complete Context.TM.
Service development. In the embodiment illustrated herein, the
application software program (200, 300, 400 and 700) is written in
a combination of Java and C++. The application software (200, 300,
400 and 700) can use Structured Query Language (SQL) for extracting
data from the databases and the World Wide Web (5, 6, 7 and 8). The
user (40) and manager (41) can optionally interact with the
user-interface portion of the application software (700) using the
browser software (800) in the browser appliance (90) or through a
natural language interface (714) provided by the Personalized
Modeling System (100) to provide information to the application
software (200, 300, 400 and 700).
[0168] The computers (110) shown in FIG. 5 is a personal computer
that is widely available for use with Linux, Unix or Windows
operating systems. Typical memory configurations for client
personal computers (110) used with the present invention include
more than 1024 megabytes of semiconductor random access memory
(111) and a hard drive (112).
[0169] As discussed previously, the Personalized Modeling System
(100) completes processing in three distinct stages. As shown in
FIG. 6A, FIG. 6B and FIG. 6C the first stage of processing (block
200 from FIG. 1) identifies and prepares data from narrow system
databases (5); external databases (7); the world wide web (8),
external services (9) and optionally, a partner narrow system
database (6) for processing. This stage also identifies the entity
and entity function and/or mission measures. As shown in FIG. 7A,
FIG. 7B, FIG. 7C, FIG. 7D, FIG. 7E, FIG. 7F, FIG. 7G and FIG. 7H,
the second stage of processing (block 300 from FIG. 1) develops and
then continually updates a contextbase (50) for each subject
measure. As shown in FIG. 8A and FIG. 8B, the third stage of
processing (block 400 from FIG. 1) identifies the valid context
space before developing and distributing one or more entity
contexts via a Personalized Modeling System (100). The third stage
of processing also prepares and prints optional reports. If the
operation is continuous, then the processing steps described are
repeated continuously. As described below, one embodiment of the
software is a bot or agent architecture. Other architectures
including a service architecture, an n-tier client server
architecture, an integrated application architecture and
combinations thereof can be used to the same effect.
Entity Definition
[0170] The flow diagrams in FIG. 6A, FIG. 6B and FIG. 6C detail the
processing that is completed by the portion of the application
software (200) that defines the subject, identifies the functions
and measures for said subject, prepares data for use in processing
and accepts user (40) and management (41) input. As discussed
previously, the system of the present invention is capable of
accepting data from and transmitting data to all the narrow systems
(4) listed in Tables 4, 5, 6 and 7. It can also accept data from
and transmit data to the devices listed in Table 8. Data
extraction, processing and storage are normally completed by the
Personalized Modeling System (100). This data extraction,
processing and storage can be facilitated by a separate data
integration layer in an operating system or middleware application
as described in cross referenced application Ser. No. 10/748,890.
Operation of the Personalized Modeling System (100) will be
illustrated by describing the extraction and use of structured data
from a narrow system database (5) for supply chain management and
an external database (7). A brief overview of the information
typically obtained from these two databases will be presented
before reviewing each step of processing completed by this portion
(200) of the application software.
[0171] Supply chain systems are one of the narrow systems (4)
identified in Table 7. Supply chain databases are a type of narrow
system database (5) that contain information that may have been in
operation management system databases in the past. These systems
provide enhanced visibility into the availability of resources and
promote improved coordination between subject entities and their
supplier entities. All supply chain systems would be expected to
track all of the resources ordered by an entity after the first
purchase. They typically store information similar to that shown
below in Table 14.
TABLE-US-00013 TABLE 14 Supply chain system information 1. Stock
Keeping Unit (SKU) 2. Vendor 3. Total quantity on order 4. Total
quantity in transit 5. Total quantity on back order 6. Total
quantity in inventory 7. Quantity available today 8. Quantity
available next 7 days 9. Quantity available next 30 days 10.
Quantity available next 90 days 11. Quoted lead time 12. Actual
average lead time
[0172] External databases (7) are used for obtaining information
that enables the definition and evaluation of words, phrases,
context elements, context factors and event risks. In some cases,
information from these databases can be used to supplement
information obtained from the other databases and the World Wide
Web (5, 6 and 8). In the system of the present invention, the
information extracted from external databases (7) includes the data
listed in Table 15.
TABLE-US-00014 TABLE 15 External database information 1. Text
information such as that found in the Lexis Nexis database 2. Text
information from databases containing past issues of specific
publications 3. Multimedia information such as video and audio
clips 4. Idea market prices indicate likelihood of certain events
occurring 5. Event risk data including information about risk
probability and magnitude for weather and geological events 6.
Known phonemes and phrases
[0173] System processing of the information from the different data
sources (3, 4, 5, 6, 7, 8 and 9) described above starts in a block
202, FIG. 6A. The software in block 202 prompts the user (40) via
the system settings data window (701) to provide system setting
information. The system setting information entered by the user
(40) is stored in the system settings table (162) in the
contextbase (50). The specific inputs the user (40) is asked to
provide at this point in processing are shown in Table 16.
TABLE-US-00015 TABLE 16 1. Continuous, if yes, calculation
frequency? (by minute, hour, day, week, etc.) 2. Subject (patient,
group or patient-entity multi domain system) 3. SIC Codes 4. Names
of primary competitors by SIC Code (if applicable) 5. Base account
structure 6. Base units of measure 7. Base currency 8. Risk free
interest rate 9. Program bots or applications? (yes or no) 10.
Process measurements? (yes or no) 11. Probabilistic relational
models? (yes or no) 12. Knowledge capture and/or collaboration?
(yes or no) 13. Natural language interface? (yes, no or voice
activated) 14. Video data extraction? (yes or no) 15. Image data
extraction? (yes or no) 16. Internet data extraction? (yes or no)
17. Reference layer? (yes or no, if yes specify coordinate
system(s)) 18. Text data analysis? (yes or no) 19. Geo-coded data?
(if yes, then specify standard) 20. Maximum number of clusters
(default is six) 21. Management report types (text, graphic or
both) 22. Default missing data procedure (chose from selection) 23.
Maximum time to wait for user input 24. Maximum number of sub
elements (optional) 25. Most likely scenario, normal, extreme or
mix (default is normal) 26. System time period (days, month, years,
decades, light years, etc.) 27. Date range for history-forecast
time periods (optional) 28. Uncertainty level and source by narrow
system type (optionally, default is zero) 29. Weight of evidence
cutoff level (by context) 30. Time frame(s) for proactive search
(hours, days, weeks, etc.) 31. Node depth for scouting and/or
searching for data, information and knowledge 32. Impact cutoff for
scouting and/or searching for data, information and knowledge
The system settings data are used by the software in block 202 to
establish context layers. As described previously, there are
generally eight types of context layers for the subject. The
application of the remaining system settings will be further
explained as part of the detailed explanation of the system
operation. The software in block 202 also uses the current system
date and the system time period saved in the system settings table
(162) to determine the time periods (generally in months) where
data will be sought to complete the calculations. The user (40)
also has the option of specifying the time periods that will be
used for system calculations. After the date range is stored in the
system settings table (162) in the contextbase (50), processing
advances to a software block 203.
[0174] The software in block 203 prompts the user (40) via the
entity data window (702) to identify the subject, identify subject
functions and identify any extensions to the subject hierarchy or
hierarchies specified in the system settings table (162). For
example if the organism hierarchy (2300) was chosen, the user (40)
could extend the hierarchy by specifying a join with the cellular
hierarchy (2200). As part of the processing in this block, the user
(40) is also given the option to modify the subject hierarchy or
hierarchies. If the user (40) elects to modify one or more
hierarchies, then the software in the block will prompt the user
(40) to provide information for use in modifying the pre-defined
hierarchy metadata in the hierarchy metadata table (155) to
incorporate the modifications. The user (40) can also elect to
limit the number of separate levels that are analyzed below the
subject in a given hierarchy. For example, an organization could
choose to examine the impact of their divisions on organization
performance by limiting the context elements to one level below the
subject. After the user (40) completes the specification of
hierarchy extensions, modifications and limitations, the software
in block 203 selects the appropriate metadata from the hierarchy
metadata table (155) and establishes the hierarchy metadata (155)
and stores the ontology (152) and entity schema (157). The software
in block 203 uses the extensions, modifications and limitations
together with three rules for establishing the entity schema:
[0175] 1. the members of the entity hierarchy that are above the
subject are factors; [0176] 2. hierarchies that could be used to
extend the entity hierarchy that are not selected will be excluded;
and [0177] 3. all other hierarchies and groups will be potential
factors. After subject schema is developed, the user (40) is asked
to define process maps and procedures. The maps and procedures
identified by the user (40) are stored in the relationship layer
table (144) in the contextbase (50). The information provided by
the user (40) will be supplemented with information developed later
in the first stage of processing. It is also possible to obtain
relationship layer information concerning process maps and
procedures in an automated fashion by analyzing transaction
patterns or reverse engineering narrow systems (4) as they often
codify the relationship between different context elements,
factors, events, resources and/or actions. The Complete Context.TM.
Capture and Collaboration Service (622) can also be used here to
supplement the information provided by the user (40) with
information from subject matter experts (42). After data storage is
complete, processing advances to a software block 204.
[0178] The software in block 204 prompts a context interface window
(715) to communicate via a network (45) with the different devices
(3), systems (4), databases (5, 6, 7), the World Wide Web (8) and
external services (9) that are data sources for the Personalized
Modeling System (100). As shown on FIG. 10 the context interface
window (715) contains a multiple step operation where the sequence
of steps depends on the nature of the interaction and the data
being provided to the Personalized Modeling System (100). In one
embodiment, a data input session would be managed by the a software
block (720) that identifies the data source (3, 4, 5, 6, 7, 8 or 9)
using standard protocols such as UDDI or xml headers, maintains
security and establishes a service level agreement with the data
source (3, 4, 5, 6, 7, 8 or 9). The data provided at this point
could include transaction data, descriptive data, imaging data,
video data, text data, sensor data, geospatial coordinate data,
array data, virtual reference coordinate data and combinations
thereof. The session would proceed to a software block (722) for
pre-processing such as discretization, transformation and/or
filtering. After completing the pre-processing in software block
722, processing would advance to a software block (724). The
software in that block would determine if the data provided by the
data source (3, 4, 5, 6, 7, 8 or 9) complied with the entity schema
or ontology using pair-wise similarity measures on several
dimensions including terminology, internal structure, external
structure, extensions, hierarchical classifications (see Tables 1,
2 and 3) and semantics. If it did comply, then the data would not
require alignment and the session would advance to a software block
(732) where any conversions to match the base units of measure,
currency or time period specified in the system settings table
(162) would be identified before the session advanced to a software
block (734) where the location of this data would be mapped to the
appropriate context layers and stored in the contextbase (50).
Establishing a virtual database in this manner eliminates the
latency that can cause problems for real time processing. The
virtual database information for the element layer for the subject
and context elements is stored in the element layer table (141) in
the contextbase (50). The virtual database information for the
resource layer for the subject resources is stored in the resource
layer table (143) in the contextbase (50). The virtual database
information for the environment layer for the subject and context
factors is stored in the environment layer table (149) in the
contextbase (50). The virtual database information for the
transaction layer for the subject, context elements, actions and
events is stored in the transaction layer table (142) in the
contextbase (50). The processing path described in this paragraph
is just one of many paths for processing data input.
[0179] As shown FIG. 10, the context interface window (715) has
provisions for an alternate data input processing path. This path
is used if the data are not in alignment with the entity schema
(157) or ontology (152). In this alternate mode, the data input
session would still be managed by the session management software
in block (720) that identifies the data source (3, 4, 5, 6, 7, 8 or
9) maintains security and establishes a service level agreement
with the data source (3, 4, 5, 6, 7, 8 or 9). The session would
proceed to the pre-processing software block (722) where the data
from one or more data sources (3,4, 5, 6, 7, 8 or 9) that requires
translation and optional analysis is processed before proceeding to
the next step. The software in block 722 has provisions for
translating, parsing and other pre-processing of audio, image,
micro-array, transaction, video and unformatted text data formats
to schema or ontology compliant formats (xml formats in one
embodiment). The audio, text and video data are prepared as
detailed in cross referenced patent application Ser. No.
10/717,026. Image translation involves conversion, registration,
segmentation and segment identification using object boundary
models. Other image analysis algorithms can be used to the same
effect. Other pre-processing steps can include discretization and
stochastic resonance processing. After pre-processing is complete,
the session advances to a software block 724. The software in block
724 determine whether or not the data was in alignment with the
ontology (152) or schema (157) stored in the contextbase (50) using
pair wise comparisons as described previously. Processing then
advances to the software in block 736 which uses the mappings
identified by the software in block 724 together with a series of
matching algorithms including key properties, similarity, global
namespace, value pattern and value range algorithms to align the
input data with the entity schema (157) or ontology (152).
Processing, then advances to a software block 738 where the
metadata associated with the data are compared with the metadata
stored in the subject schema table (157). If the metadata are
aligned, then processing is completed using the path described
previously. Alternatively, if the metadata are still not aligned,
then processing advances to a software block 740 where joins,
intersections and alignments between the two schemas or ontologies
are completed in an automated fashion. Processing then advances to
a software block 742 where the results of these operations are
compared with the schema (157) or ontology (152) stored in the
contextbase (50). If these operations have created alignment, then
processing is completed using the path described previously.
Alternatively, if the metadata are still not aligned, then
processing advances to a software block 746 where the schemas
and/or ontologies are checked for partial alignment. If there is
partial alignment, then processing advances to a software block
744. Alternatively, if there is no alignment, then processing
advances to a software block 747 where the data are tagged for
manual review and stored in the unassigned data table (146). The
software in block 744 cleaves the data in order to separate the
portion that is in alignment from the portion that is not in
alignment. The portion of the data that is not in alignment is
forwarded to software block 747 where it is tagged for manual
alignment and stored in the unassigned data table (146). The
portion of the data that is in alignment is processed using the
path described previously. Processing advances to a block 748 where
the user (40) reviews the unassigned data table (146) using the
review window (703) to see if the entity schema should be modified
to encompass the currently unassigned data and the changes in the
schema (157) and/or ontology (152)--if any--are saved in the
contextbase (50).
[0180] After context interface window (715) processing is completed
for all available data from the devices (3), systems (4), databases
(5, 6 and 7), the World Wide Web (8), and external services (9),
processing advances to a software block 206 where the software in
block 206 optionally prompts the context interface window (715) to
communicate via a network (45) with the Complete Context.TM. Input
Service (601). The context interface window (715) uses the path
described previously for data input to map the identified data to
the appropriate context layers and store the mapping information in
the contextbase (50) as described previously. After storage of the
Complete Context.TM. Input Service (601) data are complete,
processing advances to a software block 207.
[0181] The software in block 207 prompts the user (40) via the
review data window (703) to optionally review the context layer
data that has been stored in the first few steps of processing. The
user (40) has the option of changing the data on a one time basis
or permanently. Any changes the user (40) makes are stored in the
table for the corresponding context layer (i.e. transaction layer
changes are saved in the transaction layer table (142), etc.). As
part of the processing in this block, an interactive GEL algorithm
prompts the user (40) via the review data window (703) to check the
hierarchy or group assignment of any new elements, factors and
resources that have been identified. Any newly defined categories
are stored in the relationship layer table (144) and the subject
schema table (157) in the contextbase (50) before processing
advances to a software block 208.
[0182] The software in block 208 prompts the user (40) via the
requirement data window (710) to optionally identify requirements
for the subject. Requirements can take a variety of forms but the
two most common types of requirements are absolute and relative.
For example, a requirement that the level of cash should never drop
below $50,000 is an absolute requirement while a requirement that
there should never be less than two months of cash on hand is a
relative requirement. The user (40) also has the option of
specifying requirements as a subject function later in this stage
of processing. Examples of different requirements are shown in
Table 17.
TABLE-US-00016 TABLE 17 Entity Requirement (reason) Individual
(1401) Stop working at 67 (retirement) Keep blood pressure below
155/95 (health) Available funds > $X by 01/01/14 (college for
daughter) Government Organization Foreign currency reserves > $X
(IMF (1607) requirement) 3 functional divisions on standby
(defense) Pension assets > liabilities (legal) Circulatory
System (2304) Cholesterol level between 120 and 180 Pressure
between 110/75 and 150/100
The software in this block provides the ability to specify absolute
requirements, relative requirements and standard "requirements" for
any reporting format that is defined for use by the Complete
Context.TM. Review Service (607).
[0183] After requirements are specified, they are stored in the
requirement table (159) in the contextbase (50) by entity before
processing advances to a software block 211.
[0184] The software in block 211 checks the unassigned data table
(146) in the contextbase (50) to see if there are any data that has
not been assigned to an entity and/or context layer. If there are
no data without a complete assignment (entity and element,
resource, factor or transaction context layer constitutes a
complete assignment), then processing advances to a software block
214. Alternatively, if there are data without an assignment, then
processing advances to a software block 212. The software in block
212 prompts the user (40) via the identification and classification
data window (705) to identify the context layer and entity
assignment for the data in the unassigned data table (146). After
assignments have been specified for every data element, the
resulting assignments are stored in the appropriate context layer
tables in the contextbase (50) by entity before processing advances
to a software block 214.
[0185] The software in block 214 checks the element layer table
(141), the transaction layer table (142) and the resource layer
table (143) and the environment layer table (149) in the
contextbase (50) to see if data are missing for any specified time
period. If data are not missing for any time period, then
processing advances to a software block 218. Alternatively, if data
for one or more of the specified time periods identified in the
system settings table (162) for one or more items is missing from
one or more context layers, then processing advances to a software
block 216. The software in block 216 prompts the user (40) via the
review data window (703) to specify the procedure that will be used
for generating values for the items that are missing data by time
period. Options the user (40) can choose at this point include: the
average value for the item over the entire time period, the average
value for the item over a specified time period, zero or the
average of the preceding item and the following item values and
direct user input for each missing value. If the user (40) does not
provide input within a specified interval, then the default missing
data procedure specified in the system settings table (162) is
used. When the missing time periods have been filled and stored for
all the items that were missing data, then system processing
advances to a block 218.
[0186] The software in block 218 retrieves data from the element
layer table (141), the transaction layer table (142), the resource
layer table (143) and the environment layer table (149). It uses
this data to calculate indicators for the data associated with each
element, resource and environmental factor. The indicators
calculated in this step are comprised of comparisons, regulatory
measures and statistics. Comparisons and statistics are derived
for: appearance, description, numeric, shape, shape/time and time
characteristics. These comparisons and statistics are developed for
different types of data as shown below in Table 18.
TABLE-US-00017 TABLE 18 Characteristic/ Appear- Descrip- Numer-
Shape- Data type ance tion ic Shape Time Time audio X X X
coordinate X X X X X image X X X X X text X X X transaction X X
video X X X X X X = comparisons and statistics are developed for
these characteristic/data type combinations
Numeric characteristics are pre-assigned to different domains.
Numeric characteristics include amperage, area, concentration,
density, depth, distance, growth rate, hardness, height, hops,
impedance, level, mass to charge ratio, nodes, quantity, rate,
resistance, similarity, speed, tensile strength, voltage, volume,
weight and combinations thereof. Time characteristics include
frequency measures, gap measures (i.e. time since last occurrence,
average time between occurrences, etc.) and combinations thereof.
The numeric and time characteristics are also combined to calculate
additional indicators. Comparisons include: comparisons to baseline
(can be binary, 1 if above, 0 if below), comparisons to external
expectations, comparisons to forecasts, comparisons to goals,
comparisons to historical trends, comparisons to known bad,
comparisons to known good, life cycle comparisons, comparisons to
normal, comparisons to peers, comparisons to regulations,
comparison to requirements, comparisons to a standard, sequence
comparisons, comparisons to a threshold (can be binary, 1 if above,
0 if below) and combinations thereof. Statistics include: averages
(mean, median and mode), convexity, copulas, correlation,
covariance, derivatives, Pearson correlation coefficients, slopes,
trends and variability. Time lagged versions of each piece of data,
statistic and comparison are also developed. The numbers derived
from these calculations are collectively referred to as
"indicators" (also known as item performance indicators and factor
performance indicators). The software in block 218 also calculates
mathematical and/or logical combinations of indicators called
composite variables (also known as composite factors when
associated with environmental factors). These combinations include
both pre-defined combinations and derived combinations. The AQ
program is used for deriving combinations. It should be noted that
other attribute derivation algorithms, such as the LINUS
algorithms, may be used to generate the combinations. The
indicators and the composite variables are tagged and stored in the
appropriate context layer table--the element layer table (141), the
resource layer table (143) or the environment layer table
(149)--before processing advances to a software block 220.
[0187] The software in block 220 checks the bot date table (163)
and deactivates pattern bots with creation dates before the current
system date and retrieves information from the system settings
table (162), the element layer table (141), the transaction layer
table (142), the resource layer table (143) and the environment
layer table (149). The software in block 220 then initializes
pattern bots for each layer to identify patterns in each layer.
Bots are independent components of the application software of the
present invention that complete specific tasks. In the case of
pattern bots, their tasks are to identify patterns in the data
associated with each context layer. In one embodiment, pattern bots
use Apriori algorithms identify patterns including frequent
patterns, sequential patterns and multi-dimensional patterns.
However, a number of other pattern identification algorithms
including the sliding window algorithm; differential association
rule, beam-search, frequent pattern growth, decision trees and the
PASCAL algorithm can be used alone or in combination to the same
effect. Every pattern bot contains the information shown in Table
19.
TABLE-US-00018 TABLE 19 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Storage location 4. Entity type(s) 5. Entity 6. Context
Layer 7. Algorithm
After being initialized, the bots identify patterns for the data
associated with elements, resources, factors and combinations
thereof. Each pattern is given a unique identifier and the
frequency and type of each pattern is determined. The numeric
values associated with the patterns are indicators. The values are
stored in the appropriate context layer table before processing
advances to a software block 222.
[0188] The software in block 222 uses causal association algorithms
including LCD, CC and CU to identify causal associations between
indicators, composite variables, element data, factor data,
resource data and events, actions, processes and measures. The
software in this block uses semantic association algorithms
including path length, subsumption, source uncertainty and context
weight algorithms to identify associations. The identified
associations are stored in the causal link table (148) for possible
addition to the relationship layer table (144) before processing
advances to a software block 224.
[0189] The software in block 224 uses a tournament of petri nets,
time warping algorithms and stochism algorithms to identify
probable subject processes in an automated fashion. Other pathway
identification algorithms can be used to the same effect. The
identified processes are stored in the relationship layer table
(144) before processing advances to a software block 226.
[0190] The software in block 226 prompts the user (40) via the
review data window (703) to optionally review the new associations
stored in the causal link table (148) and the newly identified
processes stored in the relationship layer table (144).
Associations and/or processes that have already been specified or
approved by the user (40) will not be displayed automatically. The
user (40) has the option of accepting or rejecting each identified
association or process. Any associations or processes the user (40)
accepts are stored in the relationship layer table (144) before
processing advances a software block 242.
[0191] The software in block 242 checks the measure layer table
(145) in the contextbase (50) to determine if there are current
models for all measures for every entity. If all measure models are
current, then processing advances to a software block 252.
Alternatively, if all measure models are not current, then the next
measure for the next entity is selected and processing advances to
a software block 244.
[0192] The software in block 244 checks the bot date table (163)
and deactivates event risk bots with creation dates before the
current system date. The software in the block then retrieves the
information from the transaction layer table (142), the
relationship layer table (144), the event risk table (156), the
subject schema table (157) and the system settings table (162) in
order to initialize event risk bots for the subject in accordance
with the frequency specified by the user (40) in the system
settings table (162). Bots are independent components of the
application software that complete specific tasks. In the case of
event risk bots, their primary tasks are to forecast the frequency
and magnitude of events that are associated with negative measure
performance in the relationship layer table (144). In addition to
forecasting risks that are traditionally covered by insurance such
as fires, floods, earthquakes and accidents, the system of the
present invention also uses the data to forecast standard,
"non-insured" event risks such as the risk of employee resignation
and the risk of customer defection. The system of the present
invention uses a tournament forecasting method for event risk
frequency and duration. The mapping information from the
relationship layer is used to identify the elements, factors,
resources and/or actions that will be affected by each event. Other
forecasting methods can be used to the same effect. Every event
risk bot contains the information shown in Table 20.
TABLE-US-00019 TABLE 20 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Hierarchy or
group 6. Entity 7. Event (fire, flood, earthquake, tornado,
accident, defection, etc.)
After the event risk bots are initialized they activate in
accordance with the frequency specified by the user (40) in the
system settings table (162). After being activated the bots
retrieve the specified data and forecast the frequency and measure
impact of the event risks. The resulting forecasts are stored in
the event risk table (156) before processing advances to a software
block 246.
[0193] The software in block 246 checks the bot date table (163)
and deactivates extreme risk bots with creation dates before the
current system date. The software in block 246 then retrieves the
information from the transaction layer table (142), the
relationship layer table (144), the event risk table (156), the
subject schema table (157) and the system settings table (162) in
order to initialize extreme risk bots in accordance with the
frequency specified by the user (40) in the system settings table
(162). Bots are independent components of the application software
that complete specific tasks. In the case of extreme risk bots,
their primary task is to forecast the probability of extreme events
for events that are associated with negative measure performance in
the relationship layer table (144). The extreme risks bots use the
Blocks method and the peak over threshold method to forecast
extreme risk magnitude and frequency. Other extreme risk algorithms
can be used to the same effect. The mapping information is then
used to identify the elements, factors, resources and/or actions
that will be affected by each extreme risk. Every extreme risk bot
activated in this block contains the information shown in Table
21.
TABLE-US-00020 TABLE 21 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Hierarchy or
Group 6. Entity 7. Method: blocks or peak over threshold 8. Event
(fire, flood, earthquake, tornado, accident, defection, etc.)
After the extreme risk bots are initialized, they activate in
accordance with the frequency specified by the user (40) in the
system settings table (162). Once activated, they retrieve the
specified information, forecast extreme event risks and map the
impacts to the different elements, factors, resources and/or
actions. The extreme event risk information is stored in the event
risk table (156) in the contextbase (50) before processing advances
to a software block 248.
[0194] The software in block 248 checks the bot date table (163)
and deactivates competitor risk bots with creation dates before the
current system date. The software in block 248 then retrieves the
information from the transaction layer table (142), the
relationship layer table (144), the event risk table (156), the
subject schema table (157) and the system settings table (162) in
order to initialize competitor risk bots in accordance with the
frequency specified by the user (40) in the system settings table
(162). Bots are independent components of the application software
that complete specific tasks. In the case of competitor risk bots,
their primary task is to identify the probability of competitor
actions and/or events that are associated with negative measure
performance in the relationship layer table (144). The competitor
risk bots use game theoretic real option models to forecast
competitor risks. Other risk forecasting algorithms can be used to
the same effect. The mapping information is then used to identify
the elements, factors, resources and/or actions that will be
affected by each customer risk. Every competitor risk bot activated
in this block contains the information shown in Table 22.
TABLE-US-00021 TABLE 22 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Entity
type(s) 6. Entity 7. Competitor
After the competitor risk bots are initialized, they retrieve the
specified information and forecast the frequency and magnitude of
competitor risks. The bots save the competitor risk information in
the event risk table (156) in the contextbase (50) and processing
advances to a block 250.
[0195] The software in block 250 retrieves data from the event risk
table (156) and the subject schema table (157) before using a
measures data window (704) to display a table showing the
distribution of risk impacts by element, factor, resource and
action. After the review of the table is complete, the software in
block 250 prompts the manager (41) via the measures data window
(704) to specify one or more measures for the subject. Measures are
quantitative indications of subject behavior or performance. The
primary types of behavior are production (includes improvements and
new creations), destruction (includes reductions and complete
destruction) and maintenance. As discussed previously, the manager
(41) is given the option of using pre-defined measures or creating
new measures using terms defined in the subject schema table (157).
The measures can combine performance and risk measures or the
performance and risk measures can be kept separate. If more than
one measure is defined for the subject, then the manager (41) is
prompted to assign a weighting or relative priority to the
different measures that have been defined. As system processing
advances, the assigned priorities can be compared to the priorities
that entity actions indicate are most important. The priorities
used to guide analysis can be the stated priorities, the inferred
priorities or some combination thereof. The gap between stated
priorities and actual priorities is a congruence measure that can
be used in analyzing aspects of performance--particularly mental
health.
[0196] After the specification of measures and priorities has been
completed, the values of each of the newly defined measures are
calculated using historical data and forecast data. If forecast
data are not available, then the Complete Context.TM. Forecast
Service (603) is used to supply the missing values. These values
are then stored in the measure layer table (145) along with the
measure definitions and priorities. When data storage is complete,
processing advances to a software block 252.
[0197] The software in block 252 checks the bot date table (163)
and deactivates forecast update bots with creation dates before the
current system date. The software in block 252 then retrieves the
information from the system settings table (162) and environment
layer table (149) in order to initialize forecast bots in
accordance with the frequency specified by the user (40) in the
system settings table (162). Bots are independent components of the
application software of the present invention that complete
specific tasks. In the case of forecast update bots, their task is
to compare the forecasts for context factors and with the
information available from futures exchanges (including idea
markets) and update the existing forecasts. This function is
generally only used when the system is not run continuously. Every
forecast update bot activated in this block contains the
information shown in Table 23.
TABLE-US-00022 TABLE 23 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Entity
type(s) 6. Entity 7. Context factor 8. Measure 9. Forecast time
period
After the forecast update bots are initialized, they activate in
accordance with the frequency specified by the user (40) in the
system settings table (162). Once activated, they retrieve the
specified information and determine if any forecasts need to be
updated to bring them in line with the market data. The bots save
the updated forecasts in the environment layer table (149) by
entity and processing advances to a software block 254.
[0198] The software in block 254 checks the bot date table (163)
and deactivates scenario bots with creation dates before the
current system date. The software in block 254 then retrieves the
information from the system settings table (162), the element layer
table (141), the transaction layer table (142), the resource layer
table (143), the relationship layer table (144), the environment
layer table (149), the event risk table (156) and the subject
schema table (157) in order to initialize scenario bots in
accordance with the frequency specified by the user (40) in the
system settings table (162).
[0199] Bots are independent components of the application software
of the present invention that complete specific tasks. In the case
of scenario bots, their primary task is to identify likely
scenarios for the evolution of the elements, factors, resources and
event risks by entity. The scenario bots use the statistics
calculated in block 218 together with the layer information
retrieved from the contextbase (50) to develop forecasts for the
evolution of the elements, factors, resources, events and actions
under normal conditions, extreme conditions and a blended
extreme-normal scenario. Every scenario bot activated in this block
contains the information shown in Table 24.
TABLE-US-00023 TABLE 24 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Type: normal,
extreme or blended 6. Entity type(s) 7. Entity 8. Measure
After the scenario bots are initialized, they activate in
accordance with the frequency specified by the user (40) in the
system settings table (162). Once activated, they retrieve the
specified information and develop a variety of scenarios as
described previously. After the scenario bots complete their
calculations, they save the resulting scenarios in the scenarios
table (168) by entity in the contextbase (50) and processing
advances to a block 301.
Contextbase Development
[0200] The flow diagrams in FIG. 7A, FIG. 7B, FIG. 7C, FIG. 7D,
FIG. 7E, FIG. 7F, FIG. 7G and FIG. 7H detail the processing that is
completed by the portion of the application software (300) that
continually develops a function measure oriented contextbase (50)
by creating and activating analysis bots that: [0201] 1. Supplement
the relationship layer (144) information developed previously by
identifying relationships between the elements, factors, resources,
events, actions and one or more measures; [0202] 2. Complete the
measure layer (145) by developing robust models of the elements,
factors, resources, events and/or actions driving measure
performance; [0203] 3. Develop robust models of the elements,
factors, resources and events driving action and/or event
occurrence rates and impact levels; [0204] 4. Analyze measures for
the subject hierarchy in order to evaluate alignment and adjust
measures in order to achieve alignment in an automated fashion; and
[0205] 5. Determine the relationship between function and/or
mission measures and subject performance. Each analysis bot
generally normalizes the data being analyzed before processing
begins. As discussed previously, processing in this embodiment
includes an analysis of all measures and alternative architectures
that include a web and/or grid service architecture. The system of
the present invention can combine any number of measures in order
to evaluate the performance of any entity in the seventeen
hierarchies/groups described previously.
[0206] Before discussing this stage of processing in more detail,
it will be helpful to review the processing already completed. As
discussed previously, we are interested developing the complete
context for the behavior of a subject. We will develop this
complete context by developing a detailed understanding of the
impact of elements, environmental factors, resources, events,
actions and other relevant entities on one or more subject function
and/or mission measures. Some of the elements and resources may
have been grouped together to complete processes (a special class
of element). The first stage of processing reviewed the data from
some or all of the narrow systems (4) listed in Table 4, 5, 6 and 7
and the devices (3) listed in Table 8 and established a contextbase
(50) that formalized the understanding of the identity and
description of the elements, factors, resources, events and
transactions that impact subject function and/or mission measure
performance. The contextbase (50) also ensures ready access to the
data used for the second and third stages of computation in the
Personalized Modeling System (100). In the second stage of
processing we will use the contextbase (50) to develop an
understanding of the relative impact of the different elements,
factors, resources, events and transactions on subject
measures.
[0207] Because processes rely on elements and resources to produce
actions, the user (40) is given the choice between a process view
and an element view for measure analysis to avoid double counting.
If the user (40) chooses the element approach, then the process
impact can be obtained by allocating element and resource impacts
to the processes. Alternatively, if the user (40) chooses the
process approach, then the process impacts can be divided by
element and resource.
[0208] Processing in this portion of the application begins in
software block 301. The software in block 301 checks the measure
layer table (145) in the contextbase (50) to determine if there are
current models for all measures for every entity. Measures that are
integrated to combine the performance and risk measures into an
overall measure are considered two measures for purposes of this
evaluation. If all measure models are current, then processing
advances to a software block 322. Alternatively, if all measure
models are not current, then processing advances to a software
block 302.
[0209] The software in block 302 checks the subject schema table
(157) in the contextbase (50) to determine if spatial data is being
used. If spatial data is being used, then processing advances to a
software block 341. Alternatively, if all spatial data are not
being used, then processing advances to a software block 303.
[0210] The software in block 303 retrieves the previously
calculated values for the next measure from the measure layer table
(145) before processing advances to a software block 304. The
software in block 304 checks the bot date table (163) and
deactivates temporal clustering bots with creation dates before the
current system date. The software in block 304 then initializes
bots in accordance with the frequency specified by the user (40) in
the system settings table (162). The bots retrieve information from
the measure layer table (145) for the entity being analyzed and
defines regimes for the measure being analyzed before saving the
resulting cluster information in the relationship layer table (144)
in the contextbase (50). Bots are independent components of the
application software of the present invention that complete
specific tasks. In the case of temporal clustering bots, their
primary task is to segment measure performance into distinct time
regimes that share similar characteristics. The temporal clustering
bot assigns a unique identification (id) number to each "regime" it
identifies before tagging and storing the unique id numbers in the
relationship layer table (144). Every time period with data are
assigned to one of the regimes. The cluster id for each regime is
associated with the measure and entity being analyzed. The time
regimes are developed using a competitive regression algorithm that
identifies an overall, global model before splitting the data and
creating new models for the data in each partition. If the error
from the two models is greater than the error from the global
model, then there is only one regime in the data. Alternatively, if
the two models produce lower error than the global model, then a
third model is created. If the error from three models is lower
than from two models then a fourth model is added. The processing
continues until adding a new model does not improve accuracy. Other
temporal clustering algorithms may be used to the same effect.
Every temporal clustering bot contains the information shown in
Table 25.
TABLE-US-00024 TABLE 25 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Maximum
number of clusters 6. Entity type(s) 7. Entity 8. Measure
When bots in block 304 have identified and stored regime
assignments for all time periods with measure data for the current
entity, processing advances to a software block 305.
[0211] The software in block 305 checks the bot date table (163)
and deactivates variable clustering bots with creation dates before
the current system date. The software in block 305 then initializes
bots in order for each element, resource and factor for the current
entity. The bots activate in accordance with the frequency
specified by the user (40) in the system settings table (162),
retrieve the information from the element layer table (141), the
transaction layer table (142), the resource layer table (143), the
environment layer table (149) and the subject schema table (157) in
order and define segments for element, resource and factor data
before tagging and saving the resulting cluster information in the
relationship layer table (144).
[0212] Bots are independent components of the application software
of the present invention that complete specific tasks. In the case
of variable clustering bots, their primary task is to segment the
element, resource and factor data--including performance
indicators--into distinct clusters that share similar
characteristics. The clustering bot assigns a unique id number to
each "cluster" it identifies, tags and stores the unique id numbers
in the relationship layer table (144). Every item variable for each
element, resource and factor is assigned to one of the unique
clusters. The element data, resource data and factor data are
segmented into a number of clusters less than or equal to the
maximum specified by the user (40) in the system settings table
(162). The data are segmented using several clustering algorithms
including: an unsupervised "Kohonen" neural network, decision tree,
context distance, support vector method, K-nearest neighbor,
expectation maximization (EM) and the segmental K-means algorithm.
For algorithms that normally use the specified number of clusters
the bot will use the maximum number of clusters specified by the
user (40) in the system settings table (162). Every variable
clustering bot contains the information shown in Table 26.
TABLE-US-00025 TABLE 26 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Context
component 6. Clustering algorithm type 7. Entity type(s) 8. Entity
9. Measure 10. Maximum number of clusters 11. Variable 1 . . . to
11 + n. Variable n
When bots in block 305 have identified, tagged and stored cluster
assignments for the data associated with every element, resource
and factor in the relationship layer table (144), processing
advances to a software block 307.
[0213] The software in block 307 checks the measure layer table
(145) in the contextbase (50) to see if the current measure is an
options based measure like contingent liabilities, real options or
competitor risk. If the current measure is not an options based
measure, then processing advances to a software block 309.
Alternatively, if the current measure is an options based measure,
then processing advances to a software block 308.
[0214] The software in block 308 checks the bot date table (163)
and deactivates option bots with creation dates before the current
system date. The software in block 308 then retrieves the
information from the system settings table (162), the subject
schema table (157) and the element layer table (141), the
transaction layer table (142), the resource layer table (143), the
relationship layer table (144), the environment layer table (149)
and the scenarios table (168) in order to initialize option bots in
accordance with the frequency specified by the user (40) in the
system settings table (162).
[0215] Bots are independent components of the application software
of the present invention that complete specific tasks. In the case
of option bots, their primary task is to determine the impact of
each element, resource and factor on the entity option measure
under different scenarios. The option simulation bots run a normal
scenario, an extreme scenario and a combined scenario with and
without clusters. In one embodiment, Monte Carlo models are used to
complete the probabilistic simulation, however other option models
including binomial models, multinomial models and dynamic
programming can be used to the same effect. The element, resource
and factor impacts on option measures could be determined using the
process detailed below for the other types of measures. However, in
the one preferred embodiment being described herein, a separate
procedure is used. Every option bot activated in this block
contains the information shown in Table 27.
TABLE-US-00026 TABLE 27 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Scenario:
normal, extreme or combined 6. Option type: real option, contingent
liability or competitor risk 7. Entity type(s) 8. Entity 9. Measure
10. Clustered data? (yes or no) 11. Algorithm
After the option bots are initialized, they activate in accordance
with the frequency specified by the user (40) in the system
settings table (162). Once activated, the bots retrieve the
specified information and simulate the measure over the time
periods specified by the user (40) in the system settings table
(162) in order to determine the impact of each element, resource
and factor on the option. After the option bots complete their
calculations, the impacts and sensitivities for the option
(clustered data--yes or no) that produced the best result under
each scenario are saved in the measure layer table (145) in the
contextbase (50) and processing returns to software block 301.
[0216] If the current measure was not an option measure, then
processing advanced to software block 309. The software in block
309 checks the bot date table (163) and deactivates all predictive
model bots with creation dates before the current system date. The
software in block 309 then retrieves the information from the
system settings table (162), the subject schema table (157), the
element layer table (141), the transaction layer table (142), the
resource layer table (143), the relationship layer table (144) and
the environment layer table (149) in order to initialize predictive
model bots for each measure layer.
[0217] Bots are independent components of the application software
that complete specific tasks. In the case of predictive model bots,
their primary task is to determine the relationship between the
indicators and the one or more measures being evaluated. Predictive
model bots are initialized for each cluster and regime of data in
accordance with the cluster and regime assignments specified by the
bots in blocks 304 and 305. A series of predictive model bots is
initialized at this stage because it is impossible to know in
advance which predictive model type will produce the "best"
predictive model for the data from each entity. The series for each
model includes: neural network, CART, GARCH, constraint net,
projection pursuit regression, stepwise regression, logistic
regression, probit regression, factor analysis, growth modeling,
linear regression, redundant regression network, boosted Naive
Bayes Regression, support vector method, markov models, kriging,
multivalent models, Gillespie models, relevance vector method,
MARS, rough-set analysis and generalized additive model (GAM).
Other types predictive models can be used to the same effect. Every
predictive model bot contains the information shown in Table
28.
TABLE-US-00027 TABLE 28 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Entity
type(s) 6. Entity 7. Measure 8. Type: cluster, regime, cluster
& regime 9. Predictive model type
After predictive model bots are initialized, the bots activate in
accordance with the frequency specified by the user (40) in the
system settings table (162). Once activated, the bots retrieve the
specified data from the appropriate table in the contextbase (50)
and randomly partition the element, resource or factor data into a
training set and a test set. The software in block 309 uses
"bootstrapping" where the different training data sets are created
by re-sampling with replacement from the original training set so
data records may occur more than once. Training with genetic
algorithms can also be used. After the predictive model bots in the
tournament complete their training and testing, the best fit
predictive model assessments of element, resource and factor
impacts on measure performance are saved in the measure layer table
(145) before processing advances to a block 310.
[0218] The software in block 310 determines if clustering improved
the accuracy of the predictive models generated by the bots in
software block 309 by entity. The software in block 310 uses a
variable selection algorithm such as stepwise regression (other
types of variable selection algorithms can be used) to combine the
results from the predictive model bot analyses for each type of
analysis--with and without clustering--to determine the best set of
variables for each type of analysis. The type of analysis having
the smallest amount of error as measured by applying the root mean
squared error algorithm to the test data is given preference in
determining the best set of variables for use in later analysis.
Other error algorithms including entropy measures may also be used.
There are four possible outcomes from this analysis as shown in
Table 29.
TABLE-US-00028 TABLE 29 1. Best model has no clustering 2. Best
model has temporal clustering, no variable clustering 3. Best model
has variable clustering, no temporal clustering 4. Best model has
temporal clustering and variable clustering
If the software in block 310 determines that clustering improves
the accuracy of the predictive models for an entity, then
processing advances to a software block 314. Alternatively, if
clustering does not improve the overall accuracy of the predictive
models for an entity, then processing advances to a software block
312.
[0219] The software in block 312 uses a variable selection
algorithm such as stepwise regression (other types of variable
selection algorithms can be used) to combine the results from the
predictive model bot analyses for each model to determine the best
set of variables for each model. The models having the smallest
amount of error, as measured by applying the root mean squared
error algorithm to the test data, are given preference in
determining the best set of variables. Other error algorithms
including entropy measures may also be used. As a result of this
processing, the best set of variables contain the variables (aka
element, resource and factor data), indicators and composite
variables that correlate most strongly with changes in the measure
being analyzed. The best set of variables will hereinafter be
referred to as the "performance drivers".
[0220] Eliminating low correlation factors from the initial
configuration of the vector creation algorithms increases the
efficiency of the next stage of system processing. Other error
algorithms including entropy measures may be substituted for the
root mean squared error algorithm. After the best set of variables
have been selected, tagged and stored in the relationship layer
table (144) for each entity, the software in block 312 tests the
independence of the performance drivers for each entity before
processing advances to a block 313.
[0221] The software in block 313 checks the bot date table (163)
and deactivates causal predictive model bots with creation dates
before the current system date. The software in block 313 then
retrieves the information from the system settings table (162), the
subject schema table (157), the element layer table (141), the
transaction layer table (142), the resource layer table (143), the
relationship layer table (144) and the environment layer table
(149) in order to initialize causal predictive model bots for each
element, resource and factor in accordance with the frequency
specified by the user (40) in the system settings table (162).
Sub-context elements, resources and factors may be used in the same
manner.
[0222] Bots are independent components of the application software
that complete specific tasks. In the case of causal predictive
model bots, their primary task is to refine the performance driver
selection to reflect only causal variables. A series of causal
predictive model bots are initialized at this stage because it is
impossible to know in advance which causal predictive model will
produce the "best" vector for the best fit variables from each
model. The series for each model includes a number of causal
predictive model bot types: Tetrad, MML, LaGrange, Bayesian,
Probabilistic Relational Model (if allowed), Impact Factor Majority
and path analysis. The Bayesian bots in this step also refine the
estimates of element, resource and/or factor impact developed by
the predictive model bots in a prior processing step by assigning a
probability to the impact estimate. The software in block 313
generates this series of causal predictive model bots for each set
of performance drivers stored in the relationship layer table (144)
in the previous stage in processing. Every causal predictive model
bot activated in this block contains the information shown in Table
30.
TABLE-US-00029 TABLE 30 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Causal
predictive model type 6. Entity type(s) 7. Entity 8. Measure
After the causal predictive model bots are initialized by the
software in block 313, the bots activate in accordance with the
frequency specified by the user (40) in the system settings table
(162). Once activated, they retrieve the specified information for
each model and sub-divide the variables into two sets, one for
training and one for testing. After the causal predictive model
bots complete their processing for each model, the software in
block 313 uses a model selection algorithm to identify the model
that best fits the data. For the system of the present invention, a
cross validation algorithm is used for model selection. The
software in block 313 then saves the refined impact estimates in
the measure layer table (145) and the best fit causal element,
resource and/or factor indicators are identified in the
relationship layer table (144) in the contextbase (50) before
processing returns to software block 301.
[0223] If software in block 310 determines that clustering improves
predictive model accuracy, then processing advances directly to
block 314 as described previously. The software in block 314 uses a
variable selection algorithm such as stepwise regression (other
types of variable selection algorithms can be used) to combine the
results from the predictive model bot analyses for each model,
cluster and/or regime to determine the best set of variables for
each model. The models having the smallest amount of error as
measured by applying the root mean squared error algorithm to the
test data are given preference in determining the best set of
variables. Other error algorithms including entropy measures may
also be used. As a result of this processing, the best set of
variables contains: the element data and factor data that correlate
most strongly with changes in the function measure. The best set of
variables will hereinafter be referred to as the "performance
drivers". Eliminating low correlation factors from the initial
configuration increases the efficiency of the next stage of system
processing. Other error algorithms including entropy measures may
be substituted for the root mean squared error algorithm. After the
best set of variables have been selected, they are tagged as
performance drivers and stored in the relationship layer table
(144), the software in block 314 tests the independence of the
performance drivers before processing advances to a block 315.
[0224] The software in block 315 checks the bot date table (163)
and deactivates causal predictive model bots with creation dates
before the current system date. The software in block 315 then
retrieves the information from the system settings table (162), the
subject schema table (157), the element layer table (141), the
transaction layer table (142), the resource layer table (143), the
relationship layer table (144) and the environment layer table
(149) in order to initialize causal predictive model bots in
accordance with the frequency specified by the user (40) in the
system settings table (162). Bots are independent components of the
application software of the present invention that complete
specific tasks. In the case of causal predictive model bots, their
primary task is to refine the element, resource and factor
performance driver selection to reflect only causal variables.
(Note: these variables are grouped together to represent a single
element vector when they are dependent). In some cases it may be
possible to skip the correlation step before selecting causal item
variables, factor variables, indicators, and composite variables. A
series of causal predictive model bots are initialized at this
stage because it is impossible to know in advance which causal
predictive model will produce the "best" vector for the best fit
variables from each model. The series for each model includes:
Tetrad, LaGrange, Bayesian, Probabilistic Relational Model and path
analysis. The Bayesian bots in this step also refine the estimates
of element or factor impact developed by the predictive model bots
in a prior processing step by assigning a probability to the impact
estimate. The software in block 315 generates this series of causal
predictive model bots for each set of performance drivers stored in
the subject schema table (157) in the previous stage in processing.
Every causal predictive model bot activated in this block contains
the information shown in Table 31.
TABLE-US-00030 TABLE 31 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Type:
cluster, regime, cluster & regime 5. Entity type(s) 6. Entity
7. Measure 8. Causal predictive model type
After the causal predictive model bots are initialized by the
software in block 315, the bots activate in accordance with the
frequency specified by the user (40) in the system settings table
(162). Once activated, they retrieve the specified information for
each model and subdivide the variables into two sets, one for
training and one for testing. The same set of training data are
used by each of the different types of bots for each model. After
the causal predictive model bots complete their processing for each
model, the software in block 315 uses a model selection algorithm
to identify the model that best fits the data for each element,
resource and factor being analyzed by model and/or regime by
entity. For the system of the present invention, a cross validation
algorithm is used for model selection. The software in block 315
saves the refined impact estimates in the measure layer table (145)
and identifies the best fit causal element, resource and/or factor
indicators in the relationship layer table (144) in the contextbase
(50) before processing returns to software block 301.
[0225] When the software in block 301 determines that all measure
models are current, then processing advances to a software block
322. The software in block 322 checks the measure layer table (145)
and the event model table (158) in the contextbase (50) to
determine if all event models are current. If all event models are
current, then processing advances to a software block 332.
Alternatively, if new event models need to be developed, then
processing advances to a software block 325. The software in block
325 retrieves information from the system settings table (162), the
subject schema table (157), the element layer table (141), the
transaction layer table (142), the resource layer table (143), the
relationship layer table (144), the environment layer table (149)
and the event model table (158) in order to complete summaries of
event history and forecasts before processing advances to a
software block 304 where the processing sequence described above
(save for the option bot processing)--is used to identify drivers
for event frequency. After all event frequency models have been
developed they are stored in the event model table (158),
processing advances to a software block 332.
[0226] The software in block 332 checks the measure layer table
(145) and impact model table (166) in the contextbase (50) to
determine if impact models are current for all event risks and
transactions. If all impact models are current, then processing
advances to a software block 341. Alternatively, if new impact
models need to be developed, then processing advances to a software
block 335. The software in block 335 retrieves information from the
system settings table (162), the subject schema table (157), the
element layer table (141), the transaction layer table (142), the
resource layer table (143), the relationship layer table (144), the
environment layer table (149) and the impact model table (166) in
order to complete summaries of impact history and forecasts before
processing advances to a software block 304 where the processing
sequence described above--save for the option bot processing--is
used to identify drivers for event and action impact (or
magnitude). After impact models have been developed for all event
risks and transaction impacts they are stored in the impact model
table (166) and processing advances to a software block 341.
[0227] If a spatial coordinate system is being used, then
processing advances to a block 341 before the processing described
above begins. The software in block 341 checks the subject schema
table (157) in the contextbase (50) to determine if spatial data is
being used. If spatial data is being used, then processing advances
to a software block 342. Alternatively, if all spatial data are not
being used, then processing advances to a software block 370.
[0228] The software in block 342 checks the measure layer table
(145) in the contextbase (50) to determine if there are current
models for all spatial measures for every entity level. If all
measure models are current, then processing advances to a software
block 356. Alternatively, if all spatial measure models are not
current, then processing advances to a software block 303. The
software in block 303 retrieves the previously calculated values
for the measure from the measure layer table (145) before
processing advances to software block 304.
[0229] The software in block 304 checks the bot date table (163)
and deactivates temporal clustering bots with creation dates before
the current system date. The software in block 304 then initializes
bots in accordance with the frequency specified by the user (40) in
the system settings table (162). The bots retrieve information from
the measure layer table (145) for the entity being analyzed and
defines regimes for the measure being analyzed before saving the
resulting cluster information in the relationship layer table (144)
in the contextbase (50). Bots are independent components of the
application software of the present invention that complete
specific tasks. In the case of temporal clustering bots, their
primary task is to segment measure performance into distinct time
regimes that share similar characteristics. The temporal clustering
bot assigns a unique identification (id) number to each "regime" it
identifies before tagging and storing the unique id numbers in the
relationship layer table (144). Every time period with data is
assigned to one of the regimes. The cluster id for each regime is
associated with the measure and entity being analyzed. The time
regimes are developed using a competitive regression algorithm that
identifies an overall, global model before splitting the data and
creating new models for the data in each partition. If the error
from the two models is greater than the error from the global
model, then there is only one regime in the data. Alternatively, if
the two models produce lower error than the global model, then a
third model is created. If the error from three models is lower
than from two models then a fourth model is added. The processing
continues until adding a new model does not improve accuracy. Other
temporal clustering algorithms may be used to the same effect.
Every temporal clustering bot contains the information shown in
Table 32.
TABLE-US-00031 TABLE 32 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Maximum
number of clusters 6. Entity type(s) 7. Entity 8. Measure
When bots in block 304 have identified and stored regime
assignments for all time periods with measure data for the current
entity, processing advances to a software block 305.
[0230] The software in block 305 checks the bot date table (163)
and deactivates variable clustering bots with creation dates before
the current system date. The software in block 305 then initializes
bots in order for each context element, resource and factor for the
current entity level. The bots activate in accordance with the
frequency specified by the user (40) in the system settings table
(162), retrieve the information from the element layer table (141),
the transaction layer table (142), the resource layer table (143),
the environment layer table (149) and the subject schema table
(157) and define segments for context element, resource and factor
data before tagging and saving the resulting cluster information in
the relationship layer table (144). Bots are independent components
of the application software of the present invention that complete
specific tasks. In the case of variable clustering bots, their
primary task is to segment the element, resource and factor
data--including indicators--into distinct clusters that share
similar characteristics. The clustering bot assigns a unique id
number to each "cluster" it identifies, tags and stores the unique
id numbers in the relationship layer table (144). Every variable
for every context element, resource and factor is assigned to one
of the unique clusters. The element data, resource data and factor
data are segmented into a number of clusters less than or equal to
the maximum specified by the user (40) in the system settings table
(162). The data are segmented using several clustering algorithms
including: an unsupervised "Kohonen" neural network, decision tree,
support vector method, K-nearest neighbor, expectation maximization
(EM) and the segmental K-means algorithm. For algorithms that
normally have the number of clusters specified by a user, the bot
will use the maximum number of clusters specified by the user (40).
Every variable clustering bot contains the information shown in
Table 33.
TABLE-US-00032 TABLE 33 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Context
component 6. Clustering algorithm 7. Entity type(s) 8. Entity 9.
Measure 10. Maximum number of clusters 11. Variable 1 . . . to 11 +
n. Variable n
When bots in block 305 have identified, tagged and stored cluster
assignments for the data associated with every element, resource
and factor in the relationship layer table (144), processing
advances to a software block 343.
[0231] The software in block 343 checks the bot date table (163)
and deactivates spatial clustering bots with creation dates before
the current system date. The software in block 343 then retrieves
the information from the system settings table (162), the subject
schema table (157), the element layer table (141), the transaction
layer table (142), the resource layer table (143), the relationship
layer table (144), the environment layer table (149), the reference
layer table (154) and the scenarios table (168) in order to
initialize spatial clustering bots in accordance with the frequency
specified by the user (40) in the system settings table (162). Bots
are independent components of the application software that
complete specific tasks. In the case of spatial clustering bots,
their primary task is to segment the element, resource and factor
data--including performance indicators--into distinct clusters that
share similar characteristics. The clustering bot assigns a unique
id number to each "cluster" it identifies, tags and stores the
unique id numbers in the relationship layer table (144). Data for
each context element, resource and factor are assigned to one of
the unique clusters. The element, resource and factor data are
segmented into a number of clusters less than or equal to the
maximum specified by the user (40) in the system settings table
(162). The system of the present invention uses several spatial
clustering algorithms including: hierarchical clustering, cluster
detection, k-ary clustering, variance to mean ratio, lacunarity
analysis, pair correlation, join correlation, mark correlation,
fractal dimension, wavelet, nearest neighbor, local index of
spatial association (LISA), spatial analysis by distance indices
(SADIE), mantel test and circumcircle. Every spatial clustering bot
activated in this block contains the information shown in Table
34.
TABLE-US-00033 TABLE 34 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Context
component 6. Clustering algorithm 7. Entity type(s) 8. Entity 9.
Measure 10. Maximum number of clusters 11. Variable 1 . . . to 11 +
n. Variable n
When bots in block 343 have identified, tagged and stored cluster
assignments for the data associated with every element, resource
and factor in the relationship layer table (144), processing
advances to a software block 307.
[0232] The software in block 307 checks the measure layer table
(145) in the contextbase (50) to see if the current measure is an
options based measure like contingent liabilities, real options or
competitor risk. If the current measure is not an options based
measure, then processing advances to a software block 344.
Alternatively, if the current measure is an options based measure,
then processing advances to a software block 308.
[0233] The software in block 308 checks the bot date table (163)
and deactivates option bots with creation dates before the current
system date. The software in block 308 then retrieves the
information from the system settings table (162), the subject
schema table (157), the element layer table (141), the transaction
layer table (142), the resource layer table (143), the relationship
layer table (144), the environment layer table (149), the reference
layer table (154) and the scenarios table (168) in order to
initialize option bots in accordance with the frequency specified
by the user (40) in the system settings table (162).
[0234] Bots are independent components of the application software
of the present invention that complete specific tasks. In the case
of option bots, their primary task is to determine the impact of
each element, resource and factor on the entity option measure
under different scenarios. The option simulation bots run a normal
scenario, an extreme scenario and a combined scenario with and
without clusters. In one embodiment, Monte Carlo models are used to
complete the probabilistic simulation. However, other option models
including binomial models, multinomial models and dynamic
programming can be used to the same effect. The element, resource
and factor impacts on option measures could be determined using the
processed detailed below for the other types of measures, however,
in this embodiment a separate procedure is used. The models are
initialized with specifications used in the baseline calculations.
Every option bot activated in this block contains the information
shown in Table 35.
TABLE-US-00034 TABLE 35 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Scenario:
normal, extreme or combined 6. Option type: real option, contingent
liability or competitor risk 7. Entity type(s) 8. Entity 9. Measure
10. Clustered data? (Yes or No) 11. Algorithm
After the option bots are initialized, they activate in accordance
with the frequency specified by the user (40) in the system
settings table (162). Once activated, the bots retrieve the
specified information and simulate the measure over the time
periods specified by the user (40) in the system settings table
(162) in order to determine the impact of each element, resource
and factor on the option. After the option bots complete their
calculations, the impacts and sensitivities for the option
(clustered data--yes or no) that produced the best result under
each scenario are saved in the measure layer table (145) in the
contextbase (50) and processing returns to software block 341.
[0235] If the current measure was not an option measure, then
processing advanced to software block 344. The software in block
309 checks the bot date table (163) and deactivates all predictive
model bots with creation dates before the current system date. The
software in block 344 then retrieves the information from the
system settings table (162), the subject schema table (157) and the
element layer table (141), the transaction layer table (142), the
resource layer table (143), the relationship layer table (144), the
environment layer table (149) and the reference layer (154) in
order to initialize predictive model bots for the measure being
evaluated.
[0236] Bots are independent components of the application software
that complete specific tasks. In the case of predictive model bots,
their primary task is to determine the relationship between the
indicators and the measure being evaluated. Predictive model bots
are initialized for each cluster and/or regime of data in
accordance with the cluster and/or regime assignments specified by
the bots in blocks 304, 305 and 343. A series of predictive model
bots is initialized at this stage because it is impossible to know
in advance which predictive model type will produce the "best"
predictive model for the data from each entity. The series for each
model includes: neural network, CART, GARCH, projection pursuit
regression, stepwise regression, logistic regression, probit
regression, factor analysis, growth modeling, linear regression,
redundant regression network, boosted naive bayes regression,
support vector method, markov models, rough-set analysis, kriging,
simulated annealing, latent class models, gaussian mixture models,
triangulated probability and kernel estimation. Each model includes
spatial autocorrelation indicators as performance indicators. Other
types predictive models can be used to the same effect. Every
predictive model bot contains the information shown in Table
36.
TABLE-US-00035 TABLE 36 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Entity
type(s) 6. Entity 7. Measure 8. Type: variable (y or n), spatial (y
or n), spatial-temporal (y or n) 9. Predictive model type
After predictive model bots are initialized, the bots activate in
accordance with the frequency specified by the user (40) in the
system settings table (162). Once activated, the bots retrieve the
specified data from the appropriate table in the contextbase (50)
and randomly partition the element, resource and/or factor data
into a training set and a test set. The software in block 344 uses
"bootstrapping" where the different training data sets are created
by re-sampling with replacement from the original training set so
data records may occur more than once. Training with genetic
algorithms can also be used. After the predictive model bots
complete their training and testing, the best fit predictive model
assessments of element, resource and factor impacts on measure
performance are saved in the measure layer table (145) before
processing advances to a block 345.
[0237] The software in block 345 determines if clustering improved
the accuracy of the predictive models generated by the bots in
software block 344. The software in block 345 uses a variable
selection algorithm such as stepwise regression (other types of
variable selection algorithms can be used) to combine the results
from the predictive model bot analyses for each type of
analysis--with and without clustering--to determine the best set of
variables for each type of analysis. The type of analysis having
the smallest amount of error as measured by applying the root mean
squared error algorithm to the test data are given preference in
determining the best set of variables for use in later analysis.
Other error algorithms including entropy measures may also be used.
There are eight possible outcomes from this analysis as shown in
Table 37.
TABLE-US-00036 TABLE 37 1. Best model has no clustering 2. Best
model has temporal clustering, no variable clustering, no spatial
clustering 3. Best model has variable clustering, no temporal
clustering, no spatial clustering 4. Best model has temporal
clustering, variable clustering, no spatial clustering 5. Best
model has no temporal clustering, no variable clustering, spatial
clustering 6. Best model has temporal clustering, no variable
clustering, spatial clustering 7. Best model has variable
clustering, no temporal clustering, spatial clustering 8. Best
model has temporal clustering, variable clustering, spatial
clustering
If the software in block 345 determines that clustering improves
the accuracy of the predictive models for an entity, then
processing advances to a software block 348. Alternatively, if
clustering does not improve the overall accuracy of the predictive
models for an entity, then processing advances to a software block
346.
[0238] The software in block 346 uses a variable selection
algorithm such as stepwise regression (other types of variable
selection algorithms can be used) to combine the results from the
predictive model bot analyses for each model to determine the best
set of variables for each model. The models having the smallest
amount of error, as measured by applying the root mean squared
error algorithm to the test data, are given preference in
determining the best set of variables. Other error algorithms
including entropy measures may also be used. As a result of this
processing, the best set of variables contain the variables (aka
element, resource and factor data), indicators, and composite
variables that correlate most strongly with changes in the measure
being analyzed. The best set of variables will hereinafter be
referred to as the "performance drivers".
[0239] Eliminating low correlation factors from the initial
configuration of the vector creation algorithms increases the
efficiency of the next stage of system processing. Other error
algorithms including entropy measures may be substituted for the
root mean squared error algorithm. After the best set of variables
have been selected, tagged and stored in the relationship layer
table (144) for each entity level, the software in block 346 tests
the independence of the performance drivers for each entity level
before processing advances to a block 347.
[0240] The software in block 347 checks the bot date table (163)
and deactivates causal predictive model bots with creation dates
before the current system date. The software in block 347 then
retrieves the information from the system settings table (162), the
subject schema table (157), the element layer table (141), the
transaction layer table (142), the resource layer table (143), the
relationship layer table (144) and the environment layer table
(149) in order to initialize causal predictive model bots for each
element, resource and factor in accordance with the frequency
specified by the user (40) in the system settings table (162).
Sub-context elements, resources and factors may be used in the same
manner.
[0241] Bots are independent components of the application software
that complete specific tasks. In the case of causal predictive
model bots, their primary task is to refine the performance driver
selection to reflect only causal variables. A series of causal
predictive model bots are initialized at this stage because it is
impossible to know in advance which causal predictive model will
produce the "best" fit for variables from each model. The series
for each model includes six causal predictive model bot types:
kriging, latent class models, gaussian mixture models, kernel
estimation and Markov-Bayes. The software in block 347 generates
this series of causal predictive model bots for each set of
performance drivers stored in the relationship layer table (144) in
the previous stage in processing. Every causal predictive model bot
activated in this block contains the information shown in Table
38.
TABLE-US-00037 TABLE 38 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Causal
predictive model type 6. Entity type(s) 7. Entity 8. Measure
After the causal predictive model bots are initialized by the
software in block 347, the bots activate in accordance with the
frequency specified by the user (40) in the system settings table
(162). Once activated, they retrieve the specified information for
each model and sub-divide the variables into two sets, one for
training and one for testing. After the causal predictive model
bots complete their processing for each model, the software in
block 347 uses a model selection algorithm to identify the model
that best fits the data. For the system of the present invention, a
cross validation algorithm is used for model selection. The
software in block 347 then saves the refined impact estimates in
the measure layer table (145) and the best fit causal element,
resource and/or factor indicators are identified in the
relationship layer table (144) in the contextbase (50) before
processing returns to software block 342.
[0242] If software in block 345 determines that clustering improves
predictive model accuracy, then processing advances directly to
block 348 as described previously. The software in block 348 uses a
variable selection algorithm such as stepwise regression (other
types of variable selection algorithms can be used) to combine the
results from the predictive model bot analyses for each model,
cluster and/or regime to determine the best set of variables for
each model. The models having the smallest amount of error as
measured by applying the root mean squared error algorithm to the
test data are given preference in determining the best set of
variables. Other error algorithms including entropy measures can
also be used. As a result of this processing, the best set of
variables contains the element data, resource data and factor data
that correlate most strongly with changes in the function and/or
mission measures. The best set of variables will hereinafter be
referred to as the "performance drivers". Eliminating low
correlation factors from the initial configuration of the vector
creation algorithms increases the efficiency of the next stage of
system processing. Other error algorithms including entropy
measures may be substituted for the root mean squared error
algorithm. After the best set of variables have been selected, they
are tagged as performance drivers and stored in the relationship
layer table (144), the software in block 348 tests the independence
of the performance drivers before processing advances to a block
349.
[0243] The software in block 349 checks the bot date table (163)
and deactivates causal predictive model bots with creation dates
before the current system date. The software in block 349 then
retrieves the information from the system settings table (162), the
subject schema table (157), the element layer table (141), the
transaction layer table (142), the resource layer table (143), the
relationship layer table (144) and the environment layer table
(149) in order to initialize causal predictive model bots in
accordance with the frequency specified by the user (40) in the
system settings table (162). Bots are independent components of the
application software of the present invention that complete
specific tasks. In the case of causal predictive model bots, their
primary task is to refine the element, resource and factor
performance driver selection to reflect only causal variables.
(Note: these variables are grouped together to represent a single
vector when they are dependent). In some cases it may be possible
to skip the correlation step before selecting causal the item
variables, factor variables, indicators and composite variables. A
series of causal predictive model bots are initialized at this
stage because it is impossible to know in advance which causal
predictive model will produce the "best" fit variables for each
measure. The series for each measure includes six causal predictive
model bot types: kriging, latent class models, gaussian mixture
models, kernel estimation and Markov-Bayes. The software in block
349 generates this series of causal predictive model bots for each
set of performance drivers stored in the subject schema table (157)
in the previous stage in processing. Every causal predictive model
bot activated in this block contains the information shown in Table
39.
TABLE-US-00038 TABLE 39 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Type:
cluster, regime, cluster & regime 6. Entity type(s) 7. Entity
8. Measure 9. Causal predictive model type
After the causal predictive model bots are initialized by the
software in block 349, the bots activate in accordance with the
frequency specified by the user (40) in the system settings table
(162). Once activated, they retrieve the specified information for
each model and sub-divide the variables into two sets, one for
training and one for testing. The same set of training data is used
by each of the different types of bots for each model. After the
causal predictive model bots complete their processing for each
model, the software in block 349 uses a model selection algorithm
to identify the model that best fits the data for each process,
element, resource and/or factor being analyzed by model and/or
regime by entity. For the system of the present invention, a cross
validation algorithm is used for model selection. The software in
block 349 saves the refined impact estimates in the measure layer
table (145) and identifies the best fit causal element, resource
and/or factor indicators in the relationship layer table (144) in
the contextbase (50) before processing returns to software block
342.
[0244] When the software in block 342 determines that all spatial
measure models are current processing advances to a software block
356. The software in block 356 checks the measure layer table (145)
and the event model table (158) in the contextbase (50) to
determine if all event models are current. If all event models are
current, then processing advances to a software block 361.
Alternatively, if new event models need to be developed, then
processing advances to a software block 325. The software in block
325 retrieves information from the system settings table (162), the
subject schema table (157), the element layer table (141), the
transaction layer table (142), the resource layer table (143), the
relationship layer table (144), the environment layer table (149),
the reference layer table (154) and the event model table (158) in
order to complete summaries of event history and forecasts before
processing advances to a software block 304 where the processing
sequence described above--save for the option bot processing--is
used to identify drivers for event risk and transaction frequency.
After all event frequency models have been developed they are
stored in the event model table (158) and processing advances to
software block 361.
[0245] The software in block 361 checks the measure layer table
(145) and impact model table (166) in the contextbase (50) to
determine if impact models are current for all event risks and
actions. If all impact models are current, then processing advances
to a software block 370. Alternatively, if new impact models need
to be developed, then processing advances to a software block 335.
The software in block 335 retrieves information from the system
settings table (162), the subject schema table (157), the element
layer table (141), the transaction layer table (142), the resource
layer table (143), the relationship layer table (144), the
environment layer table (149)), the reference layer table (154) and
the impact model table (166) in order to complete summaries of
impact history and forecasts before processing advances to a
software block 305 where the processing sequence described
above--save for the option bot processing--is used to identify
drivers for event risk and transaction impact (or magnitude). After
impact models have been developed for all event risks and action
impacts they are stored in the impact model table (166) and
processing advances to a software block 370 via software block
361.
[0246] The software in block 370 determines if adding spatial data
improves the accuracy of the predictive models. The software in
block 370 uses a variable selection algorithm such as stepwise
regression (other types of variable selection algorithms can be
used) to combine the results from each type of prior analysis--with
and without spatial data--to determine the best set of variables
for each type of analysis. The type of analysis having the smallest
amount of error as measured by applying the root mean squared error
algorithm to the test data are used for subsequent later analysis.
Other error algorithms including entropy measures may also be used.
There are eight possible outcomes from this analysis as shown in
Table 40.
TABLE-US-00039 TABLE 40 1. Best measure, event and impact models
are spatial 2. Best measure and event models are spatial, best
impact model is not spatial 3. Best measure and impact models are
spatial, best event model is not spatial 5. Best measure models are
spatial, best event and impact models are not spatial 5. Best
measure models are not spatial, best event and impact models are
spatial 6. Best measure and impact models are not spatial, best
event model is spatial 7. Best measure and event models are not
spatial, best impact model is spatial 8. Best measure, event and
impact models are not spatial
The best set of models identified by the software in block 370 are
tagged for use in subsequent processing before processing advances
to a software block 371.
[0247] The software in block 371 checks the measure layer table
(145) in the contextbase (50) to determine if probabilistic
relational models were used in measure impacts. If probabilistic
relational models were used, then processing advances to a software
block 377. Alternatively, if probabilistic relational models were
not used, then processing advances to a software block 372.
[0248] The software in block 372 tests the performance drivers to
see if there is interaction between elements, factors and/or
resources by entity. The software in this block identifies
interaction by evaluating a chosen model based on stochastic-driven
pairs of value-driver subsets. If the accuracy of such a model is
higher that the accuracy of statistically combined models trained
on attribute subsets, then the attributes from subsets are
considered to be interacting and then they form an interacting set.
Other tests of driver interaction can be used to the same effect.
The software in block 372 also tests the performance drivers to see
if there are "missing" performance drivers that are influencing the
results. If the software in block 372 does not detect any
performance driver interaction or missing variables for each
entity, then system processing advances to a block 376.
Alternatively, if missing data or performance driver interactions
across elements, factors and/resources are detected by the software
in block 372 for one or more measures, processing advances to a
software block 373.
[0249] The software in block 373 evaluates the interaction between
performance drivers in order to classify the performance driver
set. The performance driver set generally matches one of the six
patterns of interaction: a multi-component loop, a feed forward
loop, a single input driver, a multi-input driver, auto-regulation
or a chain. After classifying each performance driver set the
software in block 373 prompts the user (40) via the structure
revision window (706) to accept the classification and continue
processing, establish probabilistic relational models as the
primary causal model and/or adjust the specification(s) for the
context elements and factors in some other way in order to minimize
or eliminate interaction that was identified. For example, the user
(40) can also choose to re-assign a performance driver to a new
context element or factor to eliminate an identified
inter-dependency. After the optional input from the user (40) is
saved in the element layer table (141), the environment layer table
(149) and the system settings table (162), processing advances to a
software block 374. The software in block 374 checks the element
layer table (141), the environment layer table (149) and system
settings table (162) to see if there are any changes in structure.
If there have been changes in the structure, then processing
returns to block 201 and the system processing described previously
is repeated. Alternatively, if there are no changes in structure,
then the information regarding the element interaction is saved in
the relationship layer table (144) before processing advances to a
block 376.
[0250] The software in block 376 checks the bot date table (163)
and deactivates vector generation bots with creation dates before
the current system date. The software in block 376 then initializes
vector generation bots for each context element, sub-context
element, element combination, factor combination, context factor
and sub-context factor. The bots activate in accordance with the
frequency specified by the user (40) in the system settings table
(162) and retrieve information from the element layer table (141),
the transaction layer table (142), the resource layer table (143),
the relationship layer table (144) and the environment layer table
(149). Bots are independent components of the application software
that complete specific tasks. In the case of vector generation
bots, their primary task is to produce vectors that summarize the
relationship between the causal performance drivers and changes in
the measure being examined. The vector generation bots use
induction algorithms to generate the vectors. Other vector
generation algorithms can be used to the same effect. Every vector
generation bot contains the information shown in Table 41.
TABLE-US-00040 TABLE 41 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Hierarchy or
group 6. Entity 7. Measure 8. Context component or combination 9.
Factor 1 . . . to 9 + n. Factor n
When bots in block 376 have created and stored vectors for all time
periods with data for all the elements, sub-elements, factors,
sub-factors, resources, sub-resources and combinations that have
vectors in the subject schema table (157) by entity, processing
advances to a software block 377.
[0251] The software in block 377 checks the bot date table (163)
and deactivates life bots with creation dates before the current
system date. The software in block 377 then retrieves the
information from the system settings table (162), the element layer
table (141), the transaction layer table (142), the resource layer
table (143), the relationship layer table (144) and the environment
layer table (149) in order to initialize life bots for each element
and factor. Bots are independent components of the application
software that complete specific tasks. In the case of life bots,
their primary task is to determine the expected life of each
element, resource and factor. There are three methods for
evaluating the expected life: [0252] 1. Elements, resources and
factors that are defined by a population of members or items (such
as: channel partners, customers, employees and vendors) will have
their lives estimated by forecasting the lives of members of the
population and then integrating the results into an overall
population density matrix. The forecast of member lives will be
determined by the "best" fit solution from competing life
estimation methods including the Iowa type survivor curves, Weibull
distribution survivor curves, growth models, Gompertz-Makeham
survivor curves, Bayesian population matrix estimation and
polynomial equations using the tournament method for selecting from
competing forecasts; [0253] 2. Elements, resources and factors
(such as patents, long term supply agreements, certain laws and
insurance contracts) that have legally defined lives will have
their lives calculated using the time period between the current
date and the expiration date of their defined life; and [0254] 3.
Finally, elements, resources and factors that do not have defined
lives will have their lives estimated to equal the forecast time
period.
[0255] Every element life bot contains the information shown in
Table 42.
TABLE-US-00041 TABLE 42 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Hierarchy or
group 6. Entity 7. Measure 8. Context component or combination 9.
Life estimation method (item analysis, defined or forecast
period)
After the life bots are initialized, they are activated in
accordance with the frequency specified by the user (40) in the
system settings table (162). After being activated, the bots
retrieve information for each element and sub-context element from
the contextbase (50) in order to complete the estimate of element
life. The resulting values are then tagged and stored in the
element layer table (141), the resource layer table (143) or the
environment layer table (149) in the contextbase (50) before
processing advances to a block 379.
[0256] The software in block 379 checks the bot date table (163)
and deactivates dynamic relationship bots with creation dates
before the current system date. The software in block 379 then
retrieves the information from the system settings table (162), the
element layer table (141), the transaction layer table (142), the
resource layer table (143), the relationship layer table (144), the
environment layer table (149) and the event risk table (156) in
order to initialize dynamic relationship bots for the measure. Bots
are independent components of the application software that
complete specific tasks. In the case of dynamic relationship bots,
their primary task is to identify the best fit dynamic model of the
interrelationship between the different elements, factors,
resources and events that are driving measure performance. The best
fit model is selected from a group of potential linear models and
non-linear models including swarm models, complexity models,
maximal time step models, simple regression models, power law
models and fractal models. Every dynamic relationship bot contains
the information shown in Table 43.
TABLE-US-00042 TABLE 43 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Hierarchy or
group 6. Entity 7. Measure 8. Algorithm
The bots in block 379 identify the best fit model of the dynamic
interrelationship between the elements, factors, resources and
risks for the reviewed measure and store information regarding the
best fit model in the relationship layer table (144) before
processing advances to a software block 380.
[0257] The software in block 380 checks the bot date table (163)
and deactivates partition bots with creation dates before the
current system date. The software in the block then retrieves the
information from the system settings table (162), the element layer
table (141), the transaction layer table (142), the resource layer
table (143), the relationship layer table (144), the measure layer
table (145), the environment layer table (149), the event risk
table (156) and the scenarios table (168) to initialize partition
bots in accordance with the frequency specified by the user (40) in
the system settings table (162). Bots are independent components of
the application software of the present invention that complete
specific tasks. In the case of partition bots, their primary task
is to use the historical and forecast data to segment the
performance measure contribution of each element, factor, resource,
combination and performance driver into a base value and a
variability or risk component. The system of the present invention
uses wavelet algorithms to segment the performance contribution
into two components although other segmentation algorithms such as
GARCH could be used to the same effect. Every partition bot
contains the information shown in Table 44.
TABLE-US-00043 TABLE 44 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Hierarchy or
group 6. Entity 7. Measure 8. Context component or combination 9.
Segmentation algorithm
After the partition bots are initialized, the bots activate in
accordance with the frequency specified by the user (40) in the
system settings table (162). After being activated the bots
retrieve data from the contextbase (50) and then segment the
performance contribution of each element, factor, resource or
combination into two segments. The resulting values by period for
each entity are then stored in the measure layer table (145),
before processing advances to a software block 382.
[0258] The software in block 382 retrieves the information from the
event model table (158) and the impact model table (166) and
combines the information from both tables in order to update the
event risk estimate for the entity. The resulting values by period
for each entity are then stored in the event risk table (156),
before processing advances to a software block 389.
[0259] The software in block 389 checks the bot date table (163)
and deactivates simulation bots with creation dates before the
current system date. The software in block 389 then retrieves the
information from the relationship layer table (144), the measure
layer table (145), the event risk table (156), the subject schema
table (157), the system settings table (162) and the scenarios
table (168) in order to initialize simulation bots in accordance
with the frequency specified by the user (40) in the system
settings table (162).
[0260] Bots are independent components of the application software
that complete specific tasks. In the case of simulation bots, their
primary task is to run three different types of simulations of
subject measure performance. The simulation bots run probabilistic
simulations of measure performance using the normal scenario, the
extreme scenario and the blended scenario. They also run an
unconstrained genetic algorithm simulation that evolves to the most
negative value possible over the specified time period. In one
embodiment, Monte Carlo models are used to complete the
probabilistic simulation, however other probabilistic simulation
models such as Quasi Monte Carlo, genetic algorithm and Markov
Chain Monte Carlo can be used to the same effect. The models are
initialized using the statistics and relationships derived from the
calculations completed in the prior stages of processing to relate
measure performance to the performance driver, element, factor,
resource and event risk scenarios. Every simulation bot activated
in this block contains the information shown in Table 46.
TABLE-US-00044 TABLE 46 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Type: normal,
extreme, blended or genetic algorithm 6. Measure 7. Hierarchy or
group 8. Entity
After the simulation bots are initialized, they activate in
accordance with the frequency specified by the user (40) in the
system settings table (162). Once activated, they retrieve the
specified information and simulate measure performance by entity
over the time periods specified by the user (40) in the system
settings table (162). In doing so, the bots will forecast the range
of performance and risk that can be expected for the specified
measure by entity within the confidence interval defined by the
user (40) in the system settings table (162) for each scenario. The
bots also create a summary of the overall risks facing the entity
for the current measure. After the simulation bots complete their
calculations, the resulting forecasts are saved in the scenarios
table (168) by entity and the risk summary is saved in the report
table (153) in the contextbase (50) before processing advances to a
software block 390.
[0261] The software in block 390 checks the measure layer table
(145) and the system settings table (162) in the contextbase (50)
to see if probabilistic relational models were used. If
probabilistic relational models were used, then processing advances
to a software block 398. Alternatively, if the current calculations
did not rely on probabilistic relational models, then processing
advances to a software block 391.
[0262] The software in block 391 checks the bot date table (163)
and deactivates measure bots with creation dates before the current
system date. The software in block 391 then retrieves the
information from the system settings table (162), the measure layer
table (145) and the subject schema table (157) in order to
initialize bots for each context element, context factor, context
resource, combination or performance driver for the measure being
analyzed. Bots are independent components of the application
software of the present invention that complete specific tasks. In
the case of measure bots, their task is to determine the net
contribution of the network of elements, factors, resources,
events, combinations and performance drivers to the measure being
analyzed. The relative contribution of each element, factor,
resource, combination and performance driver is determined by using
a series of predictive models to find the best fit relationship
between the context element vectors, context factor vectors,
combination vectors and performance drivers and the measure. The
system of the present invention uses different types of predictive
models to identify the best fit relationship: neural network, CART,
projection pursuit regression, generalized additive model (GAM),
GARCH, MMDR, MARS, redundant regression network, ODE, boosted Naive
Bayes Regression, relevance vector, hierarchical Bayes, Gillespie
algorithm models, the support vector method, markov, linear
regression, and stepwise regression. The model having the smallest
amount of error as measured by applying the root mean squared error
algorithm to the test data are the best fit model. Other error
algorithms and/or uncertainty measures including entropy measures
may also be used. The "relative contribution algorithm" used for
completing the analysis varies with the model that was selected as
the "best-fit". For example, if the "best-fit" model is a neural
net model, then the portion of the measure attributable to each
input vector is determined by the formula shown in Table 47.
TABLE-US-00045 TABLE 47 ( Sum k = 1 k = m Sum j = 1 j = n I jk
.times. O k / Sum j = 1 j = n I ik ) / Sum k = 1 k = m ( Sum j = 1
j = n I jk .times. O k ) ##EQU00001## Where I.sub.jk = Absolute
value of the input weight from input node j to hidden node k
O.sub.k = Absolute value of output weight from hidden node k M =
number of hidden nodes N = number of input nodes
After completing the best fit calculations, the bots review the
lives of the context elements that impact measure performance. If
one or more of the elements has an expected life that is shorter
than the forecast time period stored in the system settings table
(162), then a separate model will be developed to reflect the
removal of the impact from the element(s) that are expiring. The
resulting values for relative component of context contributions to
measure performance are then calculated and saved in the subject
schema table (157). If the calculations are related to a commercial
business then the value of each contribution will also be saved.
The overall model of measure performance is saved in the measure
layer table (145). Every measure bot contains the information shown
in Table 48.
TABLE-US-00046 TABLE 48 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Hierarchy or
group 6. Entity 7. Measure 8. Context component or combination
After the measure bots are initialized by the software in block 391
they activate in accordance with the frequency specified by the
user (40) in the system settings table (162). After being
activated, the bots retrieve information and complete the analysis
of the measure performance. As described previously, the resulting
relative contribution percentages are saved in the subject schema
table (157) by entity. The overall model of measure performance is
saved in the measure layer table (145) by entity before processing
advances to a software block 392.
[0263] The software in block 392 checks the measure layer table
(145) in the contextbase (50) to determine if all subject measures
are current. If all measures are not current, then processing
returns to software block 302 and the processing described above
for this portion (300) of the application software is repeated.
Alternatively, if all measure models are current, then processing
advances to a software block 394.
[0264] The software in block 394 retrieves the previously stored
values for measure performance from the measure layer table (145)
before processing advances to a software block 395. The software in
block 395 checks the bot date table (163) and deactivates measure
relevance bots with creation dates before the current system date.
The software in block 395 then retrieves the information from the
system settings table (162) and the measure layer table (145) in
order to initialize a bot for each entity being analyzed. bots are
independent components of the application software of the present
invention that complete specific tasks. In the case of measure
relevance bots, their tasks are to determine the relevance of each
of the different measures to entity performance and determine the
priority that appears to be placed on each of the different
measures is there is more than one. The relevance and ranking of
each measure is determined by using a series of predictive models
to find the best fit relationship between the measures and entity
performance. The system of the present invention uses several
different types of predictive models to identify the best fit
relationship: neural network, CART, projection pursuit regression,
generalized additive model (GAM), GARCH, MMDR, redundant regression
network, markov, ODE, boosted naive Bayes Regression, the relevance
vector method, the support vector method, linear regression, and
stepwise regression. The model having the smallest amount of error
as measured by applying the root mean squared error algorithm to
the test data are the best fit model. Other error algorithms
including entropy measures may also be used. Bayes models are used
to define the probability associated with each relevance measure
and the Viterbi algorithm is used to identify the most likely
contribution of all elements, factors, resources, projects, events,
and risks by entity. The relative contributions are saved in the
measure layer table (145) by entity. Every measure relevance bot
contains the information shown in Table 49.
TABLE-US-00047 TABLE 49 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Hierarchy or
group 6. Entity 7. Measure
After the measure relevance bots are initialized by the software in
block 395 they activate in accordance with the frequency specified
by the user (40) in the system settings table (162). After being
activated, the bots retrieve information and complete the analysis
of the measure performance. As described previously, the relative
measure contributions to measure performance and the associated
probability are saved in the measure layer table (145) by entity
before processing advances to a software block 396.
[0265] The software in block 396 retrieves information from the
measure table (145) and then checks the measures for the entity
hierarchy to determine if the different levels are in alignment. As
discussed previously, lower level measures that are out of
alignment can be identified by the presence of measures from the
same level with more impact on subject measure performance. For
example, employee training could be shown to be a strong
performance driver for the entity. If the human resources
department (that is responsible for both training and performance
evaluations) had been using only a timely performance evaluation
measure, then the measures would be out of alignment. If measures
are out of alignment, then the software in block 396 prompts the
manager (41) via the measure edit data window (708) to change the
measures by entity in order to bring them into alignment.
Alternatively, if measures by entity are in alignment, then
processing advances to a software block 397.
[0266] The software in block 397 checks the bot date table (163)
and deactivates frontier bots with creation dates before the
current system date. The software in block 397 then retrieves
information from the event risk table (156), the system settings
table (162) and the scenarios table (168) in order to initialize
frontier bots for each scenario. Bots are independent components of
the application software of the present invention that complete
specific tasks. In the case of frontier bots, their primary task is
to define the efficient frontier for entity performance measures
under each scenario. The top leg of the efficient frontier for each
scenario is defined by successively adding the features, options
and performance drivers that improve performance while decreasing
risk to the optimal mix in resource efficiency order. The bottom
leg of the efficient frontier for each scenario is defined by
successively adding the features, options and performance drivers
that decrease performance while decreasing risk to the optimal mix
in resource efficiency order. Every frontier bot contains the
information shown in Table 50.
TABLE-US-00048 TABLE 50 1. Unique ID number (based on date, hour,
minute, second of creation) 2. Creation date (date, hour, minute,
second) 3. Mapping information 4. Storage location 5. Entity 6.
Scenario: normal, extreme and blended
After the software in block 397 initializes the frontier bots, they
activate in accordance with the frequency specified by the user
(40) in the system settings table (162). After completing their
calculations, the results of all three sets of calculations
(normal, extreme and most likely) are saved in the report table
(153) in sufficient detail to generate a chart like the one shown
in FIG. 12 before processing advances to a software block 398.
[0267] The software in block 398 takes the previously stored entity
schema from the subject schema table (157) and combines it with the
relationship information in the relationship layer table (144) and
the measure layer table (145) to develop the entity ontology. The
ontology is then stored in the ontology table (152) using the OWL
language. Use of the rdf (resource description framework) based OWL
language will enable the communication and synchronization of the
entities ontology with other entities and will facilitate the
extraction and use of information from the semantic web. The
semantic web rule language (swrl) that combines OWL with Rule ML
can also be used to store the ontology. After the relevant entity
ontology is saved in the contextbase (50), processing advances to a
software block 402.
Complete Context Service Propagation
[0268] The flow diagrams in FIG. 8A and FIG. 8B detail the
processing that is completed by the portion of the application
software (400) that identifies valid context space, identifies
principles, integrates the different entity contexts into an
overall context, propagates a Complete Context.TM. Service and
optionally displays and prints management reports detailing the
measure performance of an entity. Processing in this portion of the
application software (400) starts in software block 402.
[0269] The software in block 402 calculates expected uncertainty by
multiplying the user (40) and subject matter expert (42) estimates
of narrow system (4) uncertainty by the relative importance of the
data from the narrow system for each function measure. The expected
uncertainty for each measure is expected to be lower than the
actual uncertainty (measured using R.sup.2 as discussed previously)
because total uncertainty is a function of data uncertainty plus
parameter uncertainty (i.e. are the specified elements, resources
and factors the correct ones) and model uncertainty (does the model
accurately reflect the relationship between the data and the
measure). After saving the uncertainty information in the
uncertainty table (150) processing advances to a software block
403.
[0270] The software in block 403 retrieves information from the
relationship layer table (144), the measure layer table (145) and
the context frame table (160) in order to define the valid context
space for the current relationships and measures stored in the
contextbase (50). The current measures and relationships are
compared to previously stored context frames to determine the range
of contexts in which they are valid with the confidence interval
specified by the user (40) in the system settings table (162). The
resulting list of valid frame definitions stored in the context
space table (151). The software in this block also completes a
stepwise elimination of each user specified constraint. This
analysis helps determine the sensitivity of the results and may
indicate that it would be desirable to use some resources to relax
one or more of the established constraints. The results of this
analysis are stored in the context space table (151) before
processing advances to a software block 410.
[0271] The software in block 410 integrates the one or more entity
contexts into an overall entity context using the weightings
specified by the user (40) or the weightings developed over time
from user preferences. This overall context and the one or more
separate contexts are propagated as a SOAP compliant Personalized
Modeling System (100). Each layer is presented separately for each
function and the overall context. As discussed previously, it is
possible to bundle or separate layers in any combination. This
information in the service is communicated to the Complete
Context.TM. Suite (625), narrow systems (4) and devices (3) using
the Complete Context.TM. Service Interface (711) before processing
passes to a software block 414. It is to be understood that the
system is also capable of bundling this the context information by
layer in one or more bots as well as propagating a layer containing
this information for use in a computer operating system, mobile
operating system, network operating system or middleware
application.
[0272] The software in block 414 checks the system settings table
(162) in the contextbase (50) to determine if a natural language
interface (714) is going to be used. If a natural language
interface is going be used, then processing advances to a software
block 420. Alternatively, if a natural language interface is not
going to be used, then processing advances to a software block
431.
[0273] The software in block 420 combines the ontology developed in
prior steps in processing with unsupervised natural language
processing to provide a true natural language interface to the
system of the present invention (100). A true natural language
interface is an interface that provides the system of the present
invention with an understanding of the meaning of the words as well
as a correct identification of the words. As shown in FIG. 11, the
processing to support the development of a true natural language
interface starts with the receipt of audio input to the natural
language interface (714) from audio sources (1), video sources (2),
devices (3), narrow systems (4), a portal (11) and/or services in
the Complete Context.TM. Suite (625). From there, the audio input
passes to a software block 750 where the input is digitized in a
manner that is well know. After being digitized, the input passes
to a software block 751 where it is segmented into phonemes using a
constituent-context model. The phonemes are then passed to a
software block 752 where they are compared to previously stored
phonemes in the phoneme table (170) to identify the most probable
set of words contained in the input. The most probable set of words
are saved in the natural language table (169) in the contextbase
(50) before processing advances to a software block 756.
[0274] The software in block 756 compares the word set to
previously stored phrases in the phrase table (172) and the
ontology from the ontology table (152) to classify the word set as
one or more phrases. After the classification is completed and
saved in the natural language table (169), processing passes to a
software block 757.
[0275] The software in block 757 checks the natural language table
(169) to determine if there are any phrases that could not be
classified with a weight of evidence level greater than or equal to
the level specified by the user (40) in the system settings table
(162). If all the phrases could be classified within the specified
levels, then processing advances to a software block 759.
Alternatively, if there were phrases that could not be classified
within the specified levels, then processing advances to a software
block 758.
[0276] The software in block 758 uses the constituent-context model
that uses word classes in conjunction with a dependency structure
model to identify one or more new meanings for the low probability
phrases. These new meanings are compared to known phrases in an
external database (7) such as the Penn Treebank and the system
ontology (152) before being evaluated, classified and presented to
the user (40). After classification is complete, processing
advances to software block 759.
[0277] The software in block 759 uses the classified input and
ontology to generate a response (that may include the completion of
actions) to the translated input and generate a response to the
natural language interface (714) that is then forwarded to a device
(3), a narrow system (4), an external service (9), a portal (11),
an audio output device (12) or an service in the Complete
Context.TM. Suite (625). This process continues until all natural
language input has been processed. When this processing is
complete, processing advances to a software block 431.
[0278] The software in block 431 checks the system settings table
(162) in the contextbase (50) to determine if services or bots are
going to be created. If services or bots are not going to be
created, then processing advances to a software block 433.
Alternatively, if services or bots are going to be created, then
processing advances to a software block 432.
[0279] The software in block 432 supports the development interface
window (712) that supports four distinct types of development
projects by the Complete Context.TM. Programming System (610):
[0280] 1. the development of extensions to Complete Context.TM.
Suite (625) in order to provide the user (40) with the specific
information for a given user requirement; [0281] 2. the development
of Complete Context.TM. Bots (650) to complete one or more actions,
initiate one or more actions, complete one or more events, respond
to requests for actions, respond to actions, respond to events,
obtain data or information and combinations thereof. The software
developed using this option can be used for software bots or agents
and robots; [0282] 3. programming devices (3) with rules of
behavior for different contexts that are consistent with the
context frame being provided--i.e. when in church (reference layer
location) do not ring unless it is the boss (element) calling; and
[0283] 4. the development of new context aware services. The first
screen displayed by the Complete Context.TM. Programming System
(610) asks the user (40) to identify the type of development
project. The second screen displayed by the Complete Context.TM.
Programming System (610) will depend on which type of development
project the user (40) is completing. If the first option is
selected, then the user (40) is given the option of using
pre-defined patterns and/or patterns extracted from existing narrow
systems (4) to modify one or more of the services in the Complete
Context.TM. Suite (625). The user (40) can also program the service
extensions using C++ or Java with or without the use of
patterns.
[0284] If the second option is selected, then the user (40) is
shown a display of the previously developed entity schema (157) for
use in defining an assignment and context frame for a Complete
Context.TM. Bot (650). After the assignment specification is stored
in the bot assignment table (167), the Complete Context.TM.
Programming System (610) defines a probabilistic simulation of bot
performance under the three previously defined scenarios. The
results of the simulations are displayed to the user (40) via the
development interface window (712). The Complete Context.TM.
Programming System (610) then gives the user (40) the option of
modifying the bot assignment or approving the bot assignment. If
the user (40) decides to change the bot assignment, then the change
in assignment is saved in the bot assignment table (167) and the
process described for this software block is repeated.
Alternatively, if the user (40) does not change the bot assignment,
then Complete Context.TM. Programming System (610) completes two
primary functions. First, it combines the bot assignment with
results of the simulations to develop the set of program
instructions that will maximize bot performance under the forecast
scenarios. The bot programming includes the entity ontology and is
saved in the bot assignment table (167). In one embodiment Prolog
is used to program the bots. Prolog is used because it readily
supports the situation calculus analyses used by the Complete
Context.TM. Bots (650) to evaluate their situation and select the
appropriate course of action. Each Complete Context.TM. Bot (650)
has the ability to interact with bots and entities that use other
schemas or ontologies in an automated fashion.
[0285] If the third option is selected, then the previously
information about the context quotient for the device (3) is
developed and used to select the pre-programmed options (i.e. ring,
don't ring, silent ring, etc.) that will be presented to the user
(40) for implementation. The user (40) will also be given the
ability to construct new rules for the device (3) using the
parameters contained within the device-specific context frame.
[0286] If the fourth option is selected, then the user (40) is
given a pre-defined context frame interface shell along with the
option of using pre-defined patterns and/or patterns extracted from
existing narrow systems (4) to develop a new service. The user (40)
can also program the new service completely using C# or Java.
[0287] When programming is complete using one of the four options,
processing advances to a software block 433. The software in block
433 prompts the user (40) via the report display and selection data
window (713) to review and select reports for printing. The format
of the reports is either graphical, numeric or both depending on
the type of report the user (40) specified in the system settings
table (162). If the user (40) selects any reports for printing,
then the information regarding the selected reports is saved in the
report table (153). After the user (40) has finished selecting
reports, the selected reports are displayed to the user (40) via
the report display and selection data window (713). After the user
(40) indicates that the review of the reports has been completed,
processing advances to a software block 434. The processing can
also pass to block 434 if the maximum amount of time to wait for no
response specified by the user (40) in the system settings table is
exceeded before the user (40) responds.
[0288] The software in block 434 checks the report table (153) to
determine if any reports have been designated for printing. If
reports have been designated for printing, then processing advances
to a block 435. It should be noted that in addition to standard
reports like a performance risk matrix and the graphical depictions
of the efficient frontier shown (FIG. 12), the system of the
present invention can generate reports that rank the elements,
factors, resources and/or risks in order of their importance to
measure performance and/or measure risk by entity, by measure
and/or for the entity as a whole. The system can also produce
reports that compare results to plan for actions, impacts and
measure performance if expected performance levels have been
specified and saved in appropriate context layer. The software in
block 435 sends the designated reports to the printer (118). After
the reports have been sent to the printer (118), processing
advances to a software block 437. Alternatively, if no reports were
designated for printing, then processing advances directly from
block 434 to block 437. The software in block 437 checks the system
settings table (162) to determine if the system is operating in a
continuous run mode. If the system is operating in a continuous run
mode, then processing returns to block 205 and the processing
described previously is repeated in accordance with the frequency
specified by the user (40) in the system settings table (162).
Alternatively, if the system is not running in continuous mode,
then the processing advances to a block 438 where the system
stops.
[0289] Thus, the reader will see that the system and method
described above transforms data, information and knowledge from
disparate devices (3) and narrow systems (4) into a Personalized
Modeling System (100). The level of detail, breadth and speed of
the analysis gives users of the Personalized Modeling System (100)
the ability to create context and apply it to solving real world
health problems in an fashion that is uncomplicated and
powerful.
[0290] While the above description contains many specificities,
these should not be construed as limitations on the scope of the
invention, but rather as an exemplification of one embodiment
thereof. Accordingly, the scope of the invention should be
determined not by the embodiment illustrated, but by the appended
claims and their legal equivalents.
* * * * *