U.S. patent application number 10/010754 was filed with the patent office on 2004-01-01 for data pool architecture, system, and method for intelligent object data in heterogeneous data environments.
This patent application is currently assigned to BIOSENTIENTS, INC.. Invention is credited to Gombocz, Erich A., Stanley, Robert A..
Application Number | 20040003132 10/010754 |
Document ID | / |
Family ID | 27585989 |
Filed Date | 2004-01-01 |
United States Patent
Application |
20040003132 |
Kind Code |
A1 |
Stanley, Robert A. ; et
al. |
January 1, 2004 |
Data pool architecture, system, and method for intelligent object
data in heterogeneous data environments
Abstract
Data pool architecture, system, method and computer program
product, for intelligent object data in heterogeneous data
environments. Applications include life sciences, biotechnology,
therapeutic diagnostics and intervention, pharmaceuticals, and
bioinformatics. Intelligent object data structure.. Intelligent
molecular objects (IMO) and data structure include procedural and
non-procedural components where non-procedural component has
features in common with conventional data and procedural component
couples additional information with data and adds functionality and
capabilities to system or method utilizing it. Applications
handling system, architecture, and framework referred to as the
intelligent object handler (IOH). Intelligent object pools (IOP)
which comprise a second database structure in addition to the afore
described IMO data structure.
Inventors: |
Stanley, Robert A.;
(Emeryville, CA) ; Gombocz, Erich A.; (San
Francisco, CA) |
Correspondence
Address: |
FLEHR HOHBACH TEST
ALBRITTON & HERBERT, LLP
Suite 3400
Four Embarcadero Center
San Francisco
CA
94111
US
|
Assignee: |
BIOSENTIENTS, INC.
|
Family ID: |
27585989 |
Appl. No.: |
10/010754 |
Filed: |
December 6, 2001 |
Related U.S. Patent Documents
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Current U.S.
Class: |
719/316 |
Current CPC
Class: |
Y10S 707/99945 20130101;
G06F 16/20 20190101; Y10S 707/99944 20130101 |
Class at
Publication: |
709/316 |
International
Class: |
G06F 009/46; G06F
009/00 |
Claims
We claim:
1. An Intelligent Object Pool for an information technology
platform and Intelligent Object data architecture, said Intelligent
Object Pool comprising processing components, interfaces, and their
comprised structures, definitions, algorithms and methods for a.
bi-directional information interchange between core architectural
elements of the platform; including at least one or some
combination of an external Intelligent Object Handler; comprised
Intelligent Objects; and comprised iPool data subsets; said
information interchange enabled by access interfaces interacting
with other access interfaces and/or components, as well as various
modules and plug-ins; and b. bi-directional information interchange
between components and access interfaces, such as between a
comprised result aggregation engine component, a comprised result
aggregation engine interface; an external master query interface,
and an external status management component; i. said information
interchange taking place in automated; event-driven; interactive;
and/or user-directed methods for activation and implementation of
comprised methods for c. defining and managing Intelligent Object
boundaries and access; d. processing of result aggregation; and e.
storage, processing and management of i. Intelligent Objects, said
Intelligent Objects comprising such as vectorized pointers to
accessible data content; meta-data; and data-enabling methods and
processes; as well as of ii. data pool or "Pool" contents,
comprising the entity of potentially accessible Intelligent Objects
and their data content.
2. An Intelligent Object Pool as in claim 1, comprising further
methods, such as for a. provision of automated; event-driven;
interactive; and/or user-directed methods for activation and
implementation of comprised methods for; i. management and
processing of Intelligent Object subsets (iPools); ii. assessment,
ranking, alerting and validating data integrity; ii. management and
processing of data exchange; iii. management and processing of data
definition; iv. management and processing of process optimization;
v. processing of queries; vi. processing of analyses; vii.
processing of significance generation; iix. provision of data
relationship viewing; ix. provision of data relationship analysis;
x. provision of data relationship learning and knowledge
extraction; and b. storage, processing and management of data
subset Intra-Pools (iPools) comprising defined subsets of
Intelligent Object data within said data Pool.
3. An Intelligent Object Pool as in claim 1, comprising further
methods for a. provision of pool boundary protocols, definitions
and methods for data security; data integrity assessment; and data
exchange via comprised components and access interfaces; b.
provision of meta-data indices and query protocols, definitions and
methods for Intelligent Object meta-data definition and annotation;
meta-data linking; access optimization, routing optimization and
processing optimization via comprised components and interfaces; c.
provision of pool content access protocols, definitions and methods
for aggregate meta-data definition; provision of meta-data
definitions via table lookup for interactive content at routing; d.
provision of meta-data definitions via table lookup for direct
information interchange between Intelligent Objects; aggregation of
processing or query results; report generation; and data
relationship viewing via comprised sets of components and
interfaces.
4. An Intelligent Object Pool as in claim 1, comprising further
pool content access protocols including modules for distributed
learning and knowledge extraction; such as distributed learning
engines (DLE/DLEi); and/or knowledge extraction engines
(KEE/KEEi).
5. An object pool for an information technology platform and data
object data architecture, said object pool comprising processing
components, interfaces, and their comprised structures,
definitions, algorithms and methods for a. bi-directional
information interchange between core architectural elements of the
platform; including at least one or some combination of an external
data object Handler; comprised data objects; and comprised subsets
of data objects; said information interchange enabled by comprised
modules, plug-ins, access interfaces and/or components; b.
bi-directional information interchange between components and
access interfaces, such as between a comprised result aggregation
engine component, a comprised result aggregation engine interface;
an external master query interface, and an external status
management component; c. provision of automated; event-driven;
interactive; and/or user-directed methods for activation and
implementation of comprised methods for; i. management and
processing of data object boundaries and access; ii. processing of
result aggregation; and iii. storage, processing and management of
1. data objects, said data objects comprising such as vectorized
pointers to accessible data content; meta-data; and data-enabling
methods and processes; as well as of 2. data pool contents,
comprising the entity of potentially accessible data objects and
their data content.
6. An object pool as in claim 5, comprising further methods, such
as for a. provision of automated; event-driven; interactive; and/or
user-directed methods for activation and implementation of
comprised methods for; i. management and processing of subsets of
data objects; ii. assessment, ranking, alerting and validating data
integrity; iii. management and processing of data exchange; iv.
management and processing of data definition; v. management and
processing of process optimization; vi. processing of queries; vii.
processing of analyses; viii. processing of significance
generation; ix. provision of data relationship viewing; x.
provision of data relationship analysis; xi. provision of data
relationship learning and knowledge extraction; b. storage,
processing and management of i. data subset Intra-Pools (subsets of
data objects) comprising defined subsets of data object data within
said data Pool.
7. An object pool as in claim 5, comprising further methods for a.
provision of pool boundary protocols, definitions and methods for
data security; data integrity assessment; and data exchange via
comprised components and access interfaces. b. provision of
meta-data indices and related meta-data query protocols,
definitions and methods for data object meta-data definition and
annotation; meta-data linking; access optimization, routing
optimization and processing optimization via comprised components
and interfaces; c. provision of pool content access protocols,
definitions and methods for aggregate meta-data definition;
provision of meta-data definitions via table lookup for interactive
content routing; provision of meta-data definitions via table
lookup for direct information interchange between data objects;
aggregation of processing or query results; report generation; and
data relationship viewing via comprised sets of components and
interfaces.
8. An object pool as in claim 5, comprising further pool content
access protocols including modules for distributed learning and
knowledge extraction; such as distributed learning engines
(DLE/DLEi); and/or knowledge extraction engines (KEE/KEEi).
9. A set of pool boundary protocols for provision of data security,
exchange and integrity, comprising components and access interfaces
providing methods for a. bi-directional information interchange
with components and access interfaces including at least one or
some combination of comprised Intelligent Objects, Pool, iPools and
an external Intelligent Object Handler; b. defining boundaries for
access, security and authentication.
10. A set of pool boundary protocol definitions as in claim 9,
comprising components and access interfaces providing further
methods for a. generation and management of data availability and
accessing information; b. management and processing of data
subscribed to or otherwise exchanged; c. maintenance of access
histories for auditing of exchange and ownership activities; and d.
verification of data integrity states for quality assurance.
11. A set of pool boundary protocol definitions as in claim 9,
comprising components and access interfaces providing further
methods for utilization of Intelligent Objects.
12. A set of pool boundary protocol definitions as in claim 9,
comprising components and access interfaces providing further
methods for utilization of object-oriented data structures other
than Intelligent Objects.
13. A set of meta-data index definitions to enable definition and
linking of multiple dimensions of data annotation and attributes,
to provide analytical functionality and to improve query and
analytical efficiency, comprising components and access interfaces
providing methods for a. bi-directional information interchange
with components and access interfaces including at least one or
some combination of comprised Intelligent Objects, Pool, iPools and
an external Intelligent Object Handler; b. providing linking of
meta-data index definitions; c. providing meta-data index-based
optimization of queries and processing.
14. A set of meta-data index definitions and methods as in claim
13, comprising components and access interfaces providing further
methods for a. generating meta-data index definitions for sets of
data; b. provision of meta-data information required for
definition, translation and integration of Intelligent Objects; c.
integration of analytical processes with Intelligent Objects and
iPools; and d. providing meta-data index-based optimization of
Intelligent Object-to-Intelligent Object and iPool-to-iPool
intercommunication.
15. A set of meta-data index definitions and methods as in claim
13, comprising components and access interfaces providing further
methods for a. utilizing meta-data information to activate methods
and algorithms such as nested vector lookup tables; interactive
presorting and exclusion algorithms and object clustering
algorithms.
16. A set of meta-data index definitions and methods as in claim
13, comprising components and access interfaces providing further
methods for utilization of Intelligent Objects.
17. A set of meta-data index definitions and methods as in claim
13, comprising components and access interfaces providing further
methods for utilization of object-oriented data structures other
than Intelligent Objects.
18. A set of pool content access definitions, comprising components
and access interfaces which provide a variety of automated and/or
user-directed methods for Pool and iPool accessing and processing,
such as for a. bi-directional information interchange with
components and access interfaces including at least one or some
combination of comprised Intelligent Objects, Pool, iPools and an
external Intelligent Object Handler; and b. aggregating results of
queries and/or analytical processes applied to batches or sets of
Intelligent Object data.
19. A set of pool content access definitions and methods as in
claim 18, comprising components and access interfaces providing
further methods for a. data relationship viewing; b. application of
custom-defined methods to data sets; c. real-time significance
detection; d. real-time significant answer generation; and e.
report generation.
20. A set of pool content access definitions and methods as in
claim 18, comprising components and access interfaces providing
further methods for a. enabling object-to-analysis tools
interactions; b. provision of result merging algorithms; and c.
provision of clustering algorithms; including i. calculation
methods not limited to 1. Jacquard coefficient; 2. Dice
coefficient; 3. Jeffrey coefficient; 4. Pearson coefficient 5.
simple matching; 6. product moment correlation coefficient; 7. mean
square difference; and; 8. absolute difference; and ii. clustering
methods: not limited to 1. minimum variance; 2. single, complete,
average and weighted average linking; 3. median method; 4. centroid
method; 5. neighbor joining method; 6. Fitch-Margoliash least
square method; and 7. Fitch-Margoliash evolutionary clock
method.
21. A set of pool content access definitions and methods as in
claim 18, comprising components and access interfaces providing
further methods for provision of learning and knowledge extraction
algorithms.
22. A set of pool content access definitions as in claim 18,
comprising components and access interfaces providing further
methods for utilization of Intelligent Objects.
23. A set of pool content access definitions as in claim 18,
comprising components and access interfaces providing further
methods for utilization of object-oriented data structures other
than Intelligent Objects.
24. An iPool security authentication component (iPSA) for provision
and implementation of Intelligent Object, Pool and iPool security
definitions, authentication and access permissions, comprising
automated and/or interactive methods for a. bi-directional
information interchange with components and access interfaces such
as the unified presentation layer; iPool security authentication
interface; and pool boundary protocol components; b. definition of
access permissions; c. implementation of criteria for permission or
denial of specific access, routing and processing activities;
including at least one or some combination of permission or denial
to access and/or route any Intelligent Object data content or
property information; and d. authentication and permission or
denial of data access requests according to correspondence of user
login information to user definition and administration permissions
provided by such as an external Intelligent Object Handler; iPool
access permissions; and Intelligent Object access permissions.
25. An iPool security authentication component as in claim 24,
comprising further methods for a. implementation of criteria for
permission or denial to access and/or route subsets of data content
defined to the level of single bytes by activation of vectorized
pointers to said data content.
26. An iPool security authentication component as in claim 24,
comprising further methods for a. implementation of criteria for
permission or denial to access and/or route subsets iPool meta-data
information and definitions; including at least one or some
combination of iPool content overviews; and iPool relationship
information.
28. An iPool security authentication component as in claim 24,
which comprises further methods for definition and implementation
of data according to conditions of ownership.
29. An iPool security authentication component as in claim 24,
further comprised within an information technology platform using
object-oriented data structures other than Intelligent Objects.
30. An iPool security authentication interface (iPSAi) providing
conventions used to enable communication between components and
optionally other interfaces, comprising automated and/or
interactive methods for a. bi-directional information interchange
with components and access interfaces such as the following iPool
security authentication component; Intelligent Object Pool access
interfaces; data Pool; iPools; Intelligent Object Handler; and
Intelligent Molecular Objects; b. interchange of information
required for and/or provided by iPool security authentication; and
c. provision of iPool security authentication component activity
information to access interfaces and components as required for
synchronization, recordation, auditing and validation of said
information interchange and activity.
31. An iPool security authentication interface as in claim 30,
comprising further methods for detection and extraction of
information required for iPool security authentication.
32. An iPool security authentication interface as in claim 30,
comprising further methods for interchange of information required
for and/or provided by iPool security authentication including
meta-data index information such as data content information; data
attribute information; data relationship information; protocols;
ontologies; annotations; and other data-enabling information.
33. An iPool security authentication interface as in claim 30,
comprising further methods for bi-directional interchange of data
content accessed via vector pointers as required for and/or
provided by iPool security authentication.
34. An iPool security authentication interface as in claim 30,
comprised within an information technology platform using
Intelligent Objects.
35. An iPool security authentication interface as in claim 30,
further comprised within an information technology platform using
object-oriented data structures other than Intelligent Objects.
36. An iPool availability monitoring component (iPAM) for provision
and implementation of availability, access and addressing
information, comprising automated and/or interactive methods for a.
bi-directional information interchange with components and access
interfaces including at least one or some combination of the
following unified presentation layer; iPool availability monitoring
interface; and pool boundary protocol components; and b.
availability monitoring for iPool data content availability.
37. An iPool availability monitoring component (iPAM) as in claim
36, comprising further methods for a. access management for iPool
data content according to availability; b. generation of meta-data
definitions regarding data availability; and c. provision of data
and data subset cacheing for flagged data including at least one or
some combination of specified Intelligent Object data; Intelligent
Object data content workspace subsets; offline Intelligent Object
data; and for data including at least one or some combination of
user defined; often used; and recently used data.
38. An iPool availability monitoring component (iPAM) as in claim
36, comprising further methods for a. provision of data content
backup functionality for specified data; b. provision of multiple
content addressing for specified or often used data; b. data
nomenclature alias detection and linking; and c. multiple address
ranking and management.
39. An iPool availability monitoring component as in claim 36,
comprised as a module within an information technology platform for
Intelligent Objects.
40. An iPool availability monitoring component as in claim 36,
comprised as a module within an information technology platform
using object-oriented data structures.
41. An iPool availability monitoring interface (iPAMi) providing
conventions used to enable communication between components and
optionally other interfaces, comprising automated and/or
interactive methods for a. bi-directional information interchange
with components and access interfaces such as the following iPool
availability monitoring component; Intelligent Object Pool access
interfaces; data Pool; iPools; Intelligent Object Handler; and
Intelligent Molecular Objects; b. interchange of information
required for and/or provided by iPool availability monitoring; and
c. provision of iPool availability monitoring component activity
information to access interfaces and components as required for
synchronization, recordation, auditing and validation of said
information interchange and activity.
42. An iPool availability monitoring interface as in claim 41,
comprising further methods for detection and extraction of
information required for iPool availability monitoring.
43. An iPool availability monitoring interface as in claim 41,
comprising further methods for interchange of information required
for and/or provided by iPool availability monitoring including
meta-data index information such as data content information; data
attribute information; data relationship information; protocols;
ontologies; annotations; and other data-enabling information.
44. An iPool availability monitoring interface as in claim 41,
comprising further methods for bi-directional interchange of data
content accessed via vector pointers as required for and/or
provided by iPool availability monitoring.
45. An iPool availability monitoring interface as in claim 41,
comprised within an information technology platform using
Intelligent Objects.
46. An iPool availability monitoring interface as in claim 41,
further comprised within an information technology platform using
object-oriented data structures other than Intelligent Objects.
47. An iPool exchange protocol component (iPEP), for data ownership
and data exchange definition and management, comprising methods for
a. bi-directional information interchange with components and
access interfaces including at least one or some combination of the
following unified presentation layer; iPool exchange protocol
interface; and pool boundary protocol components; b. secure,
interactive definition of iPool ownership status; and c. definition
of iPool ownership privileges and parameters.
48. An iPool exchange protocol component as in claim 47, comprising
further methods for a. definition regarding criteria for exchange,
including i. protocols and/or algorithms for Intelligent Object
data attribute access; ii. protocols and/or algorithms for such as
ownership, licensing, and subscriptions; iii. protocols and/or
algorithms for such as exchange charges, and royalties; iv.
protocols and/or algorithms for exchange accounting; and v. account
definitions; b. provision of user ownership management; c.
provision of user ownership recordation; d. provision of
authentication and permission or denial of iPool access requests;
according to correspondence of i. user ownership verification to
comprised definitions such as for access, routing and
processing;
49. An iPool exchange protocol component as in claim 47, comprising
further methods for a. provision of user exchange activity history
provided by an interface to an external Intelligent Object Handler;
b. provision of data attribute access and linking activities; c.
provision of ownership, licensing, and subscription status; d.
provision of fees, charges and royalty status information;
including i. previewing of fees, charges and/or royalty terms and
conditions according to proposed queries and/or user commands
defined but not yet requested.
50. An iPool exchange protocol component as in claim 47, comprised
as a module within an information technology platform for
Intelligent Objects.
51. An iPool exchange protocol component as in claim 47, comprised
as a module within an information technology platform using
object-oriented data structures.
52. An iPool exchange protocol interface (iPEPi) providing
conventions used to enable communication between components and
optionally other interfaces, comprising automated and/or
interactive methods for a. bi-directional information interchange
with components and access interfaces such as the following iPool
exchange protocol component; Intelligent Object Pool access
interfaces; data Pool; iPools; Intelligent Object Handler; and
Intelligent Molecular Objects; b. interchange of information
required for and/or provided by iPool exchange protocol; and c.
provision of iPool exchange protocol component activity information
to access interfaces and components as required for
synchronization, recordation, auditing and validation of said
information interchange and activity.
53. An iPool exchange protocol interface as in claim 52, comprising
further methods for detection and extraction of information
required for iPool exchange protocol.
54. An iPool exchange protocol interface as in claim 52, comprising
further methods for interchange of information required for and/or
provided by iPool exchange protocol including meta-data index
information such as data content information; data attribute
information; data relationship information; protocols; ontologies;
annotations; and other data-enabling information.
55. An iPool exchange protocol interface as in claim 52, comprising
further methods for bi-directional interchange of data content
accessed via vector pointers as required for and/or provided by
iPool exchange protocol.
56. An iPool exchange protocol interface as in claim 52, comprised
within an information technology platform using Intelligent
Objects.
57. An iPool exchange protocol interface as in claim 52, further
comprised within an information technology platform using
object-oriented data structures other than Intelligent Objects.
58. An object integrity assessment component (OIA) for detection
and verification of data integrity, comprising methods for a.
bi-directional information interchange with components and access
interfaces including at least one or some combination of the
following unified presentation layer; object integrity assessment
interface; and pool boundary protocol components; b. provision of
data checksums and checksum reporting; and c. provision of
algorithms for data integrity verification.
59. An object integrity assessment component as in claim 58,
comprising further methods for a. review of Intelligent Object
state histories for alerts or integrity violations; and b.
provision of various authentication and integrity verification
procedures as required for quality assurance/quality control.
60. An object integrity assessment component as in claim 58,
comprising further methods for a. provision of alerts regarding
Intelligent Object integrity violation status; and b. provision of
alerts regarding processes that may or will violate Intelligent
Object integrity.
61. An object integrity assessment component as in claim 58,
comprising further methods for activation of an interactive user
interface providing a description of said alert and providing
optional processing methods and/or cancellation of said
processes.
62. An object integrity assessment component as in claim 58,
comprised as a module within an information technology platform for
Intelligent Objects.
63. An object integrity assessment component as in claim 58,
comprised as a module within an information technology platform
using object-oriented data structures.
64. An object integrity assessment interface (iOIAi) providing
conventions used to enable communication between components and
optionally other interfaces, comprising automated and/or
interactive methods for a. bi-directional information interchange
with components and access interfaces such as the following iPool
integrity assessment component; Intelligent Object Pool access
interfaces; data Pool; iPools; Intelligent Object Handler; and
Intelligent Molecular Objects; b. interchange of information
required for and/or provided by object integrity assessment; and c.
provision of object integrity assessment component activity
information to access interfaces and components as required for
synchronization, recordation, auditing and validation of said
information interchange and activity.
65. An object integrity assessment interface as in claim 64,
comprising further methods for detection and extraction of
information required for object integrity assessment.
66. An object integrity assessment interface as in claim 64,
comprising further methods for interchange of information required
for and/or provided by object integrity assessment including
meta-data index information such as data content information; data
attribute information; data relationship information; protocols;
ontologies; annotations; and other data-enabling information.
67. An object integrity assessment interface as in claim 64,
comprising further methods for bi-directional interchange of data
content accessed via vector pointers as required for and/or
provided by object integrity assessment.
68. An object integrity assessment interface as in claim 64,
comprised within an information technology platform using
Intelligent Objects.
69. An object integrity assessment interface as in claim 64,
further comprised within an information technology platform using
object-oriented data structures other than Intelligent Objects.
70. An iPool integrity assessment component (iPIA), for detection
and verification of iPool integrity, comprising methods for a.
bi-directional information interchange with components and access
interfaces including at least one or some combination of the
following unified presentation layer; iPool integrity assessment
interface; and pool boundary protocol components; b. provision of
iPool data checksums and checksum reporting; and c. provision of
algorithms for data set integrity verification.
71. An iPool integrity assessment component as in claim 70,
comprising further methods for a. review of iPool histories for
alerts or integrity violations; and b. provision of various
authentication and integrity verification procedures as required
for quality assurance/quality control.
72. An iPool integrity assessment component as in claim 70,
comprising further methods for a. provision of alerts regarding
iPool integrity violation status; and b. provision of alerts
regarding processes that may or will violate iPool integrity.
73. An iPool integrity assessment component as in claim 70,
comprising further methods for a. activation of an interactive user
interface providing a description of said alert and providing
optional processing methods and/or cancellation of said
processes.
74. An iPool integrity assessment component as in claim 70,
comprised as a module within an information technology platform for
Intelligent Objects.
75. An iPool integrity assessment component as in claim 70,
comprised as a module within an information technology platform
using object-oriented data structures.
76. An iPool integrity assessment interface (iPIAi) providing
conventions used to enable communication between components and
optionally other interfaces, comprising automated and/or
interactive methods for a. bi-directional information interchange
with components and access interfaces such as the following iPool
integrity assessment component; Intelligent Object Pool access
interfaces; data Pool; iPools; Intelligent Object Handler; and
Intelligent Molecular Objects; b. interchange of information
required for and/or provided by iPool integrity assessment; and c.
provision of iPool integrity assessment component activity
information to access interfaces and components as required for
synchronization, recordation, auditing and validation of said
information interchange and activity.
77. An iPool integrity assessment interface as in claim 76,
comprising further methods for detection and extraction of
information required for iPool integrity assessment.
78. An iPool integrity assessment interface as in claim 76,
comprising further methods for interchange of information required
for and/or provided by iPool integrity assessment including
meta-data index information such as data content information; data
attribute information; data relationship information; protocols;
ontologies; annotations; and other data-enabling information.
79. An iPool integrity assessment interface as in claim 76,
comprising further methods for bi-directional interchange of data
content accessed via vector pointers as required for and/or
provided by iPool integrity assessment.
80. An iPool integrity assessment interface as in claim 76,
comprised within an information technology platform using
Intelligent Objects.
81. An iPool integrity assessment interface as in claim 76, further
comprised within an information technology platform using
object-oriented data structures other than Intelligent Objects.
82. An iPool meta-data index component (iMDX), for generation and
provision of iPool meta-data information, comprising methods for a.
bi-directional information interchange with components and access
interfaces including at least one or some combination of the
following unified presentation layer; iPool meta-data index
interface; and meta-data index components; i. provision of
meta-data definitions for iPool data subsets; such as 1. data
subset content attributes; 2. data type, structure, and access
dependencies; 3. data ownership and access information; and 4. data
relationship information; according to available algorithms for
determination of such as, but not limited to similarity;
association; contiguity; proximity; dependency; functionality; data
activity ranking; data significance ranking; and data validation
ranking.
83. An iPool meta-data index component as in claim 82, further
comprising methods for a. exclusion of irrelevant object data
layers; b. provision of result aggregation optimization based on
linking of result aggregation requirements to iPool Meta-data index
content; and c. provision of query optimization based on linking of
query parameters to iPool Meta-data index content.
84. An iPool meta-data index component as in claim 82, further
comprising methods for provision of iPool meta-data information
required for methods and processes including at least one or some
combination of iPool activation; iPool sorting; iPool-to-iPool
queries; iPool data presentation; and result generation.
85. An iPool integrity assessment component as in claim 82,
comprised as a module within an information technology platform for
Intelligent Objects.
86. An iPool integrity assessment component as in claim xx,
comprised as a module within an information technology platform
using object-oriented data structures.
87. An iPool meta-data index interface (iMDXi) providing
conventions used to enable communication between components and
optionally other interfaces, comprising automated and/or
interactive methods for a. bi-directional information interchange
with components and access interfaces such as the following iPool
integrity assessment component; Intelligent Object Pool access
interfaces; data Pool; iPools; Intelligent Object Handler; and
Intelligent Molecular Objects; b. interchange of information
required for and/or provided by iPool meta-data indexing; and c.
provision of iPool meta-data index component activity information
to access interfaces and components as required for
synchronization, recordation, auditing and validation of said
information interchange and activity.
88. An iPool meta-data index interface as in claim 87, comprising
further methods for detection and extraction of information
required for iPool meta-data indexing.
89. Pool meta-data index interface as in claim 87, comprising
further methods for interchange of information required for and/or
provided by iPool meta-data indexing actions including meta-data
index information such as data content information; data attribute
information; data relationship information; protocols; ontologies;
annotations; and other data-enabling information.
90. Pool meta-data index interface as in claim 87, comprising
further methods for bi-directional interchange of data content
accessed via vector pointers as required for and/or provided by
iPool meta-data indexing.
91. Pool meta-data index interface as in claim 87, comprised within
an information technology platform using Intelligent Objects.
92. Pool meta-data index interface as in claim 87, further
comprised within an information technology platform using
object-oriented data structures other than Intelligent Objects.
93. A real-time meta-data linking component (RML), for dynamic
linking of meta-data, comprising methods for a. bi-directional
information interchange with components and access interfaces
including at least one or some combination of the following unified
presentation layer; real-time meta-data linking interface; and
meta-data index components; b. detection of meta-data presented via
actions including automated and/or user-directed queries; commands;
and processing requests; and c. linking of meta-data detected
corresponding to meta-data comprised within linked meta-data
indices.
94. A real-time meta-data linking component as in claim 93,
comprising further methods for linking of meta-data detected
corresponding to meta-data comprised within linked meta-data
indices said meta-data comprising at least one or some combination
of attributes; annotations; properties, values, definitions;
tables; lists; addressing protocols; access and routing protocols;
processing protocols; and/or query parameters.
95. A real-time meta-data linking component as in claim 93,
comprising further methods for a. activation of methods and
processes corresponding to linked meta-data; b. provision of
relevant data access based on correspondence of said query and/or
processing request meta-data parameters to provided meta-data
indices.
96. A real-time meta-data linking component as in claim 93,
comprised as a module within an information technology platform for
Intelligent Objects.
97. A real-time meta-data linking component as in claim 93,
comprised as a module within an information technology platform
using object-oriented data structures.
98. A real-time meta-data linking interface (RMLi) providing
conventions used to enable communication between components and
optionally other interfaces, comprising automated and/or
interactive methods for a. bi-directional information interchange
with components and access interfaces such as the following
real-time meta-data linking component; Intelligent Object Pool
access interfaces; data Pool; iPools; Intelligent Object Handler;
and Intelligent Molecular Objects; b. interchange of information
required for and/or provided by real-time meta-data linking; and c.
provision of real-time meta-data linking component activity
information to access interfaces and components as required for
synchronization, recordation, auditing and validation of said
information interchange and activity.
99. A real-time meta-data linking interface as in claim 98,
comprising further methods for detection and extraction of
information required for real-time meta-data linking.
100. A real-time meta-data linking interface as in claim 98,
comprising further methods for interchange of information required
for and/or provided by real-time meta-data linking including
meta-data index information such as data content information; data
attribute information; data relationship information; protocols;
ontologies; annotations; and other data-enabling information.
101. A real-time meta-data linking interface as in claim 98,
comprising further methods for bi-directional interchange of data
content accessed via vector pointers as required for and/or
provided by real-time meta-data linking.
102. A real-time meta-data linking interface as in claim 98,
comprised within an information technology platform using
Intelligent Objects.
103. A real-time meta-data linking interface as in claim 98,
further comprised within an information technology platform using
object-oriented data structures other than Intelligent Objects.
104. An Intelligent Object-to-Intelligent Object query component
(OQM) for meta-data definition of data interrelationships on the
individual data object level, comprising methods for a.
bi-directional information interchange with components and access
interfaces including at least one or some combination of the
following unified presentation layer; Intelligent
Object-to-Intelligent Object query interface; and meta-data index
components; b. provision of dynamically updated and/or user-defined
meta-data definitions for meta-data linking and query optimization;
of at least one or some combination of i. Intelligent Object state
information; structure; function; type; and content; further
comprising ii. meta-data information such as content addressing;
content association; weighting; sorting; and ranking; of such as
attributes; annotations; and definitions.
105. An Intelligent Object-to-Intelligent Object query component as
in claim 104, comprising further methods for a. provision of
definitions for Intelligent Object-to-Intelligent Object
information interchange; comprising meta-data definitions; lists;
tables; indices; for b. activation of direct meta-data to meta-data
information linking; accessing; routing; and information
interchange corresponding to automated and/or user-directed
queries.
106. An Intelligent Object-to-Intelligent Object query component as
in claim 104, comprising further methods for a. organization of
said Intelligent Object content and meta-data linking according to
information including at least one or some combination of content
attributes; validation state; ranking; relationships; associations;
and the like for b. provision of query optimization; based on
dynamic updating of said Intelligent Object organization and
linking for information interchange; according to correspondence
between said information and queries; query histories; commands;
command histories; and/or other access, routing and processing
actions.
107. An Intelligent Object-to-Intelligent Object query in claim
104, comprised as a module within an information technology
platform for Intelligent Objects.
108. An Intelligent Object-to-Intelligent Object query component as
in claim 104, comprised as a module within an information
technology platform using object-oriented data structures.
109. An Intelligent Object-to-Intelligent Object meta-data query
interface (OQMi) providing conventions used to enable communication
between components and optionally other interfaces, comprising
automated and/or interactive methods for a. bi-directional
information interchange with components and access interfaces such
as the following Intelligent Object-to-Intelligent Object query
component; Intelligent Object Pool access interfaces; data Pool;
iPools; Intelligent Object Handler; and Intelligent Molecular
Objects; b. interchange of information required for and/or provided
by Intelligent Object-to-Intelligent Object meta-data query
optimization; and c. provision of Intelligent Object-to-Intelligent
Object meta-data query component activity information to access
interfaces and components as required for synchronization,
recordation, auditing and validation of said information
interchange and activity.
110. An Intelligent Object-to-Intelligent Object meta-data query
interface as in claim 109, comprising further methods for detection
and extraction of information required for customization and/or
optimization of Intelligent Object-to-Intelligent Object
queries.
111. An Intelligent Object-to-Intelligent Object meta-data query
interface as in claim 109, comprising further methods for
interchange of information required for and/or provided by
Intelligent Object-to-Intelligent Object query processes, said
information comprising meta-data index information such as data
content information; data attribute information; data relationship
information; protocols; ontologies; annotations; and other
data-enabling information.
112. An Intelligent Object-to-Intelligent Object query interface as
in claim 109, comprising further methods for bi-directional
interchange of data content accessed via vector pointers as
required for and/or provided by Intelligent Object-to-Intelligent
Object meta-data queries.
113. An Intelligent Object-to-Intelligent Object query interface as
in claim 109, comprised within an information technology platform
using Intelligent Objects.
114. An Intelligent Object-to-Intelligent Object query interface as
in claim 109, further comprised within an information technology
platform using object-oriented data structures other than
Intelligent Objects.
115. An iPool-to-iPool query component (PPQ) for meta-data
definition of data interrelationships on the iPool level,
comprising methods for a. bi-directional information interchange
with components and access interfaces including at least one or
some combination of the following unified presentation layer;
iPool-to-iPool query interface; and meta-data index components; b.
provision of dynamically updated and/or user-defined meta-data
definitions for meta-data linking and query optimization; of at
least one or some combination of i. Intelligent Object state
information; structure; function; type; and content; further
comprising ii. meta-data information such as content addressing;
content association; weighting; sorting; and ranking; of such as
attributes; annotations; and definitions.
116. An iPool-to-iPool query component as in claim 115, comprising
further methods for a. provision of definitions for iPool-to-iPool
information interchange; comprising meta-data definitions; lists;
tables; indices; for b. activation of direct meta-data to meta-data
information linking; accessing; routing; and information
interchange corresponding to automated and/or user-directed
queries.
117. An iPool-to-iPool query component as in claim 115, comprising
further methods for organization of said iPool content and
meta-data linking according to information including at least one
or some combination of content attributes; validation state;
ranking; relationships; associations; and the like for provision of
query optimization; based on dynamic updating of said iPool
organization and linking for information interchange; according to
correspondence between said information and queries; query
histories; commands; command histories; and/or other access,
routing and processing actions.
118. An iPool-to-iPool query in claim 115, comprised as a module
within an information technology platform for Intelligent
Objects.
119. An iPool-to-iPool query component as in claim 115, comprised
as a module within an information technology platform using
object-oriented data structures.
120. An iPool-to-iPool meta-data query interface (OQMi) providing
conventions used to enable communication between components and
optionally other interfaces, comprising automated and/or
interactive methods for a. bi-directional information interchange
with components and access interfaces such as the following
iPool-to-iPool query component; Intelligent Object Pool access
interfaces; data Pool; iPools; Intelligent Object Handler; and
Intelligent Molecular Objects; b. interchange of information
required for and/or provided by iPool-to-iPool meta-data query
optimization; and c. provision of iPool-to-iPool meta-data query
component activity information to access interfaces and components
as required for synchronization, recordation, auditing and
validation of said information interchange and activity.
121. An iPool-to-iPool meta-data query interface as in claim 120,
comprising further methods for detection and extraction of
information required for customization and/or optimization of
iPool-to-iPool queries.
122. An iPool-to-iPool meta-data query interface as in claim 120,
comprising further methods for interchange of information required
for and/or provided by iPool-to-iPool query processes, said
information comprising meta-data index information such as data
content information; data attribute information; data relationship
information; protocols; ontologies; annotations; and other
data-enabling information.
123. An iPool-to-iPool query interface as in claim 120, comprising
further methods for bi-directional interchange of data content
accessed via vector pointers as required for and/or provided by
iPool-to-iPool meta-data queries.
124. An iPool-to-iPool query interface as in claim 120, comprised
within an information technology platform using Intelligent
Objects.
125. An iPool-to-iPool query interface as in claim 120, further
comprised within an information technology platform using
object-oriented data structures other than Intelligent Objects.
126. An aggregate meta-data index generator component (aMDX), for
generating meta-data indices based upon aggregated information and
processing results, comprising methods for a. bi-directional
information interchange with components and access interfaces
including at least one or some combination of the following unified
presentation layer; aggregate meta-data index generator interface;
and pool content access components; and b. generation of meta-data
definitions for iPool data subsets; including at least one or some
combination of automatically provided and/or user-defined
information regarding Intelligent Object and iPool data.
127. An aggregate meta-data index generator as in claim 126,
comprising further methods for a. generation of meta-data
definitions for iPool data subsets; including at least one or some
combination of automatically provided and/or user-defined
information regarding Intelligent Object and iPool data i.
including meta-data for such as iPool subset attributes;
annotations; protocols; ontologies; content attributes; data type,
structure, and access dependencies; ownership and access
information; relationship information; activity ranking histories;
significance ranking histories; and validation ranking
histories.
128. An aggregate meta-data index generator as in claim 126,
comprising further methods for a. organization; sorting and ranking
of said information according to available algorithms for
assessment of relationships such as at least one of the following
similarity; association; contiguity; proximity; dependency;
functionality; for b. query optimization requirements, based on
correspondence of submitted query parameters; and query histories;
to contents of meta-data indices.
129. An aggregate meta-data index generator as in claim 126,
comprising further methods for a. generation of meta-data
information required for methods and processes including at least
one or some combination of iPool activation; iPool sorting;
iPool-to-iPool queries; iPool data presentation; and result
generation.
130. An aggregate meta-data index generator as in claim 126,
comprised as a module within an information technology platform for
Intelligent Objects.
131. An aggregate meta-data index generator as in claim 126,
comprised as a module within an information technology platform
using object-oriented data structures.
132. An aggregate meta-data index generator interface (aMDXi)
providing conventions used to enable communication between
components and optionally other interfaces, comprising automated
and/or interactive methods for a. bi-directional information
interchange with components and access interfaces such as the
following iPool-to-iPool query component; Intelligent Object Pool
access interfaces; data Pool; iPools; Intelligent Object Handler;
and Intelligent Molecular Objects; b. interchange of information
required for and/or provided by comprised methods and processes for
meta-data index generation and optimization; and c. provision of
aggregate meta-data index generator component activity information
to access interfaces and components as required for
synchronization, recordation, auditing and validation of said
information interchange and activity.
133. An aggregate meta-data index generator interface as in claim
132, comprising further methods for detection and extraction of
information required for customization and/or optimization of
meta-data index generation.
134. An aggregate meta-data index generator interface as in claim
132, comprising further methods for interchange of information
required for and/or provided by meta-data index generation, said
information comprising meta-data index information such as data
content information; data attribute information; data relationship
information; protocols; ontologies; annotations; and other
data-enabling information.
135. An aggregate meta-data index generator interface as in claim
132, comprising further methods for bi-directional interchange of
data content accessed via vector pointers as required for and/or
provided by meta-data index generation methods and processes.
136. An aggregate meta-data index generator interface as in claim
132, comprised within an information technology platform using
Intelligent Objects.
137. An aggregate meta-data index generator interface as in claim
132, further comprised within an information technology platform
using object-oriented data structures other than Intelligent
Objects.
138. An aggregate real-time significance generator component
(aRSG), for comparison; correlation; significance detection; and
significance generation of relationships between Intelligent Object
data and between iPool subsets, comprising methods for a.
bi-directional information interchange with components and access
interfaces including at least one or some combination of the
following unified presentation layer; aggregate real-time
significance generator interface; and pool content access
components; and b. application of algorithms for normalized
comparison; correlation; significance detection and generation of
information including at least one or some combination of
Intelligent Object properties; data content properties and values;
and meta-data index content; according to all valid query
parameters.
139. An aggregate real-time significance generator as in claim 138,
comprising further methods for organization of data utilizing
methods and processes including assembly; ranking; classification;
and/or tabulation of Intelligent Objects; based on the significance
of results generated by external applications; components; access
interfaces; and activated by query submissions, user preference
rules and/or viewing instructions.
140. An aggregate real-time significance generator as in claim 138,
comprising further methods for a. comparison of normalized
parameters such as data content values; and meta-data index
properties provided by direct Intelligent Object-to-Intelligent
Object; and iPool-to-iPool information interchange provided via
addressing including at least one or some combination of vector
subset pointing; meta-data index addressing; and activated by such
as query submissions, user preference rules and/or viewing
instructions; for b. organization of Intelligent Object and iPools,
utilizing methods and processes including assembly; ranking;
classification; and/or tabulation of Intelligent Objects.
141. An aggregate real-time significance generator as in claim 138,
comprised as a module within an information technology platform for
Intelligent Objects.
142. An aggregate real-time significance generator as in claim 138,
comprised as a module within an information technology platform
using object-oriented data structures.
143. An aggregate real-time significance generator interface
(aRSGi) providing conventions used to enable communication between
components and optionally other interfaces, comprising automated
and/or interactive methods for a. bi-directional information
interchange with components and access interfaces such as the
following iPool-to-iPool query component; Intelligent Object Pool
access interfaces; data Pool; iPools; Intelligent Object Handler;
and Intelligent Molecular Objects; b. interchange of information
required for and/or provided by comprised methods and processes for
significance generation and optimization; and c. provision of
real-time significance generator component activity information to
access interfaces and components as required for synchronization,
recordation, auditing and validation of said information
interchange and activity.
144. An aggregate real-time significance generator interface as in
claim 143, comprising further methods for detection and extraction
of information required for customization and/or optimization of
processes for significance detection and generation.
145. An aggregate real-time significance generator interface as in
claim 143, comprising further methods for interchange of
information required for and/or provided by significance
generation, said information comprising nested vector lookup tables
and meta-data index information such as data content information;
data attribute information; data relationship information;
protocols; ontologies; annotations; and other data-enabling
information.
146. An aggregate real-time significance generator interface as in
claim 143, comprising further methods for bi-directional
interchange of data content accessed via vector pointers as
required for and/or provided by meta-data index generation methods
and processes.
147. An aggregate real-time significance generator interface as in
claim 143, comprised within an information technology platform
using Intelligent Objects.
148. An aggregate real-time significance generator interface as in
claim 143, further comprised within an information technology
platform using object-oriented data structures other than
Intelligent Objects.
149. A result aggregation engine component (RAE), for aggregation
of significant results of queries or analytical processing applied
to any content of the Intelligent Object Pool, comprising methods
for a. bi-directional information interchange with components and
access interfaces including at least one or some combination of the
following unified presentation layer; result aggregation engine
interface; and pool content access components; and b. aggregation
of significant results provided by an aggregate significance
generator module and/or other application, module, component and
access interface according to requests for result aggregation
submitted via automated; and/or user-directed queries; commands;
processing requests; alerts; updates; and reports.
150. A result aggregation engine component as in claim 149, further
comprising methods for a. generation of aggregated, customized,
assembled, ranked, validated, and/or otherwise classified meta-data
profiles; and b. generation of aggregated, customized, assembled,
ranked, validated, and/or otherwise classified meta-data outputs
and updates such as for iPool definition updates; iPool
relationship definitions; Intelligent Object definition updates;
and Intelligent Object relationship definitions pertaining to
Intelligent Object and iPool properties, values and
relationships
151. A result aggregation engine component as in claim 149, further
comprising methods for generation of aggregated and otherwise
organized results for presentation and reporting via an external
Intelligent Object Handler.
152. A result aggregation engine component as in claim 149,
comprised as a module within an information technology platform for
Intelligent Objects.
153. A result aggregation engine component as in claim 149,
comprised as a module within an information technology platform
using object-oriented data structures.
154. A result aggregation engine interface (aRAEi) providing
conventions used to enable communication between components and
optionally other interfaces, comprising automated and/or
interactive methods for a. bi-directional information interchange
with components and access interfaces such as the following
iPool-to-iPool query component; Intelligent Object Pool access
interfaces; data Pool; iPools; Intelligent Object Handler; and
Intelligent Molecular Objects; b. interchange of information
required for and/or provided by comprised methods and processes for
result aggregation and optimization; and c. provision of real-time
result aggregation activity information to access interfaces and
components as required for synchronization, recordation, auditing
and validation of said information interchange and activity.
155. A result aggregation engine interface as in claim 154,
comprising further methods for detection and extraction of
information required for customization and/or optimization of
processes for result detection and aggregation.
156. A result aggregation engine interface as in claim 154,
comprising further methods for interchange of information required
for and/or provided by result aggregation, said information
comprising nested vector lookup tables and meta-data index
information such as data content information; data attribute
information; data relationship information; protocols; ontologies;
annotations; and other data-enabling information.
157. A result aggregation engine interface as in claim 154,
comprising further methods for bi-directional interchange of data
content accessed via vector pointers as required for and/or
provided by result aggregation methods and processes.
158. A result aggregation engine interface as in claim 154,
comprised within an information technology platform using
Intelligent Objects.
159. A result aggregation engine interface as in claim 154, further
comprised within an information technology platform using
object-oriented data structures other than Intelligent Objects.
160. An IMO Zoom component (IMO-Z) for interactive user viewing,
comprising methods for a. bi-directional information interchange
with components and access interfaces including at least one or
some combination of the following unified presentation layer; IMO
Zoom interface; and pool content access components; b. accessing
and routing of graphical viewing information for user viewing and
interactivity.
161. An IMO Zoom component as in claim 160, further comprising
methods for a. extraction of graphical viewing information; b.
processing of graphical viewing information; and c. addressing of
graphical viewing information; i. said graphical viewing
information comprising at least one or some combination of
thumbnail views, expanded or "zoomed" views, data relationship
overviews; self-organizing maps; similarity clustering;
dendrograms; charts; tables; and/or additional graphical
representations.
162. An IMO Zoom component as in claim 160, further comprising
methods for a. activation of thumbnail viewing of such as
Intelligent Object data content; iPool data contents; according to
correspondence of meta-data or data content attributes to automated
and/or user-directed queries and/or commands.
163. An IMO Zoom component as in claim 160, further comprising
methods for a. provision of user-directed and/or automated
graphical pre-sorting and sorting of such as said Intelligent
Objects according to correspondence of data content attributes to
automated and/or user-directed queries and/or commands.
164. An IMO Zoom component as in claim 160, comprised as a module
within an information technology platform for Intelligent
Objects.
165. An IMO Zoom component as in claim 160, comprised as a module
within an information technology platform using object-oriented
data structures.
166. An IMO Zoom interface (IMO-Zi) providing conventions used to
enable communication between components and optionally other
interfaces, comprising automated and/or interactive methods for a.
bi-directional information interchange with components and access
interfaces including at least one or some combination of the
following IMO Zoom component; Intelligent Object Pool access
interfaces; Intelligent Object Handler; and Intelligent Molecular
Objects; b. detection of graphical content provided by said
Intelligent Objects and their data content; Intelligent Object
Pool; iPools; components; and access interfaces; c. accessing and
routing of graphic information via such as vectorized data field
pointers; and meta-data index definitions and linking provided by
IMO Zoom viewing processes; and provision of access control for
information interchange to and from said Intelligent Objects,
Intelligent Object Pool, iPools, components and interfaces.
167. An IMO Zoom interface as in claim 166, further comprising
automated and/or interactive methods for a. synchronization of said
viewing information interchange and activity b. recordation of said
viewing information interchange and activity; c. auditing of said
viewing information interchange and activity; d. validation of said
viewing information interchange and activity.
168. An IMO Zoom interface as in claim 166, comprised within an
information technology platform using Intelligent Objects.
169. An IMO Zoom interface as in claim 166, further comprised
within an information technology platform using object-oriented
data structures other than Intelligent Objects.
170. A distributed learning engine component (DLE), for automated
and/or interactive computer learning in a distributed data and
applications environment, comprising methods for a. bi-directional
information interchange with components and access interfaces
including at least one or some combination of the following unified
presentation layer; distributed learning engine interface; and pool
content access components. b. application of automated and/or
interactive learning processes including at least one or some
combination of i. simulation; prediction, hypothesis generation,
testing, result assessment and comparison; interactive result
optimization and feedback; ii. data and user workspace definition;
assessment; organization; customization; iii. data and user
workspace relationship definition; assessment; organization;
customization; and/or iv. optimization of accessing, routing and
processing protocols for user workspaces; Intelligent Objects;
Intelligent Object Pools; and iPools.
171. A distributed learning engine component as in claim 170,
comprising further methods for interactive application of
protocols, functions and algorithms for learning utilizing
including at least one or some combination of user queries;
automated queries; customized sets of queries; data objects such as
the Intelligent Object; data handlers such as the Intelligent
Object Handler; data pools such as the Intelligent Object Pool;
iPools; the global data entity; and/or data subsets therein as a
data resource.
172. A distributed learning engine component as in claim 170,
comprising further methods for a. application of a comprised set of
pool content access definitions, which may include but are not
limited to the following cross-reference clustering; Boolean
network algorithms; Bayesian network algorithms; neural network
algorithms; iterative learning algorithms; time series analysis
algorithms; pattern matching algorithms; structure matching
algorithms; and rule induction algorithms; to enable b. at least
one or more of the following automated, dynamic and/or interactive
processes for iterative refinement; significance detection;
tabulation; validation; ranking; assembly; and/or other forms of
distributed learning.
173. A distributed learning engine component as in claim 170,
comprising further methods for provision of said learning processes
within a local environment, such as on a single computer containing
meaningfully related data and analytical applications.
174. A distributed learning engine component as in claim 170,
comprised as a module within an information technology platform for
Intelligent Objects.
175. A distributed learning engine component as in claim 170,
comprised as a module within an information technology platform
using object-oriented data structures.
176. A distributed learning engine interface (DLEi) providing
conventions used to enable communication between components and
optionally other interfaces, comprising automated and/or
interactive methods for a. bi-directional information interchange
with components and access interfaces such as the following
iPool-to-iPool query component; Intelligent Object Pool access
interfaces; data Pool; iPools; Intelligent Object Handler; and
Intelligent Molecular Objects; b. interchange of information
required for and/or provided by comprised methods and processes for
distributed learning and optimization; and c. provision of
real-time distributed learning component activity information to
access interfaces and components as required for synchronization,
recordation, auditing and validation of said information
interchange and activity.
177. A distributed learning engine interface as in claim 176,
comprising further methods for detection and extraction of
information required for customization and/or optimization of
processes for distributed learning.
178. A distributed learning engine interface as in claim 176,
comprising further methods for interchange of information required
for and/or provided by distributed learning, said information
comprising nested data content vector lookup tables and meta-data
index information such as data content information; data attribute
information; data relationship information; protocols; ontologies;
annotations; and other data-enabling information.
179. A distributed learning engine interface as in claim 176,
comprising further methods for bi-directional interchange of data
content accessed via vector pointers as required for and/or
provided by distributed learning methods and processes.
180. A distributed learning engine interface as in claim 176,
comprised within an information technology platform using
Intelligent Objects.
181. A distributed learning engine interface as in claim 176,
further comprised within an information technology platform using
object-oriented data structures other than Intelligent Objects.
182. A knowledge extraction engine component (KEE), for automated
and/or interactive knowledge extraction, comprising methods for a.
bi-directional information interchange with components and access
interfaces including at least one or some combination of the
following unified presentation layer; knowledge extraction engine
interface; and pool content access components; b. application of
automated and/or interactive knowledge extraction processes
including at least one or some combination of i. active data
attribute searching, detection and extraction; ii. similarity and
significance assessment and comparison; iii. hypothesis generation;
iv. report generation; v. interactive result optimization and
feedback; vi. data, annotation and user workspace definition;
assessment; organization and customization; and vii. data,
annotation and user workspace relationship definition; assessment;
organization; and customization.
183. A knowledge extraction engine component as in claim 182,
further comprising methods for a. application of a comprised set of
pool content access definitions, which may include but are not
limited to the following cross-reference clustering; Boolean
network algorithms; Bayesian network algorithms; neural network
algorithms; iterative learning algorithms; time series analysis
algorithms; pattern matching algorithms; structure matching
algorithms; and rule induction algorithms; to enable b. at least
one or more of the following automated, dynamic and/or interactive
processes for iterative refinement; significance detection;
tabulation; validation; ranking; assembly; and/or other forms of
knowledge extraction.
184. A knowledge extraction engine component as in claim 182,
further comprising methods for interactive application of
protocols, functions and algorithms for knowledge extraction
utilizing including at least one or some combination of user
queries; automated queries; customized sets of queries; data
objects such as the Intelligent Object; data handlers such as the
Intelligent Object Handler; data pools such as the Intelligent
Object Pool; iPools; the global data entity; and/or data subsets
therein as a data resource.
185. A knowledge extraction engine component as in claim 182,
comprised as a module within an information technology platform for
Intelligent Objects.
186. A knowledge extraction engine component as in claim 182,
comprised as a module within an information technology platform
using object-oriented data structures.
187. A knowledge extraction engine interface (KEEi) providing
conventions used to enable communication between components and
optionally other interfaces, comprising automated and/or
interactive methods for a. bi-directional information interchange
with components and access interfaces such as the following
iPool-to-iPool query component; Intelligent Object Pool access
interfaces; data Pool; iPools; Intelligent Object Handler; and
Intelligent Molecular Objects; b. interchange of information
required for and/or provided by comprised methods and processes for
knowledge extraction and optimization; and c. provision of
real-time knowledge extraction activity information to access
interfaces and components as required for synchronization,
recordation, auditing and validation of said information
interchange and activity.
188. A knowledge extraction engine interface as in claim 187,
comprising further methods for detection and extraction of
information required for customization and/or optimization of
processes for knowledge extraction.
189. A knowledge extraction engine interface as in claim 187,
comprising further methods for interchange of information required
for and/or provided by knowledge extraction, said information
comprising nested data content vector lookup tables and meta-data
index information such as data content information; data attribute
information; data relationship information; protocols; ontologies;
annotations; and other data-enabling information.
190. A knowledge extraction engine interface as in claim 187,
comprising further methods for bi-directional interchange of data
content accessed via vector pointers as required for and/or
provided by knowledge extraction methods and processes.
191. A knowledge extraction engine interface as in claim 187,
comprised within an information technology platform using
Intelligent Objects.
192. A knowledge extraction engine interface as in claim 187,
further comprised within an information technology platform using
object-oriented data structures other than Intelligent Objects.
193. A system comprising 1. an object pool for an information
technology platform and data object data architecture; 2. a set of
pool boundary protocols for provision of data security, exchange
and integrity; 3. a set of meta-data index definitions to enable
definition and linking of multiple dimensions of data annotation
and attributes defined and implemented at levels of individual
Intelligent Objects as well as data set or subset iPools; 4. a set
of pool content access definitions, comprising components and
access interfaces which provide methods for Pool and iPool
accessing and processing; 5. an iPool security authentication
component (iPSA) for provision and implementation of Intelligent
Object, Pool and iPool security definitions, authentication and
access permissions; 6. an iPool security authentication interface
(iPSAi) providing conventions used to enable communication between
components and optionally other interfaces; 7. an iPool
availability monitoring component (iPAM) for provision and
implementation of availability, access and addressing information;
8. an iPool availability monitoring interface (iPAMi) providing
conventions used to enable communication between components and
optionally other interfaces; 9. an iPool exchange protocol
component (iPEP), for data ownership and data exchange definition
and management; 10. an iPool exchange protocol interface (iPEPi)
providing conventions used to enable communication between
components and optionally other interfaces; 11. an object integrity
assessment component (OIA) for detection and verification of data
integrity; 12. an object integrity assessment interface (iOIAi)
providing conventions used to enable communication between
components and optionally other interfaces; 13. an iPool integrity
assessment component (iPIA), for detection and verification of
iPool integrity; 14. an iPool integrity assessment interface
(iPIAi) providing conventions used to enable communication between
components and optionally other interfaces; 15. an iPool meta-data
index component (iMDX), for generation and provision of iPool
meta-data information; 16. an iPool meta-data index interface
(iMDXi) providing conventions used to enable communication between
components and optionally other interfaces; 17. a real-time
meta-data linking component (RML), for dynamic linking of
meta-data; 18. a real-time meta-data linking interface (RMLi)
providing conventions used to enable communication between
components and optionally other interfaces; 19. an Intelligent
Object-to-Intelligent Object query component (OQM) for meta-data
definition of data interrelationships on the individual data object
level; 20. an Intelligent Object-to-Intelligent Object meta-data
query interface (OQMi) providing conventions used to enable
communication between components and optionally other interfaces;
21. an iPool-to-iPool query component (PPQ) for meta-data
definition of data interrelationships on the iPool level; 22. an
iPool-to-iPool meta-data query interface (OQMi) providing
conventions used to enable communication between components and
optionally other interfaces; 23. an aggregate meta-data index
generator component (aMDX), for generating meta-data indices based
upon aggregated information and processing results; 24. an
aggregate meta-data index generator interface (aMDXi) providing
conventions used to enable communication between components and
optionally other interfaces; 25. an aggregate real-time
significance generator component (aRSG), for comparison;
correlation; significance detection; and significance generation of
relationships between Intelligent Object data and between iPool
subsets; 26. an aggregate real-time significance generator
interface (aRSGi) providing conventions used to enable
communication between components and optionally other interfaces;
27. a result aggregation engine component (RAE), for aggregation of
significant results of queries or analytical processing applied to
any content of the Intelligent Object Pool; 28. a result
aggregation engine interface (aRAEi) providing conventions used to
enable communication between components and optionally other
interfaces; 29. an IMO Zoom component (IMO-Z) for interactive user
viewing; 30. an IMO Zoom interface (IMO-Zi) providing conventions
used to enable communication between components and optionally
other interfaces; 31. a distributed learning engine component
(DLE), for automated and/or interactive computer learning in a
distributed data and applications environment; 32. a distributed
learning engine interface (DLEi) providing conventions used to
enable communication between components and optionally other
interfaces; 33. a knowledge extraction engine component (KEE), for
automated and/or interactive knowledge extraction; and 34. a
knowledge extraction engine interface (KEEi) providing conventions
used to enable communication between components and optionally
other interfaces.
Description
RELATED APPLICATIONS
[0001] Priority is hereby claimed under 35 U.S.C. 120 and/or 35
U.S.C. 119(e) to the following United States Provisional and
Utility Patent Applications, each of which is hereby incorporated
by reference:
[0002] Utility U.S. patent application Ser. No. ______ (Attorney
Docket No. A-70134/RMA) filed Dec. 6, 2001 and entitled Data Pool
Architecture, System, And Method For Intelligent Object Data In
Heterogeneous Data Environments;
[0003] Utility U.S. patent application Ser. No. ______ (Attorney
Docket No. A-70135/RMA) filed Dec. 6, 2001 and entitled Intelligent
Molecular Object Data Structure and Method for Application in
Heterogeneous Data Environments with High Data Density and Dynamic
Application Needs;
[0004] Utility U.S. patent application Ser. No. ______ (Attorney
Docket No. A-70136/RMA) filed Dec. 6, 2001 and entitled Intelligent
Object Handling Device and Method for Intelligent Object Data in
Heterogeneous Data Environments with High Data Density and Dynamic
Application Needs;
[0005] Utility U.S. patent application Ser. No. ______ (Attorney
Docket No. A-70310/RMA) filed Dec. 6, 2001 and entitled System,
Method, Software Architecture, And Business Model For An
Intelligent Object Based Information Technology Platform;
[0006] U.S. Provisional Application Serial No. 60/254,063 filed
Dec. 6, 2000 entitled Data Pool Architecture for Intelligent
Molecular Object Data in Heterogeneous Data Environments with High
Data Density and Dynamic Application Needs;
[0007] U.S. Provisional Application Serial No. 60/254,062 filed
Dec. 6, 2000 entitled Intelligent Molecular Object Data for
Heterogeneous Data Environments with High Data Density and Dynamic
Application Needs;
[0008] U.S. Provisional Application Serial No. 60/254,064 filed
Dec. 6, 2000 entitled Handling Device for Intelligent Molecular
Object Data in Heterogeneous Data Environments with High Data
Density and Dynamic Application Needs;
[0009] U.S. Provisional Application Serial No. 60/259,050 filed
Dec. 29, 2000 entitled Object State Engine for Intelligent
Molecular Object Data Technology;
[0010] U.S. Provisional Application Serial No. 60/264,238 filed
Jan. 25, 2001 entitled Object Translation Engine Interface For
Intelligent Molecular Object Data;
[0011] U.S. Provisional Application Serial No. 60/266,957 filed
Feb. 6, 2001 entitled System, Method, Software Architecture and
Business Model for an Intelligent Molecular Object Based
Information Technology Platform;
[0012] U.S. Provisional Application Serial No. 60/276,711 filed
Mar. 16, 2001 entitled Application Translation Interface For
Intelligent Molecular Object Data In Heterogeneous Data
Environments With Dynamic Application Needs;
[0013] U.S. Provisional Application Serial No. 60/282,656 filed
Apr. 9, 2001 entitled Result Generation Interface For Intelligent
Molecular Object Data In Heterogeneous Data Environments With
Dynamic Application Needs;
[0014] U.S. Provisional Application Serial No. 60/282,658 filed
Apr. 9, 2001 entitled Knowledge Extraction Engine For Intelligent
Object Data In Heterogeneous Data Environments With Dynamic
Application Needs;
[0015] U.S. Provisional Application Serial No. 60/282,654 filed
Apr. 9, 2001 entitled Result Aggregation Engine For Intelligent
Object Data In Heterogeneous Data Environments With Dynamic
Application Needs;
[0016] U.S. Provisional Application Serial No. 60/282,657 filed
Apr. 9, 2001 entitled Automated Applications Assembly Within
Intelligent Object Data Architecture For Heterogeneous Data
Environments With Dynamic Application Needs;
[0017] U.S. Provisional Application Serial No. 60/282,655 filed
Apr. 9, 2001 entitled System, Method And Business Model For
Productivity In Heterogeneous Data Environments;
[0018] U.S. Provisional Application Serial No. 60/282,979 filed
Apr. 10, 2001 entitled Legacy Synchronization Interface For
Intelligent Molecular Object Data In Heterogeneous Data
Environments With Dynamic Application Needs;
[0019] U.S. Provisional Application Serial No. 60/282,989 filed
Apr. 10, 2001 entitled Object Query Interface For Intelligent
Molecular Object Data In Heterogeneous Data Environments With
Dynamic Application Needs;
[0020] U.S. Provisional Application Serial No. 60/282,991 filed
Apr. 10, 2001 entitled Distributed Learning Engine For Intelligent
Molecular Object Data In Heterogeneous Data Environments With
Dynamic Application Needs; and
[0021] U.S. Provisional Application Serial No. 60/282,990 filed
Apr. 10, 2001 entitled Object Normalization For Intelligent
Molecular Object Data In Heterogeneous Data Environments With
Dynamic Application Needs;
[0022] each of which U.S. utility and U.S. provisional patent
application is hereby incorporated by reference in its
entirety.
FIELD OF INVENTION
[0023] This invention pertains generally to system, method,
computer program product, data structure and architecture, data
management, and software architecture; and more particularly to
system, method, computer program product, and data structure and
architecture, data management, and software architecture in the
life sciences, biotechnology, therapeutic diagnostic and
intervention, pharmaceuticals, and bioinformatics, also extendable
to other scientific, business and information-oriented application
domains.
BACKGROUND
[0024] As demand for effective Information Technology (IT) software
to provide global data access and integrated scientific and
business solutions has grown, significant challenges have become
evident. A central problem poses access, integration, and
utilization of large amounts of new and valuable information
generated in each of the major industries. Lack of unified, global,
real-time or near-real-time data access and analysis is detrimental
to crucial business processes, which include new product discovery,
product development, decision-making, product testing and
validation, and product time-to-market. Additionally, the
importance of functionally integrating multiple dimensions of
heterogeneous data in the field, such as protein expression data,
chemical structure data, bioassay data and clinical text data, is
recognized. (See for example, Lin, D., et al.: American
Genomic/Proteomic Technology (2001) 1 (1): 38-46.)
[0025] With the completion of the sequence of the human genome and
the continued effort in understanding protein expression in the
life sciences, a wealth of new genes are being discovered that will
have potential as targets for therapeutic intervention. As a result
of this new information, however, Biotechnology and Pharmaceutical
companies are drowning in a flood of data. In the Life Sciences
alone, approximately one Terabyte of data is generated per company
and day, of which currently the vast majority is unutilized for
several reasons.
[0026] Data are contained in diversified system environments using
different formats, heterogeneous databases and have been analyzed
using different applications. These applications may each apply
different processing to those data. Competitive software, based on
proprietary platforms for network and applications analysis, have
utilized data platform technologies such as SQL with open database
connectivity (ODBC), component object model (COM), Object Linking
and Embedding (OLE) and/or proprietary applications for analysis as
evidenced in issued patents, such as for example patents issued or
assigned to such companies as Sybase, Kodak, IBM, and Cellomics in
U.S. Pat. Nos. 6,161,148, 6,132,969, 5,989,835, and 5,784,294, for
data management and analysis, each of which patents are hereby
incorporated by reference.
[0027] Because of this diversity, despite the fact that the
seamless integration of public, legacy and new data is
fundamentally important and/or crucial to efficient drug discovery
and life science research, current data mining tools,
methodologies, and technologies cannot handle all data and analyze
their functional relationships simultaneously or in an acceptable
time frame. There is a significant lack of data handling methods,
particularly data handling methods that can utilize these data in a
secure, manageable way. Security is important in at least many
situations so as to prevent other competitive investigators from
taking advantage of another's investigative strategies, results or
queries. The shortcomings of these conventional tools,
methodologies, and technologies are evident within heterogeneous
software and hardware environments with global data resources.
Despite the fact that the seamless integration of public, legacy
and new data is crucial to efficient research (particularly in the
life sciences), product discovery (such as for example drug, or
treatment regime discovery) and distribution, current data mining
tools, methods, and technologies cannot handle or validate all (or
even a reasonable portion of) diverse data simultaneously or within
an acceptable time frame.
[0028] With the expansion of high numbers of dense data in a global
environment, user queries often require costly massive parallel or
other supercomputer-oriented processing in the form of mainframe
computers and/or cluster servers with various types of network
integration software pieced together for translation and access
functionality as evidenced by such companies as NetGenics, IBM and
ChannelPoint (see for example U.S. Pat. Nos. 6,125,383, 6,078,924,
6,141,660, and 6,148,298, each of which is incorporated by
reference herein); packaged in some manner (e.g. Java, CORBA,
"wrapping", XML) and networked supercomputing hardware as evidenced
by such companies as IBM, Compaq and others in issued patents (see
for example, U.S. Pat. Nos. 6,041,398, and 5,842,031, each of which
is incorporated by reference herein). Even with these expensive
software and hardware infrastructures, significant time-delays in
result generation remain the norm.
[0029] In part due to the flood of data and for other reasons as
well, there is typically a significant redundancy within the data,
making queries more time consuming and less efficient in their
results. Tools are not yet in place which can effectively detect
data redundancy over heterogeneous data types and network
environments, especially of data content subsets within data files,
and provide ranked and validated multiple addressing and/or
flagging and/or removal of such redundant data. The flood of new
and legacy data results in a significant redundancy within the data
making queries more time consuming and less efficient in their
results.
[0030] With the advent of distinct differentiations in the field of
genomics, proteomics, bioinformatics, and other information
intensive fields, and the need for informed decision making in the
life sciences and other disciplines, the state of object data is
crucial for their overall validation and weight in complex,
multi-disciplinary queries. This is even more important due to
inter-dependencies of a variety of data at different states.
Furthermore, because biological data frequently describe a
"snapshot" representing a unique moment in time of complex
processes at a defined state of the organism, data obtained at any
time refer to this unique phase of metabolism. Thus, in order to
account for meaningful comparison, only data in similar states can
or should be utilized. Therefore, there is a growing need for an
object data state processing engine, which allows one to
continuously monitor, govern, validate and update the data state
based on any activities of data or data objects in real-time or
near-real-time, or even within an acceptable time frame. Currently,
these capabilities are not broadly available for network data
structures, and they are not available for data structures
integrating heterogeneous data over distributed network
environments.
[0031] Furthermore, these differentiations in the field of
genomics, proteomics, bioinformatics, and other fields and the need
for informed decision making in the life sciences, access to all
data is crucial for overall validation and weight in complex,
multi-disciplinary queries. This is even more important due to
inter-dependencies of a variety of data at different states. The
current individual data translation approach does not support these
needs. Most of these problems require real-time processing;
automated, instant data translation of data from different sources;
and integration of heterogeneous applications and analytical
components for their solutions. Data contained in diversified
system environments may use different formats, heterogeneous
databases and different applications, each of which may apply
different processing to those data. Therefore, there is a growing
and unmet need for an automated object data translation
methodologies and/or systems and structures or engines, which
allows for bi-directional translation of multidimensional data from
various sources into intelligent molecular objects in real-time.
Currently, data translation processes between different data types
are time-consuming and require administrative exchange of
information on data structures application programming interfaces
(API's) and other dependencies. For example, some of the latest
technologies such as Incellico's CELL, IBM's DiscoveryLink,
Netgenic's Synergy and Tripos' MetaLayer solutions (see Haas et al
2001) have such requirements. These processes, although available
and used, have a number of shortcomings. Despite the fact that the
rapid seamless integration of public, legacy and newly emerging
data is crucial to efficient drug discovery and life science
research, unique "wrappers" or translation layers must currently be
designed and programmed in order to translate each of those data
sets correctly, and even with this manual integration, multiple
data types and dimensions of data interdependencies are not made
available, or "functionally integrated" for detailed qualitative
and quantitative comparison and analysis across data types and
dimensions. These solutions currently require significant effort
and resources in both, software development and data processing,
and the need for improvements such as those offered by this
invention are recognized.
[0032] An additional consideration, which is prohibitive to change
towards a more homogeneous infrastructure is the missing of
fluently definable object representation definition protocols to
prepare and present data objects for unified, functionally
integrated interaction within heterogeneous environments. There is
a lack of defined sets of user interaction and environment
definition protocols needed to provide means for intelligent data
mining and optimization of multidimensional analysis to achieve
validated solutions. Data currently are interacted with and
presented in diverse user interfaces with dedicated, unique
features and protocols, preventing universal, unified user access.
Thus, a homogeneous, unified presentation such as a flexibly
network-enabled graphical user interface, which integrates
components from diverse applications and laboratory systems
environments over a variety of connections and protocols, is highly
desirable, but heretofore non-existent for real-time data access
and analysis utilizing diverse applications and data.
[0033] Finally, an additional consideration, which is prohibitive
to change towards a homogenous data and applications
infrastructure, is cost. The cost to bring legacy systems up to
date, to retool a company's intranet-based software systems, to
create a unified environment utilizing existing software products
and tools such as CORBA, JAVA, XML, SQL and classic data
warehousing techniques, can be time-consuming and expensive.
Conventional practices require retooling and/or translating at both
application and hardware layers, as evidenced by the efforts of
such companies as Unisys and IBM (See for example, U.S. Pat. Nos.
6,038,393 and 5,634,015), and may be prohibitively expensive for
smaller and medium-sized companies or groups wishing to access this
type of functionality.
[0034] Because of the constraints outlined above, it is nearly
impossible to extract useful, functionally integrated information
from the entity of data within reasonable computing time and
efforts. For these reasons, the development of comprising a unique
architecture and system, comprising a unique application framework,
data structure, and database structure, is unavailable and needed
to overcome these obstacles (see for example, Hobbs, D. W.,
Chemical and Engineering News, (2001) 79 (13): 266, incorporated by
reference herein).
[0035] Therefore, there remains a need for a system, method,
computer program and computer program product, data structure,
architecture, and application framework that overcome these
problems and limitations.
LITERATURE
[0036] Andreoli, J-M., In: Agha G., Wegner P., Yonezawa A.(eds.):
Research Directions in Concurrent Object-Oriented Programming, MIT
Press (1993): 260-263; Bertino E., Urban S., Rundensteiner E.
A.(eds.): Theory and Practice of Object Systems (1999) 5 (3):
125-197; Chaudhri A. B., McCann J. A., Osmon P.: Theory and
Practice of Object Systems (1999) 5 (4): 263-279; Cai D., McTear M.
F., McClean S. I.: International Journal of Intelligent Systems
(2000): 15 (8): 745-761; Hert C. A., Jacob E. K., Dawson P.:
Journal of the American Society for Information Science (2000) 51
(11): 971-988; Hobbs, D. W., Chemical and Engineering News. (2001)
79 (13): 266; Lin, D., et al.: American Genomic/Proteomic
Technology (2001) 1 (1): 38-46; Williams R. J., In: Miller W.,
Thomas I., Sutton R. S., Werbos, P. J. (eds.): Neural Networks for
Control, MIT Press (1990): 97-114; Wilson G. V., Lu P. (eds.):
Parallel Programming and C++, MIT Press (1996): 257-280.; C. N.
Lauro, G. Giordano, R. Verde: Applied Stochastic Models and Data
Analysis: A multidimensional approach to conjoint analysis (1998)
14 (4): 265-274; Meyer, Bertrand: IEEE Computer. (1999) 32 (1):
139-140.; Chalmers, Mathew: Journal of the American Society for
Information Science. (1999) 50 (12): 1108-1118.; Teasley, Stephanie
and Steven Wolinsky: Science. (Jun. 22, 2001) 292:2254; Haas, L.
M., et al: IBM Systems Journal. (2001) 40 (2): 489-511.; Siepel,
A., et al: IBM Systems Journal. (2001) 40 (2): 570-591; and
Steiner, S. and Witzmann, F. A.. Electrophoresis. (2000) 21:
2099-2104; Haas, L. M., et al: IBM Systems Journal. (2001) 40 (2):
489-511; each of which publications are incorporated by
reference.
[0037] The following U.S. Pat. Nos. 5,596,744, 5,867,799,
5,745,895, 6,076,088, 5,706,453, 5,767,854, 6,035,300, 6,145,009,
5,664,066, 5,862,325, 6,016,495, 6,119,126, 6,088,717, 6,052,722,
6,064,382, 6,134,581, and 6,146,027; each of which publications are
incorporated by reference.
SUMMARY
[0038] The invention includes multiple system, structure,
apparatus, methodological, computer software/firmware, and business
or operating model elements. In one preferred embodiment, these
multiple and diverse elements are synergistically combined into a
commercial implementation; however, it will be understood that each
of the elements and/or aspects of the invention may be used
separately or in anyone of the possible combinations to provide
utility.
[0039] In one of these embodiments, the invention provides a novel
data structure referred to as an intelligent molecular object (IMO)
or for the sake of brevity an intelligent object. The phrases
intelligent molecular object and intelligent object are used
interchangeably in this description. Various embodiments of these
intelligent molecular objects (IMO) or intelligent objects are
described and may be used alone or in the context of other aspects
of the invention. One particularly advantageous embodiment of the
IMO includes both procedural (or active) and non-procedural
(non-active) components, where the non-procedural component has
features in common with (though not the same as) "data" in the
conventional sense and the procedural component couples additional
information with the "data" and adds functionality and capabilities
to a system or method utilizing the "data." The manner in which the
non-procedural component of the IMO differs from conventional data,
and the manner in which the IMO is a data structure but not a data
structure in the conventional sense are developed elsewhere in this
description. The IMO data structure (IMO) may be a local data
structure or a distributed data structure, though in its most
advantageous and functional form it will be a distributed data
structure.
[0040] In light of the above, it will be appreciated that in one
aspect, the invention provides an intelligent object data structure
(IMO) that comprises one or more of components, interfaces,
structures, definitions, and methods that takes any data content
(such as raw data or data including but not limited to data coupled
or associated with any valid type or structure) and turns the data
content into intelligent data. The intelligent data is thereby made
substantially or entirely capable of any one or combination of the
following: self-organizing, self-translating, capable of being
processed in a parallel manner, tracked, ranked according to
validation, and other features and capabilities as are described
elsewhere in this description. The intelligent data is also capable
of being functionally integrated, such as by mapping of data
content, extraction of meta-data for each individual data object,
and direct linking between objects and objects and
applications.
[0041] In another one of these embodiments, the invention provides
a novel applications handling system, architecture, and framework
referred to as the intelligent object handler (IOH). The IOH may be
analogized to a super-browser, that functionally integrates and
validates (ranks or flags according to validation status)
homogeneous and heterogeneous applications and data within a
unified, globally accessible framework over a variety of networks,
network protocols, client or access point hardware and operating
systems, and network systems (such as peer-to-peer, client-server,
ASP, and other system architecture and topologies as are known in
the art). In essence the IOH is hardware, operating system,
application program, and network protocol neutral or agnostic. In
one embodiment, the IOH applications handling framework comprises
processing components, interfaces, definitions, algorithms and
methods for functionally integrating and (optionally but desirably)
validating diverse applications and data. As with the IMO (and the
IOP described hereinafter) the IOH may be localized or
distributed.
[0042] In a third primary aspect, the invention provides
embodiments of intelligent object pools (IOP) which comprise a
second database structure in addition to the afore described IMO
data structure. Embodiments of the IOP are like a single global
database of whatever data a user or other entity is interested in,
that has been made accessible and compatible for analysis. (The
ability to perform meaningful analysis may generally depend on the
existence of meaningful relationships within the data and/or
analysis applications themselves.) The IOP creates a unified,
dynamically updated, data pool out of previously incompatible,
possibly distributed data, and provides viewing and interactivity
according to multiple, dynamically defined data content and
property dimensions. Embodiments of the IPO may be locally
configured or distributed, and may generally comprise one, more
than one, or all of processing components, interfaces, and their
comprised structures, definitions, algorithms and methods.
[0043] Each of the IMO, IOH, and IOP may typically be implemented
in computer program software having a set of procedures or
functional modules implemented as computer program instructions.
These computer software programs executing on either general
purpose computers or specialized processors having at least a
processing unit and memory form systems that provide connectivity
providing a user with access to data and other information via
known internet and intranet infrastructures.
[0044] While each of the IMO, IOP, and IOH advantageously support
the distributed and heterogeneous integration capabilities
described and provide particular advantages, non-distributed (e.g.
local) and non-heterogeneous (e.g. homogeneous) operation are
clearly supported and provide significant improvements over
conventional systems and methods.
[0045] In one particular aspect, the invention provides an
intelligent object (alternatively referred to as an intelligent
Molecular Object or IMO), which includes data structure and
methodological and procedural components and in at least one
embodiment is advantageously enabled in or as computer program
software. When implemented as computer program software executing
on or within processor and memory of one or more general purpose
computers, the inventive data structure, database structure, and
methodological and procedural components provide a system for data,
access, manipulation, and analysis.
[0046] At one level, the invention provides system, structure,
method, and computer program and computer program product for data
and user identification and status management; functional
integration of, and access to, potentially diverse data over a
variety of computing infrastructures; integration of multiple data
types and data dimensions for efficient and accurate
multidimensional, parallel queries and analyses for diversified
data content and dynamic applications needs in heterogeneous local
and/or networked computing and applications environments with
particular benefits and applicability to life sciences computing
and applications environments.
[0047] The data and user identification and status management
procedures and/or capabilities include: unified presentation and
global unique identification of the Intelligent Object;
identification, authentication and logging of users, sessions, and
data content activity locally and/or over networks; dynamic routing
of root object data, meta-data and data content locally and/or over
networks; and data status, data integrity and validation state
alerting and management.
[0048] The functional integration of, and access to, potentially
diverse data over a variety of computing infrastructures procedures
and/or capabilities include: functional content and attribute
definition for database and application access and routing;
automated raw data matrix and matrix structure definition;
automated translation of multiple data types and dimensions for
unified processing and analysis; and functional integration of
multiple data types and dimensions for unified processing and
analysis.
[0049] The integration of multiple data types and dimensions for
efficient and accurate multidimensional, parallel queries and
analyses includes procedures and/or capabilities for meta-data
indexing and query optimization; direct data-to-data information
exchange; vectorized exchange of information subsets; data-enabled
parallel processing; non-destructive cache-based processing;
graphical data preview and detailed viewing; automated functional
integration and launching of applications and activation of data
related to Intelligent Object data content; automated assembly of
applications and components for viewing and/or analysis relevant to
specified data types and contents; and custom text annotation,
linking and embedding of existing text. Each of these capabilities
and/or procedures may be applied to diversified data content and
dynamic applications needs in homogeneous and/or heterogeneous
local and/or networked computing and applications environments, and
find particular applicability and benefits to fife sciences
computing and applications environments.
[0050] The invention also provides methods and procedures for the
creation and modification of Intelligent Molecular Objects (or for
simplification "Intelligent Objects" or even more simply "Objects")
are provided which, upon user initiation, queries, data acquisition
protocols or data import requests, invoke the unique object
identifier property pane through a unified functional presentation
layer. The unique object identifier property pane assigns each new
data object a globally unique identification upon creation and
generates a minimum set of functional property panes within the
object, which account for unified viewing and processing. Once the
object's state recording is started, active identification for all
connections to and activities on the Intelligent Object are listed
within the unique object identifier property pane, containing a
real-time record of the entries. Methods and procedures for user
and session authentication, permission or denial for data access,
security and ownership management, highly selective data access and
routing, Intelligent Object handling and storage are immediately
provided.
[0051] An object root router component defines the origin of the
object within the network, directs storage of the object within the
database and reports the location of the object to the unique
object identifier property pane.
[0052] An interactive content routing component defines where data
content is located and where query-relevant content and/or results
will be directed within the network for analysis or presentation
and reports the location of the data content to the unique object
identifier property pane.
[0053] A status management component provides methods for data
status validation, logging, use-tracking, auditing,
synchronization, rollback enabled by said command history and
non-destructive vector processing, and other state management and
alerting protocols. The status management component communicates
with an external object state engine component to monitor data
integrity and to record the command history according to
G*P-compliant LIMS requirements (such as, for example, GLP, GCP,
GMP, ISO-9001, CDER, CFIR) within the object state engine (OSE)
property pane, where the information is updated and provided for
real-time or substantially real-time viewing. This information
includes one or more of detailed activity logging, such as data
acquisition state, calibration information, applied transformation
or analysis processes, local and remote access attempts, access
permission and denial, data integrity alerts, ranking status, and
regulatory validation states.
[0054] A raw data matrix property pane within the Intelligent
Object provides an overview of the full raw data content subset
including content attribute information, source location, data type
and comments regarding data content referred to by the Intelligent
Object, regardless of originating data type or structure.
[0055] A matrix structure descriptor component provides methods for
data field mapping of heterogeneous raw data to govern access to
individual data subsets (byte-level workspace assignment) and to
enable direct vectorized access to individual data fields.
[0056] A meta-data index property pane within the Intelligent
Object provides a viewer for automatically generated or
user-defined index information and brief meta-descriptions
("data-about-data") such as, but not limited to, specific data
functionality or relationships to other data or data
inter-dependencies based upon multi-parametric clustering, queries
or application of certain analytical tools or a combination thereof
to the data. This pane utilizes a meta-data index interface to
communicate with external processing engines to create an index of
descriptive data information and to provide this meta-data to the
object pane descriptor component, which integrates relevant pane
information for access and presentation. Additionally, the
meta-data index is used to aggregate and integrate results of
clustering and/or other data analyses and provides sets of rules to
optimize access and routing based upon dynamically established
query relationship trees regarding specific data functionality
and/or meta-data description. The meta-data index is also used to
rank parameters such as data quality, validation state,
significance, recency and accessibility to enable optimized access
and routing based on data type, topic and content attributes to
predefine analytical queries.
[0057] An object pane descriptor component compiles an overview of
the Intelligent Object property pane characteristics to provide
functional content and attribute definitions to access and route
data content and applications. Said object pane descriptor
component exchanges the information with components and access
interfaces to provide definitions required for dynamic addressing,
functional linking and vectorized access and routing of data
content and processing results.
[0058] The application translator link interacts with an external
data and applications handler, such as the Intelligent Object
Handler, and an object access manager component to provide a
dynamic list and interactive overview of applications, application
components and data resources. The application translation link
enables interactive linking and integration of the applications,
applications components and data resources, according to user
requirements and available resources.
[0059] The object access manager determines relevant property panes
and selectively directs their content for functional presentation
and access within a given application or database environment.
Additionally, the object access manager interacts with external
object translation engines to detect, define and address required
and/or available data sources and to direct access and routing
requests for specific data content to linked applications and/or
databases and to functionally integrate data content with a variety
of applications. The object access manager also provides
content-attribute based algorithms to enable applications
integration and inter-application communication.
[0060] An object query interface routes results of Boolean
comparisons and other algorithms applied to content attributes
according to its object pane descriptor relationships. This
component also passes aggregated results from object-to-object
direct information interchange to an external result aggregation
engine for further processing and relays significant query outcomes
back to the object pane descriptor property pane for presentation
to the user.
[0061] An object graph preview property pane is included as limited
resolution image/graphics viewer for quick graphical data review of
Intelligent Objects, and additionally provides linking to detailed
viewing as well as launching of content-specific analysis tools.
The object graph preview property pane also includes an object
graph preview processing component, which accounts for generation
of such a limited resolution image/graphics (sometimes referred to
as "thumbnail" images or graphics) from non-graphical raw data
content and which passes the image back to the object graph preview
property pane.
[0062] An optional text annotation property pane within the
Intelligent Object provides a location for customized text
annotations, referencing, definitions and integration of links to
external textual resources. An optional text annotation Interface,
which links external components or applications such as text
editors to allow for customization, formatting, reviewing and
processing of the information through external editors and which
allows to pass this information back to the text annotation
property pane and to provide integrated support for text mining
algorithms utilizing external distributed learning and/or knowledge
extraction engines.
[0063] In another particular aspect, the invention provides an
intelligent object handler (IOH), which includes methodological and
procedural elements and in at least one embodiment is
advantageously enabled in or as computer program software. When
implemented as computer program software executing on or within
processor and memory of one or more general purpose computers, the
methodological and procedural elements provide or support a system
for handling data. Methods and procedures are provided for numerous
data handling tasks in both homogeneous and heterogeneous data
environments, particularly those with high data density and dynamic
application needs. The data handling tasks include, but are not
limited to: generation of Intelligent Object data; unified
presentation of dynamically customizable functional menus and
interfaces such as for user definition, administration and security
protocols; secured user interaction, access and presentation based
on imported and/or defined user definition, administration and
security protocols; data object standardization and normalization;
definition of user interaction and computing environment protocols
required for data object translation, standardization, access and
routing; definition of data type access, translation, presentation
and routing protocols for functional data and applications
integration; definition of application and/or application
components and interface access, translation, presentation and
routing protocols for functional data and applications integration;
and provision of interactive, unified, functionality for data
acquisition, management, viewing and analysis.
[0064] In the object creation method and procedure, the Intelligent
Object generator extracts relevant data information, routes
real-time data from ongoing data acquisitions and transforms device
outputs and heterogeneous data types to Intelligent Object data.
Data content may be stored remotely from the corresponding
Intelligent Object, and both, data content as well as Intelligent
Objects (stored in "Intelligent Object Pools" or Pool subset
"iPools") may be stored locally or may be distributed across
heterogeneous data storage resources and networks. Components such
as the object standardization technique and the object
normalization engine standardize and normalize the data by
calibration according to standardized empirical criteria.
[0065] In the interactive user access and presentation method and
procedures, the unified presentation layer provides the web-enabled
graphical user interface that integrates the technology defined to
unify diverse applications, laboratory systems environments, and
Intelligent Object data at the graphic user interface layer. As an
example, in the security and access methods, the user menu
activates the user definition and administration shell and prompts
for user input regarding access privileges environments at login.
The master query component then presents security and access
protocols to the unified presentation layer and to the object state
engine for authentication and permits or denies access to begin
fielding user queries and commands for data acquisition, retrieval,
or analysis.
[0066] In the method and procedures for interactive, functionally
integrated data acquisition, management, viewing and analysis, user
interactivity at the front end is enabled by the unified
presentation layer, which is linked to defined processing
components and access interfaces.
[0067] In the environment definition method and procedures, the
application and database definition generator interface dynamically
detects application and database requirements and defines the
computing environment for the data type translator, the application
translation interface, and the application framework.
[0068] In the object definition method and procedures, the data
type translator defines the data type dependencies for the
Intelligent Object generator, the object translation engine and the
application framework component according to the applications and
database environment defined by the application/database definition
generator.
[0069] At the same time, in the functional integration method and
procedures, the Intelligent Object application framework provides
functional integration of components, access interfaces and
Intelligent Objects of the Intelligent Object Handler, to provide
fast, efficient, functionally integrated querying, viewing and
analysis. Components and interfaces such as the application and
database definition generator interface and the application
translation interface provide access and routing protocols to
heterogeneous applications and databases.
[0070] Additionally, in the method and procedures for functional
integration, the Intelligent Object Handler (IOH) enables
activities including for example, real-time or near-real-time
acquisition, management, viewing and analysis of Intelligent Object
data through the utilization of integrated meta-data tags and
pointers activated by the master query component and returned via
components and access interfaces to the master query interface for
presentation to the user.
[0071] Automated and/or user-directed interaction with external
applications, processing components, instruments and devices is
enabled by access interfaces including the master query interface,
direct instrument acquisition and control, legacy synchronization
interface, and report generation interface.
[0072] In a further particular aspect, the invention provides an
intelligent object pool (IOP). Methods, procedures, and structures
are provided for storage, management, processing and viewing of
Intelligent Molecular Objects (IMO) 200 (also referred to as
Intelligent Object or simply Objects) data within defined iPool
subsets. The methods and processes include Pool Boundary Protocol
definitions, Meta-data Query Definitions, and Pool Content Access
definitions.
[0073] The Pool Boundary Protocols enable utility including
security authentication, availability and access management, data
exchange, data and data set integrity assessment. In an exemplary
embodiment of the Pool Boundary Protocol methods, the iPool
Security Authentication module (iPSAc/iPSAi) authenticates iPool
data requests according to user login and Intelligent Object data
and/or iPool identification. Next, the iPool Availability Monitor
module (iPAMc/iPAMi) and iPool Exchange Protocol module
(iPEP/iPEPi) presents iPool relationships and availability to the
authenticated user. At the same time, the object integrity
assessment module (OIAc/OIAi) assesses Intelligent Object integrity
for security and quality assurance/quality control and the iPool
Integrity Assessment module (iPIAc/iPIAi) assesses data integrity
within defined iPools 155 for security and quality
assurance/quality control.
[0074] In an exemplary embodiment of the Meta-data Query methods,
the Real-time Meta-data Link module (RML) comprising a Real-time
Meta-data link component (RMLc) and a Real-time Meta-data link
interface (RMLi) provides for rapid relevant data access based on
direct linking of query parameters to Intelligent Object and iPool
Meta-data Index (MDX) content. Simultaneously, the Intelligent
Object-to-Intelligent Object (Object-to-Object) Query module
(OQMc/OQMi) provides for rapid query optimization based on
Intelligent Object and iPool intercommunication regarding query
parameters and Meta-data Index (MDX) content. Depending on query
parameters, an iPool-to-iPool Query module (PPQc/PPQi) may be
called, which provides for query optimization based on iPool data
intercommunication regarding query parameters and Meta-data Index
(MDX) content contained within more than one iPool.
[0075] In the Pool Content Access methods, the aggregate Real-time
Significance Generator module (aRSGc/aRSGi) provides for
significance detection based on provided definitions, equations and
results; interactive aggregation of reported Intelligent Object
attributes within the iPool; and on such as the correspondence of
the attributes to queries, processing parameters/or other automated
and/or user directed parameters. Next, the aggregate Meta-data
Index generator module (aMDXc/aMDXi) provides for meta-data index
generation for iPool sets of Intelligent Object meta-data, of
values defining aspects of the global Intelligent Object Pool or
accessed iPools 155 based on query parameters; data content,
function and relationships; and Meta-data Index (MDX) attribute
content. The comprised Result Aggregation Engine module component
and interface (RAEc/RAEi) enables results aggregation based on
Intelligent Object Meta-data and aggregate Meta-data information
and data values, and properties generated as results of automated
and/or user-directed queries, commands and applied processes and
methods. The Result Aggregation module provides information
required for Report Generation, Meta-data Index updating and the
like.
[0076] An object property-selective pre-sorting and viewing module
(IMO-Zc/IMO-Zi), "IMO Zoom" which organizes Intelligent Objects
based on their relationships within individual iPools 155, to allow
for real-time exclusion of irrelevant object data layers and
Intelligent Object relationship viewing.
BRIEF DESCRIPTION OF THE FIGURES
[0077] FIG. 1 shows diagrammatic representations of two embodiments
of an intelligent molecular object Information Technology (IMO IT)
Platform architecture and topology, depicting structural,
methodological, and functional relationships between framework,
engines, interfaces, and other components, where FIG. 1A shows a
first embodiment of the overall architecture and FIG. 1B shows an
alternative exemplary embodiment of the IMO IT architecture having
different localizations of components and interfaces, and focusing
on the integrated data and applications handling aspect of the IMO
IT Platform.
[0078] FIG. 2 is a diagrammatic representation of functional
relationships between instruments, applications, and intelligent
object pool (IOP) 204 and diverse databases within an embodiment of
the intelligent molecular object (IMO) architecture and
topology.
[0079] FIG. 3 is a diagrammatic representation of a typical
multi-user network collaborative research effort involving
geographically diverse data acquisition and query sites.
[0080] FIG. 4 is a diagrammatic representation in the form of a
procedure or flow chart showing typical data processing procedures
and flows within the IMO IT architecture to provide a relevant
real-time or near-real-time answer to a query.
[0081] FIG. 5 is a diagrammatic representation of an exemplary
system architecture or hardware configuration for the IMO(s), IOP,
and IOH and its enabling architecture.
[0082] FIG. 6 shows diagrammatic representations of several
embodiments of an intelligent object or intelligent molecular
object (IMO) 200, where FIG. 6A and FIG. 6B show alternative
exemplary embodiments of the IMO IT architecture having different
views and localizations of components and engines.
[0083] FIG. 7 shows diagrammatic representations of several
embodiments of an intelligent object pool (IOP) 204, where FIG. 7A
shows relationships and interactivity between the intelligent
object pool (IOP) and its components and access interfaces, to the
intelligent objects (IMOs) 200 provided by the IOP, and to an
external intelligent object handler (IOH) 202, and where FIG. 7B
shows an intelligent object pool (IOP) and a data subset intra-Pool
(iPool) 155.
[0084] FIG. 8 shows a diagrammatic representation of an embodiment
of an object handler or intelligent object handler (IOH).
[0085] FIG. 9 is a diagrammatic illustration in the form of a chart
showing an embodiment of distributed data resources, a Unified
Client data subset intra-Pool (iPool) 155, access, and exchange
relationships to the data resources, according to aspects of one
embodiment of the invention.
[0086] FIG. 10 is a diagrammatic illustration depicting a
representation of an embodiment of a process model for an exemplary
embodiment of the intelligent object pool (IOP) 204, including
relationships between legacy data, access protocols, and unified
object content and meta-data interaction.
[0087] FIG. 11 is a diagrammatic illustration depicting a
representation of an embodiment of the graphical user interface
window within the unified presentation layer displaying algorithms
included for clustering of intelligent object data within the
intelligent object pool (IOP) 204, as well as a number of other
property panes.
[0088] FIG. 12 is a diagrammatic illustration depicting an
embodiment of an interface iPool Zoom viewer for viewing iPool data
relationships, utilizing techniques that include dendrograms and
self-organizing maps (SOM).
[0089] FIG. 13 is a diagrammatic illustration depicting an iPool
Zoom interface and display screen image for viewing iPool data
relationships, utilizing in this example a Principal Components
Analysis (PCA) algorithm.
DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS
[0090] Having described some of the problems and limitations of
conventional systems, architectures, methods, procedures, and
computer programs and computer program products in the Background
section, we now direct some further attention to highlights of some
advantages structural, methodological, and operational features of
the invention as compared to such conventional systems and methods.
As some comparisons necessarily require or benefit from an
understanding of the invention, this comparison is not conveniently
provided in the Background section.
[0091] Some Problems and Limitations with Conventional Methods
[0092] Speed and efficiency for accessing and analysis of global
data remains a serious challenge in Life Science and biological
computing. Existing software methods have relied on prohibitively
expensive computing hardware to provide the heavy lifting required
for complex biological computing. Existing software often routes
entire raw data files for processing and generally require data
locking when shared files are accessed. Mass spectrometry data, for
example, may present files of 100 megabytes or more. Additionally,
existing techniques for data translation and integration require
"wrapping" software that further bloats programming and processing
overhead, with resulting performance hits.
[0093] To address this data-intensive processing, expensive
database back-ends, mainframe computers and/or cluster servers are
bundled with functional but inefficient network integration
software offered by such companies as Oracle, IBM
(DiscoveryLink/DB2) and Compaq (Alpha Systems). These hardware and
database back-end resources can range be very costly and even with
these massive hardware infrastructures, significant time-delays in
result generation remain the norm (Einstein, 2000). Clearly more
efficient access, integration and processing techniques, to reduce
hardware expense and to speed up results on any system, will be
valuable to the industry.
[0094] A "functionally integrated" computing environment, wherein
meaningfully related data may be directly compared and subjected to
unified processing and detailed analysis by potentially
heterogeneous applications, remains a goal for Life Science
computing. Existing software methods fail to provide for analysis
of complex, dynamic analysis utilizing "functionally integrated"
analysis of multiple relationships between globally available data,
utilizing heterogeneous applications. Existing data structures
offer limited attribute definitions, disparate and inflexible
annotation standards, and fail to dynamically define attributes for
unified processing in heterogeneous environments. Limitations such
as these restrict comparability of heterogeneous data and the
dimensionality for functional analysis and viewing.
[0095] Companies such as Array Genetics and Nonlinear Dynamics
provide tools for detailed analyses of specified structures,
activities and their multidimensional inter-relationships within
homogenous environments. However, the need to analyze data based on
more flexibly defined multidimensional dependencies using
heterogeneous data and applications is recognized. For example,
Cellomics has recently acquired software which provides the
multidimensional analytical functionality to "extract cellular
knowledge from multiparameter biological measurements integrated
with facets of chemical structure information" (Cellomics, 2001).
Another advanced software initiative, by the National Center for
Genome Research (NCGR), is currently attempting to meet these goals
in a limited manner by developing Integrated SYStems (ISYS)
software, that: promotes loose coupling of independent components
through a flexible, semistructured data model that supports
run-time association of attributes with objects, and allows
different components to maintain different "views" of the same
object (Seipel et al, 2001).
[0096] Like other competitive software, this model fails to provide
for data interoperability with diverse applications without manual
programming and processing-intensive "wrapping" for translation.
Competitive software designed to integrate diverse data generally
requires software wrapping to meet translation and interoperability
requirements, with resulting processing overhead and dependency on
additional human and hardware resources (e.g. DiscoveryLink, Java,
CORBA, XML). Even with software wrapping, direct interaction
between flexibly associated attributes of diverse data and between
this data and diverse applicastions, is limited. Although the goal
is recognized, the need remains for software capable of efficiently
supporting analysis of complex dynamic relationships, between
multiple data dependencies, from diverse global sources.
[0097] Detailed security and persistent data auditing, tracking,
regulatory validation and alerting remain a problem in global Life
Science computing. Existing software methods fail to provide
sufficient security and regulatory compliant validation for
management and of exchange sensitive biological information over
global networks. No company provides byte-level security and access
control and few provide the guaranteed raw data integrity required
to maintain patient confidentiality and clinical validation over
any network.
[0098] Leading software technology such as IBM's DB2/DiscoveryLink
technology and Oracle's 9i database, for example, fail to provide
security to the tightest level. These companies draw on structured
query language (SQL) techniques and provide their finest grained
security to the row level.
[0099] Virtual Private Database (VPD), an Oracle database feature,
offers fine-grained access control or row level security. It allows
multiple communities of users to share the same database, yet only
access the row(s) of data that pertain to each individual user or
community of users. (Oracle Corporation, 2001).
[0100] Additionally, only a few companies, such as LabVantage and
Telecation provide for network-oriented regulatory validation based
on CDER/CFIR/G*P (GLP, cGMP, GCP) compliant data tracking and
history. Products that integrate global data resources are unable
to maintain detail data state history over networks to maintain
compliance with strict laboratory information management (LIMS)
protocols. Overall, the current lack of techniques such as tightly
defined access controls, assured integrity of raw data, and strict
data history and tracking remain a hindrance for data sharing and
collaboration, both for new drug development, and especially well
for clinical data acquisition and clinical trials success. Improved
security techniques are called for in software for Life
Sciences.
[0101] Some Exemplary Benefits and Advantages
[0102] The inventive Sentient IT platform (also called "IMO IT
Platform") and architecture, a particular implementation and
embodiment of a more generalized and generic IT platform and
architecture, and its comprised components and access interfaces
provide a number of important benefits to bioinformatics computing
in particular, and to distributed and Life Science computing in
general. These include benefits for access and processing speed
involving terabytes or even petabytes of data, previously
unavailable multidimensional analytical functionality, flexible
distributed computing, and unmatched methods for security,
reliability and regulatory validation.
[0103] The Sentient IT Platform as well as other embodiments of the
inventive IT platform, offers significant benefits for improved
speed and efficiency. This enhanced speed and processing efficiency
is made possible by techniques for interactive content routing and
direct comparison of data content vectors, as well as meta-data and
data annotations, to allow for direct parallel data-to-data
communication and self-organizing data. Vector subset "workspace"
techniques for access and routing, wherein subsets of data content
as small as 1 byte may be specified for queries, minimize network
transfer and processing load and provide faster, more meaningful
access to terabytes databases.
[0104] As is commonly known, increased processing power can
increase speed, and the Sentient IT Platform's unique ability to
enable a variety of distributed supercomputing infrastructures,
such as for grid-based or peer-to-peer (any-to-any) processing will
be useful. Biosentients unique methods for efficient data-enabled
parallel processing and resource availability monitoring across
distributed environments will extend and enhance the speed benefits
offered by distributed computing infrastructures.
[0105] Additionally, the Sentient IT Platform offers techniques for
faster and more efficient data type translation and applications
integration and new applications assembly. The current state of the
art requires "wrapping" of existing data files for translation and
integration, which invariably adds processing overhead. In
contrast, the invention provides unique methods for translation
based on nested vector look up tables, which generally reduce the
processing overhead and improve performance for accessing of
existing data files.
[0106] In addition to speed benefits, the Sentient IT Platform
offers significant benefits for improved analytical functionality
across data types. The Sentient IT Platform automates integration
of heterogeneous data dependencies and relationships as well as
related validated and ranked annotation and literature. Recent
techniques for data integration such as by InCellico and Skila
provide unified environments for data relationship viewing,
clustering and data sharing. In contrast, the Sentient IT Platform
enables direct comparison of data content and annotation across
data types and structures utilizing heterogeneous analytical
applications, for "functionally integrated" analysis of multiple
variable interdependencies.
[0107] In addition to unmatched functional integration and
interconnectivity between heterogeneous related data, the platform
provides for easy plug-in of existing analysis tools and automated
new applications assembly. Interactive user customization and
automated applications assembly include a unified environment for
applications component modeling and linking, and efficient
automated application integration made possible by a functional
module comprised within the Intelligent Object application
framework, comprising the Intelligent Object application framework
component, automated applications assembly component, application
translation interface and object translation engine.
[0108] Additional analytical benefits for analysis follow from
Sentient IT Platform methods for self-organizing data, distributed
data learning, and for applications such as dynamic, kinetic
visualization, simulation and prediction based on direct comparison
of relevant data content vectors and meta-data to in silico
experiments and models. Integrated knowledge aggregation and
distributed learning engines can provide analytical tools for
automated, forecasting, alerting and data-driven simulations.
[0109] Security benefits include improved control to accessing and
routing of data. In contrast to current state of the database art,
such as Oracle's "9i" database structure, which provide "finely
grained" row-level data access control, the Sentient IT Platform
provides unique methods for controlling access to the level of a
workspace vector subset as small as a single byte. Even if
identification and e-signature encryption techniques enabled by the
Sentient IT Platform for unique data, user, session and machine
identification and access permission were subverted for an
intercepted transmission, the interceptor would normally find only
a small subset of data, meaningless outside of the context provided
by the Intelligent Object, Intelligent Object Handler and
Intelligent Object Pool.
[0110] Benefits for reliability include permanently logged,
always-on, detailed activity histories for every data object. In
contrast to technologies such as Mercury Prime's Razius" and Sparta
System's "Trackwise", which required tightly delimited network
connectivity scenarios to enable data tracking and auditing, the
Sentient IT Platform assures that comprehensive data state activity
tracking and history is always available, over flexible network
architectures, whether analysis is localized on a single
workstation or over wide area or global networks. These improved
data management and data state rollback options are made possible
by components and interfaces such as the object state engine,
status management component, and cache-based non-destructive
overlay processing methods. Non-destructive processing methods and
one-click integrity assessment assures that raw data integrity is
maintained at all times, and any data manipulation can be rolled
back to any previous data state.
[0111] The Sentient IT Platform also offers unmatched support for
regulatory compliance and validation over the complete data and
applications life-cycle. The object state engine, status management
component and legacy synchronization interface comprise methods for
maintaining persistent (always-on or immediately updated)
end-to-end data history, automated compliance status reporting and
alerting and full (GLP/GCP/GMP/ISO 9000/CDER CFR/CFIR) validation
support. Utilization of the application translation interface and
Intelligent Object application framework will make it possible to
efficiently generate new, or integrate existing interactive
clinical and regulatory report interfaces.
[0112] Compared to systems for scientific, distributed and
biological computing more broadly defined, the Sentient IT Platform
provides a number of benefits. For example, the Sentient IT
Platform provides unified, real-time bi-directional user
interactivity within "any-to-any" network connectivity
environments, an improvement over systems such as internet portals
and web-based applications services (ASPs) provided by Entigen's
Bionavigator or LabBook's eLabBook. Network options may be
automatically configured, according to detected network profiles
and custom user preference profiles. Flexible options for network
connectivity include distributed grid and peer-to-peer
super-computing, along with more traditional client-server,
server-server and other custom network connectivity options. These
tools enable more powerful collaboration and analytical
capabilities as, for the first time, all data and applications may
be securely integrated over any network and processing
configuration and are instantly available anywhere, any time.
[0113] The Sentient IT Platform provides unique methods for data
and applications translation and processing, providing an advantage
for speed, efficiency and functionality over current systems for
data and applications integration such as provided by Lion
Biosciences and IBM's DiscoveryLink. These advantages in speed and
efficiency parallel the unrealized functional interconnectivity
providing marked improvements for direct comparison and analysis of
meaningfully related dimensions of data content, even across
previously incompatible data types. Vectorized access, unique
translation technologies and data-enabled parallel processing
provide real time answers, without requiring programming-intensive,
inefficient data or database "wrapping", dedicated database
technology or massive parallel mainframe/cluster server expenses.
For the first time, terabytes of complex, heterogeneous data can be
analyzed in an affordable, unified environment, for real-time
answers to drug discovery questions.
[0114] The Sentient IT Platform provides functional integration of
existing analytical and data query tools with new tools and
previously incompatible data, without requiring compliance to
specific data annotation standards or data warehousing
specifications. This is an advantage over traditional analytical
tool or data access systems such as SwissProt's Melanie or
Edinburgh Biocomputing Systems' MPSRCH. In the Sentient IT
Platform, direct vectorized data access and translation look up
tables provides highly efficient, validated, normalized data and
applications interoperability. For the first time, all existing and
new data and applications are integrated for real-time
multidimensional analysis of heterogeneous data, in a globally
accessible and completely unified user environment.
[0115] While the complete Sentient IT Platform offers the widest
range of advantages, functional modules comprising one or more
processing components and/or access interfaces may be utilized to
enable specific functionality in key areas. Together, the
Intelligent Object, Intelligent Object Handler and Intelligent
Object Pool provide performance, functionality, security and
validation previously unavailable to bioinformatics and Life
Sciences computing.
[0116] Having described some of the features and advantages of the
inventive system, method, and information technology (IT) platform,
attention is now directed to a more detailed description of a
particular embodiment of the inventive IT platform and
architecture, referred to as the Intelligent Molecular Object
Information Technology Platform (IMO IT), or alternatively in some
embodiments as the Sentient Information Technology Platform
(Sentient IT).
[0117] The inventive Intelligent Molecular Object Information
Technology (IMO IT) Platform and its constituent subsystems,
methods, procedures, and computer software algorithms, solves the
diversified data and format as well as the heterogeneous database
and applications problems by integrating unique components,
processes, engines and interfaces such as networked data type,
database, and application type detection and table generation,
coupled with multidimensional table lookup and pointers. These
components, processes, engines and interfaces work within the IMO
IT architecture at the data object level to provide such advantages
as automated, real-time access and translation capabilities, thus
enabling instant handling of diverse data in heterogeneous
applications environments.
[0118] The IMO IT Platform also solves these and other problems and
limitations of conventional systems and methods by integrating
unique components, processes, engines and interfaces such as
workspace vector subset selection, dynamic meta-data indexing at
the data object and data subset levels, as well as direct
information interchange at the data object and data subset levels.
These components, processes, engines and interfaces, work within
the IMO IT architecture to provide significant advantages for
efficient analysis of high numbers of high density data, allowing
for true real-time data acquisition and analysis in a global data
environment.
[0119] The IMO IT Platform technology, in contrast to conventional
existing technologies described in the background, does not
reproduce or alter the raw data in any way, thus eliminating data
redundancy while simultaneously enabling multiple queries on a
single data object simultaneously. By using meta-data reference
tables, pointers and tags to provide real-time translation and
integration, which efficiently refers only to the aspects of any
raw data relevant to a specific query, the IMO IT Platform avoids
data redundancy and data access locking requirements. This provides
significant advantages over currently existing technologies as
described by such companies as Oracle and Sybase, for example, in
U.S. Pat. Nos. 6,105,030 and 5,832,484; each of which is hereby
incorporated by reference.
[0120] Furthermore, in contrast to the conventional practices
requiring retooling and/or translating at application and hardware
layers, described in the Background, the IMO IT Platform enables
translation, applications integration and validation of existing
systems, in real-time, at the data object level. This allows for
efficient scalability, interoperability and applications
development without retooling existing systems, and provides for
data-enabled validation of existing hardware and software
systems.
[0121] In one aspect, the inventive methods and procedures remedy
these constraints and limitations by providing an architecture
allowing interactive, object-based intelligent communication
in-between the data itself to extract all relevant content in a
fast, unique and automated manner, within complex network
environments without the need of upgrading or replacing current
computer systems.
[0122] These methods and procedures, also remedy these constraints
and limitations by allowing interactive, object-based intelligent
communication in-between the data itself to extract all relevant
content in a fast, unique and automated manner, within complex
network environments without the need of upgrading or replacing
current computer systems. The Intelligent Molecular Object 200
technology provides a flexible global standard, which allows for
seamless integration and real-time answers to complex,
multidimensional and interdependent queries. The intelligent
molecular object technology provides a framework for scale-up and
dynamically changing application needs in bioinformatics and the
life sciences.
[0123] They also remedy these constraints by allowing interactive,
object-based intelligent communication between object data based on
an unified presentation layer, an user definition and
administration shell and an automated application/database
definition generator interface which accounts for seamless
integration into the intelligent molecular object technology.
[0124] They further remedy these constraints by allowing
interactive, parallel, object-based intelligent communication
in-between the data itself to extract all relevant content in a
fast, unique and automated manner, within complex network
environments without the need of upgrading or replacing current
computer systems. The intelligent molecular object pool technology
provides seamless integration and real-time answers to complex,
multidimensional and interdependent queries, but still maintains
boundaries of data subsets to govern a secure, inter-data
communicative global access. The intelligent molecular object pool
technology also provides a framework for scale-up and dynamically
changing application needs in bioinformatics and the life
sciences.
[0125] Having now described some of the features, benefits, and
advantages of the invention, attention is now directed to
descriptions of various facets and embodiments of the invention.
Some of the major subsystems and procedures contributing to this
operation and the advantages that follow therefrom are now
described in greater detail.
[0126] Software Architecture for an Information Technology Platform
Using Intelligent Objects
[0127] The software architecture for an information technology
platform using intelligent objects such as Intelligent Molecular
Object (IMO), software and processing method addresses all or
selected steps ad procedures of data processing from data
acquisition through diverse sources and instrumentation to final
output of diverse data analysis results.
[0128] In the first step, this technology uses engine components to
standardize and/or normalize data from diverse sources to make
those otherwise different data comparable. The engines provide a
means to accurately compare, thus allowing one to test for
identity, increase or decrease in values or functional relevancy
and the like. In particular, algorithms for tracking and
normalization of object data (Object Normalization Engine, ONE) or
image data (Global Image Normalization, GIN) are provided. These
algorithms allow one to extract variable and non-variable regions
within a set of data and generate a global standard to which all
data can be referred. By applying these algorithms, adjustments to
all necessary parameters in a multidimensional data set can be made
automatically and simultaneously. Within the described
architecture, these algorithms can be applied in external modules
or plug-in-format for other applications as well as for access via
intranet or Internet.
[0129] Next, the architecture uses uniquely defined data objects,
the "Intelligent Molecular Object 200" (IMO) as its data records.
Each the object consists of sets of functional layers (property
panes). These property panes are activated or disabled via a
Property Pane Controller (PPC). Each object is represented by a
Unique Identifiers (UID), which governs object data security and
access permissions via the Object Access Manager (OAM), and
additional layers which define origin of the object within the
network (Object Root Router, ORR) and route content and results
interactively (Interactive Content Router, ICR) across the network
in a standardized fashion. A Status Management Component (SMC)
monitors data integrity, command history and GLP/GMP-compliance via
a table-based Object State Engine (OSE). The object state engine
provides control for any object activity in real-time, logs
activity records, provides GLP/GMP compliant experiment state
assignment, state management for object data on the network,
information request ranking and vectorized, direct addressing of
data subsets (Vector Subsets, VSS) to minimize network traffic.
This processing engine also handles external query submission and
result synchronization for inter-object queries. All these
processes allow the object to communicate intelligently via VSS,
and keep track of their interactions. Using Meta-data Indices
(MDX), workspace-oriented VSS and Object Pane Descriptors (OPD)
accounts for quick and direct communication with diversified
applications and databases via an Object Query Interface (OQI). An
integrated Application Translator Link (ATL) communicating via the
OAM allows for application integration. Additionally, functionality
is provided for Direct Information Interchange (DII) between
objects, graphical preview of the object data (Object Graph
Preview, OGP) and raw data matrix structure description (Raw Data
Matrix, RDM; Matrix Structure Definition, MSD).
[0130] Next, the architecture provides an applications framework
within the IMO technology, the Intelligent Object Handler 202
(IOH), describing sets of user interactions and object environment
definition protocols. This IOH 202 is comprised in general of a set
of processing engines and access interface protocols. These
protocols provide methods and functions for preparation and
presentation of data objects (IMO Application Framework, IMO-A) for
interaction within heterogeneous environments. A Unified
Presentation Layer (UPL) within the IOH 202 provides a web-enabled
Graphical User Interface (GUI) to integrate components or modules
from diverse applications, laboratory systems environments, and to
act as handler for IMO data (IMO Handler, IMO-H). Additional
components include a User Definition Administration Shell (UDA), a
Master Query Component (MQC) and an interface to automate the query
of application and database requirements via an Application
Definition Generator (ADG). The creation and initiation of new IMO
data is provided via an integrated IMO Generator (IMO-G). A Direct
Instrument Acquisition & Control Interface (DIAC) and a
component for Automated Application Assembly (AAA) provide
integration of real-time data acquisition and analysis. Data Type
Translators (DTT) are provided to integrate automated
transformation from heterogeneous data sources into IMO data in
real-time. Automated normalization of data by calibration with
standardized empirical criteria within the workspace IMO
Standardization Technique (IMO-S) is managed through integrated
meta-data tags and pointers. Several access interfaces are also
integrated at the IOH level. Next, an Object Translation Engine
(OTE) is integrated, which governs interactions (such as
transformation, integration and information access) between IMO
data and other diverse data environments to enable real-time
communication. Such processes automatically determine other data
structures, look up functional information of the data, create
descriptors which correspond to object property panes and determine
application type and access to IMO's in real-time. All the engines
and interfaces establish the connection to the legacy world and
provide bi-directional, multidimensional, secure access to
applications (Application Translation Interface, ATI), for queries
via OQI, for result generation (Result Generation Interface, RGI)
and for synchronization with offline legacy systems (Legacy
Synchronization Interface, LSI). Additionally, learning engines
such as a Knowledge Extraction Engine (KEE) or Distributed Learning
Engine (DLE) and the like can be implemented within the IOH
202.
[0131] Next, the architecture provides processes, which govern a
global virtual data pool, the Intelligent Object Pool (IOP) 204.
The processes contain definitions for subsets, called Intra-Pools
(iPools) 155 regulated by boundary protocols, which define
integrity and persistence of IMO relationships. IOP components
comprise iPool boundary interfaces, iPool meta-data query and
content access interfaces and iPool content ordering definitions
and protocols. iPool boundary interfaces include the iPool Security
Authentication (iPSA) component, which provides security
authentication; the iPool Integrity Assessment (iPIA) and Object
Integrity Assessment (OIA) components, which provide data integrity
and persistence; and the iPool Availability Monitoring (iPAM) and
iPool Exchange Protocols (iPEP) components, which define and
control availability and exchange at the IMO level. IPool Meta-data
query definition interfaces are provided by Object-to-Object Query
Meta-data (OQM), Real-Time Meta-data Link (RML), iPool Meta-data
Index (iMDX), and iPool-to-iPool Query (PPQ) components. These
processes apply interactive presorting and exclusion algorithms,
provide object clustering, object result clustering and
object-to-object interaction rules, and enable rapid, relevant data
access via real-time meta-data queries ordered within the iPool on
the IMO level. iPool content access and ordering definitions and
protocols include an Aggregate Meta-data Index (aMDX), Aggregate
Real-time Significance Generator (aRSG) which integrate result
merging algorithms and real-time answer generation. These iPool
content access protocols and order definitions allow for
object-to-analysis tools interactions, real-time result aggregation
and real-time exclusion of irrelevant object data layers.
Additional components include an IMO Zoomer (IMO-Z), which defines
the proximity of individual IMO data within the IOP and enables
multidimensional IMO data viewing and functional ranking.
[0132] Through the functionality detailed above, the IOP provides
an engine for global result aggregation (Result Aggregation Engine,
RAE) and instant answer output across diversified data subsets and
an interface to assess integrity of iPools 155 within the virtual,
global data pool.
[0133] Engines, interfaces and components comprising methods,
functions, and definitions are provided, to define and describe a
unique, data-enabling software architecture (IMO IT Platform).
These engines, interfaces and components implement an information
technology platform which utilizes Intelligent Molecular Object
(IMO.TM.) data and consists of a common framework comprising
always-on as well as event-driven processing engines, access
interfaces, plug-in modules and other components.
[0134] The IMO IT Platform architecture defined and described below
addresses all steps of data processing from data acquisition
through diverse sources and instrumentation to final output of
diverse data analysis results reports.
[0135] The IMO IT Platform utilizes uniquely defined data objects,
"Intelligent Molecular Object" data objects as its data records.
Each IMO consists of sets of functional layers (property panes),
describing content and providing certain functionalities to the
object. These property panes are dynamically activated or disabled
via a Property Pane Controller (PPC), function of which is to allow
or block access based on user privileges, data pool definitions and
the like.
[0136] Each IMO is represented by an Unique Identifier (UID)
contained within the identity pane, so it can be addressed and
identified on any network directly via its ID. The identity pane
also governs object data security and access permissions via the
Object Access Manager (OAM), an integrated part of the PPC to
initiate object communication. Next, each IMO contains a layer,
which contains information defining the origin of the object within
the network (Object Root Router, ORR) and its owner. Next, each IMO
contains a layer, which routes content and results interactively
(Interactive Content Router, ICR) across the network using
standardized protocols.
[0137] Next, a Status Management Component (SMC) monitors data
integrity and command history in GLP/GMP-compliance via a
table-based Object State Engine (OSE). The Object State Engine
consists of processes which control any object activity in
real-time, log activity records, provide GLP/GMP compliant
experiment state assignment and state management for object data on
stateless networks. In addition, the Object State Engine handles
information request ranking and vectorized, direct addressing of
data subsets (Vector Subsets, VSS) to minimize network traffic.
This processing engine also handles external query submission and
result synchronization for inter-object queries by providing
routing, property pane access clearance and direct,
workspace-oriented VSS addressing. All these processes allow the
object to communicate intelligently via VSS, and keep track of
their interactions.
[0138] Next, the IMO includes Meta-data Indices (MDX) layer for
rapid access, and Object Pane Descriptors (OPD) which allow for
quick and direct communication with diversified applications and
databases via an Object Query Interface (OQI), which allows for
object-level direct Information Interchange (DII) between
objects.
[0139] An integrated Application Translator Link (ATL),
communicating via the OAM and ICR, accounts for integration of
external applications and/or remote application status inquiries.
Next, functionality is provided for graphical preview of the object
data (Object Graph Preview, OGP) and raw data matrix structure
description (Raw Data Matrix, RDM; Matrix Structure Definition,
MSD).
[0140] The architecture defines an applications framework within
the IMO technology, the Intelligent Object Handler 202 (IOH), which
provides sets of user interactions and object environment
definition protocols for the IMO data. This IOH 202 is comprised in
general of a set of processing engines and access interfaces. A
Unified Presentation Layer (UPL) within the IOH 202 provides a
web-enabled Graphical User Interface (GUI), which integrates data,
components and/or analytical and processing modules from diverse
applications and laboratory systems environments. In general, these
protocols provide for preparation and presentation of data objects
for interaction within heterogeneous environments (IMO Application
Framework, IMO-A).
[0141] To ensure automated, real-time normalization of data using
one or several calibrations with empirical criteria within the
workspace, the IMO Standardization Technique (IMO-S) is provided,
which activates engine components for standardization and
normalization through utilization of integrated meta-data tags and
VSS pointers. These engine components are defined by the following
methods and functions, which standardize and/or normalize data from
diverse sources to make those otherwise different data comparable.
The engines provide a means to accurately compare, thus allowing to
test for identity, increase or decrease in values or functional
relevancy and the like. The following algorithms for tracking,
standardization and/or normalization of object data (Object
Normalization Engine, ONE) or image data (Global Image
Normalization, GIN) are defined. These algorithms allow for the
extraction of variable and non-variable regions within a set of
data and generate a global standard to which all data can be
referred.
[0142] In the case of ONE, which is described in but not restricted
to the following example in the field of Life Sciences, the data
are comprised of numeric matrices, text annotations, chemical
structure information, chirality information, spectral information,
sequence information and the like.
[0143] In the case of GIN, described by but not limited to the
following example in the field of Life Sciences, the data are
information contained in fluorescent and/or otherwise visibly
stained 1D or 2D gel electrophoresis images, array images,
microscopic images and the like, all of which may differ in image
acquisition parameters, detection technique, intensity, color,
positional distortions of zones, bands, spot or other regular or
irregular objects contained in the images which, in consequence,
relate to certain macromolecule properties, such as size/molecular
weight, isoelectric point, concentration, biological activity and
the like. By applying algorithms, which define a vector subset for
workspace selection in a single or a set of images, only those,
typically relatively small areas of the images are processed which
are needed to achieve relevant comparison. Such, data transfer
between different objects and for temporary storage and processing
via normalization, is reduced to the subsets for speed and
efficiency. By reference to either a common, global standard or a
dynamically obtained, averaged reference from all data included
within the comparison query, adjustments to all necessary
parameters in a multidimensional data set can be made automatically
and simultaneously in parallel. Since those processes apply only to
temporarily extracted small data subsets, several different request
on the same object may be processed at the same time. Since in each
case only vector subset are generated, no raw data alteration
occurs and GLP/GMP-compliant data integrity is maintained. Within
the described architecture, the algorithms can be applied as
processing engines, in external modules or in plug-in-formats for
other applications as well as for remote access via intranet or
Internet.
[0144] Engines, interfaces and components provided within the IOH
202 to provide an integrated analytical framework, including but
not limited to the following. A User Definition Administration
Shell (UDA) interface is provided, which creates, modifies and
administers user profiles and privileges and defines rules for
users within individual subsets of data, called Intra-Pools
(iPools) 155, as well as group memberships and topic-related access
rights. A Master Query Component (MQC), is provided, which creates
complex, multidimensional queries, containing pre-defined,
configurable subsets of forms commonly used, but not restricted to,
in diverse areas of Life Sciences. An Application Definition
Generator (ADG) component is provided, which automates the query of
application and database requirements and is comprised within
related translation components to generate tables required for
integrated real-time property pane presentation at the data object
level. An IMO Generator (IMO-G) component is provided, which
creates new IMO data from existing data resources, or from newly
acquired instrumentation data. An IMO handler (IMO-H) component is
provided, which initiates user commands and queries at the IMO
level via the use of integrated meta-data tags and pointers. A
Direct Instrument Acquisition & Control Interface (DIAC) is
provided, which enables bi-directional real-time communication
between the IOH 202, the IMO 200 and diverse instrumentation. An
Automated Application Assembly (AAA) component is provided, which
enables integration of real-time data acquisition and analysis
functionality through just-in-time (JIT) module linking. A Data
Type Translation (DTT) component is provided, which integrates
translation tables from the ADG from heterogeneous data sources
into IMO data in real-time. The DTT are comprised of dynamically
generated sets of reference tables to provide rapid access through
data structure definitions.
[0145] Several engines and access interfaces integrated at the IOH
level are defined, which utilize the meta-data tags and pointers to
pass information between internal and external components. All the
engines and interfaces establish the connection to the legacy
world. An Object Translation Engine (OTE) is included, which
governs interactions (such as transformation, integration and
information access) between intelligent object data and other
diverse data environments to enable real-time communication. Such
processes automatically determine other data structures, look up
functional information of the data, create descriptors which
correspond to object property panes and determine application type
and access to IMO's in real-time. An Application Translation
Interface, (ATI) is defined, which provides bi-directional,
multidimensional, secure access to applications for queries via
OQI. A Result Generation Interface (RGI) is defined, which provides
validated, assembled, ranked and tabulated results to the RAE,
enabling the generation of output reports across diversified data
subsets. A Legacy Synchronization Interface (LSI) is defined, which
provides synchronization with offline legacy data. Additionally,
learning engines such as a Knowledge Extraction Engine (KEE) or
Distributed Learning Engine (DLE) and the like can be implemented
within the IOH 202.
[0146] Next, the architecture provides the following interfaces,
component processes and engines which enable and govern a global
virtual data pool comprised of IMO data, the Intelligent Object
Pool (IOP) 204. The component processes contain definitions for
subsets, called Intra-Pools (iPools) 155, regulated by boundary
protocols, which define integrity and persistence of IMO
relationships. IOP components are defined, which comprise iPool
boundary interfaces, iPool meta-data query and content access
interfaces and iPool content ordering definitions and
protocols.
[0147] iPool boundary interfaces are defined, which include the
iPool Security Authentication (iPSA) component, which provides
security authentication; the iPool Integrity Assessment (iPIA) and
Object Integrity Assessment (OIA) components, which provide data
integrity and persistence; and the iPool Availability Monitoring
(iPAM) and iPool Exchange Protocols (iPEP) components, which define
and control availability and exchange at the IMO level.
[0148] iPool Meta-data query definition interfaces are provided by
Object-to-Object Query Meta-data (OQM), Real-Time Meta-data Link
(RML), iPool Meta-data Index (iMDX), and iPool-to-iPool Query (PPQ)
components. These processes apply interactive presorting and
exclusion algorithms, provide object clustering, object result
clustering and object-to-object interaction rules, and enable
rapid, relevant data access via real-time meta-data queries ordered
within the iPool on the IMO level.
[0149] iPool content access and ordering definitions and protocols
are provided, which include an Aggregate Meta-data Index (aMDX),
Aggregate Real-time Significance Generator (aRSG) which integrate
result merging algorithms and real-time answer generation. These
iPool content access protocols and order definitions allow for
object-to-analysis tools interactions, real-time result aggregation
and real-time exclusion of irrelevant object data layers.
Additional components include an IMO Zoomer (IMO-Z), which defines
the proximity of individual IMO data within the IOP and enables
multidimensional IMO data viewing and functional ranking.
[0150] It is evident from the above description, that this IMO IT
architecture allows for decision empowering, real-time answers to
complex, multidimensional, interdependent queries by providing the
infrastructure for a global, comprehensive analysis of otherwise
not accessible vast, inconsistent sets of data.
[0151] The following examples are offered by way of illustration
and not by way of limitation. In a typical life sciences example, a
2-dimensional electrophoretic protein separation is carried out and
the silver-stained separation pattern is introduced in an imaging
workstation to obtain a high resolution, high dynamic range image
representation of the spot pattern (typically, about 2500-6000
individual protein spots/image). Each of those spots represents a
single protein at a defined expression stage in a specific cell
environment, e.g. in this example, human liver cells. These pattern
reflect also genetic differentiations and/or modifications, e.g. in
case of human samples, the origin of the cell, gender, age, race,
physical condition of the individual and the like.
[0152] It is obvious from the above, that such pattern represent in
inherent multidimensional complexity, all of which even more
expressed by laboratory-to-laboratory deviations in performing the
analytical procedure, sometimes also on different types of
instrumentation. To analyze such a pattern for, for instance the
increase or decrease in concentration based on a pathological
disease condition or for drug-induced changes on certain proteins
in enzyme-, immunological activity and the like, non-patient
specific data have to be separated from those common in all
pattern. A large series of such gels must be analyzed, standardized
and compared to achieve this goal.
[0153] In the example, the IMO platform technology will do the
following: A scientist on a laptop (1) in site A in the USA logs
onto the IMO platform; the UDA within the IOH 202 verifies the
login, sets user permissions and encryption level, generates a
session ID and starts a session for (1) in the IOH 202. Next, the
scientist (1) uses the menu in the UPL to acquire instrument data,
in this example gel image data. The DIAC within the IOH 202
communicates with the imaging workstation (2) to remotely start
image acquisition, the IMO-G creates a new data objects for the
currently acquired image. This new IMO consists now of a UID pane,
which uniquely identifies the IMO via a 10-character key across the
network. The UID pane also defines object type, contains
information about the origin (ORR) on the network (on 1), the owner
(creator) of the IMO, how content (the raw image data in this case)
is routed (ICR) interactively on the network (in this case, the raw
image file at the imaging workstation (on 2)), of an OSE pane, in
which the first 2 entries are created via the SMC (an object
creation record, and the current data state entry, "data
acquisition in progress", an indexed multi-digit number from the
GLP/GMP state assessment table), a OPD pane describing which panes
are functionally defined, a dynamically updated OGP (displaying a
progressive thumbnail view of the image in real-time as it is
acquired) and a RDM pane consisting essentially only of file type
description and pointers to the original image file, thus there is
no data redundancy anywhere on the entire network. When image
acquisition is complete, the SMC on the IMO adds another status
entry "data acquisition completed" to the IMO state history log and
adds final data MSD's to the RDM pane.
[0154] Simultaneously, another scientist (3) in site 2 in Japan
logs onto the IMO platform; the UDA within the IOH 202 verifies the
login, sets user permissions and encryption level, generates a
session ID and starts a session for (3) in the IOH 202. Next, the
scientist (3) uses the menu in the UPL to submit a global query
about gel image data, in this case, describing a defined
disease-related change in protein expression of peptides with an
iso-electric points around 5.5 and a molecular size ranging between
80 k DA-120 k DA in liver cells from white, human males with an age
above 50. The MQC within the IOH 202 analyzes the query by
providing the IMO-H with VSS for the appropriate workspace
definitions within the 2D gel image data (based on the entered pI
and size constraints, only a small fraction of each images is
relevant to the query), which, in turn, gates the query to IMO's
via OQI. Based on MDX links, the PPC will only inquire those iPools
155 and IMO's with matching OPD's. The OAM on each such IMO checks
access privileges, triggers the PPC, sends update request to the
OSE, extracts partial image information from the original RDM for
temporary storage to be processed via ONE and GIN for image
workspace normalization and comparison. Standardized comparisons
(in this case, the protein spot location in the individual IMO RDM
subset, and its concentration, represented by its integrated,
optical density calibrated intensity) within the VSS are reported
back via ICR in each IMO to the RGI at the IOH level, which creates
ranked output. Similarly, MDX-based, the OTE at the IOH level
performs data translation of workspace data from other databases
using the ATI to integrate those relevant data into the
result-ranking. During this time, another scientist (4) in site B
in the USA logs onto to the IMO platform, and performs a similar,
but not identical query. The DLE immediately addresses the MQC to
define a new VSS only within the already addressed, active IMO's
RDX's. The RAE at the IOP aggregates the IMO results, sorts them
based on relevancy and similarity, reports to the KEE and DLE for
dynamic MDX update, and posts the final tabulated answer back to
the IOH 202 via RGI. Proximity of integrated data is related to the
IMO-Z for graphical representation. The UPL now presents the
aggregated, ranked answers to the query in several graphical and
numerical windows to the scientist (3), including the newly
generated image from scientist (1). On a different location and
network, at the same time the local UPL in scientist's (4) computer
displays the aggregated answer for his query. In the above example,
for scientist (1) 212 peptides and for scientist (4) 96 peptides
were identified, which differ in their expression level, amongst a
set of >500000 relevant 2D gel images with approx. 3000
individual spots each.
[0155] It is noted, that in a conventional database environment,
processing of the query would require the analysis of the entire
gel images and their annotations, at the required resolution
typically at file sizes around 2-5 MB each, and the alignment
(normalization) of the image in its entity, thus, requiring
enormous amounts of data to be processed. The IMO IT platform
technology's dynamic workspace definitions via VSS reduces this so
significantly, that alignment, matching and comparison are reduced
to, in this example, about 40-60 kB data each (or approximately, by
60-fold), allowing real-time result aggregation.
[0156] In light of the description provided here and in the
following more detailed description as well as the appurtenant
figures, it will be appreciated that the invention provides an
architecture, business model and method of doing business related
to searching for and analyzing data generally and in particular
relative to biological, chemical, and life sciences type
information. Several exemplary embodiments are now described by way
of example, but not limitation.
[0157] In one aspect, the invention provides a software
architecture for an information technology platform, comprising of
always-on and event-driven, engines, interfaces and processes and
using intelligent molecular software data objects for interactive
data records.
[0158] In another embodiment, this architecture is further defined
such that the architecture further comprises one or more of: a. an
Intelligent Molecular Object 200 (IMO), a versatile, data-enabling
software object, which provides for real-time translation,
integration, and object-to-object/object-to-anlysis tools
communication at the object data level, to allow multidimensional,
platform-independent complex queries in real-time; b. an
Intelligent Object Handler 202 (IOH), which provides the
application framework and user interface for IMO data, to allow for
seamless integration of their benefits into legacy systems; and c.
an Intelligent Object Pool (IOP) 204, comprising one global virtual
data pool comprised of IMO data, which integrates diverse data
resources on any system or network to provide result aggregation
and instant answers across diversified data subsets.
[0159] In another aspect, the IMP of the architectural method and
platform is further defined to include one or more of: a. a unique
identifier (UID), comprising a property pane layer created at IMO
generation, which provides typically a 10 byte, alphanumeric unique
identification on any system or network; b. an object access
manager (OAM), a property pane layer which governs data security
and access according to UID permissions; c. an object root router
(ORR), a property pane layer which contains information to define
the origin of the object within the system or network; d. an
interactive content router (ICR), a property pane layer which
routes content and results interactively across the system or
network; e. a status management component (SMC), comprised of an
object state engine and certain interfaces, which monitors data
integrity and command history in GLP/GMP-compliance via state
history and governs table lookup actions via the ICR; f. a property
pane controller (PPC), which controls the initiation of IMO
communication according to activation by 3a through 3d, above; g.
vector subsets (VSS) for automatic, dynamic, or user-defined
workspace definitions, which provide vectorized, direct addressing
of data subsets for the ICR to minimize network traffic; h.
meta-data indices (MDX), to provide efficient access via
dynamically updated meta-data description relevant to extant data
queries and definitions; i. object pane descriptors (OPD), which
provide information about each object property pane and their
function as required for direct communication with diversified
applications and databases; j. an interface for direct information
interchange (DII), which provides the interface to communication at
the object level; k. an application translator link (ATL), which
activates the OAM and ICR to determine the property panes for
functional presentation and access within a given application or
database environment; 1. an object graph preview (OGP) pane,
comprising a limited resolution image and graphics viewer for quick
graphical data review, particularly of image data and spectral
datasets; m. a raw data matrix (RDM), comprising a property pane
which provides the full information subset for any data format or
structure; and, n. matrix structure definitions (MSD), which allows
for data field mapping and enables vector access to specific data
fields.
[0160] In another aspect, the architecture is further defined such
that the architecture and accompanying process and method include
the IOH 202 where the IOH further includes: a. a unified
presentation layer (UPL), which provides a web-enabled graphical
user interface (GUI) to integrate components and/or modules from
diverse applications, laboratory systems environments and to act as
handler for IMO data; b. a user definition administration shell
(UDA), which sets ups and governs access privileges to individual
IMO data at the user-defined level and is accessible within
heterogeneous network environments; c. at least one engine for data
object normalization and standardization, image normalization and
standardization, IMO data translation, distributed learning, and
knowledge extraction; d. at least one access interface to and
in-between instruments, data and applications, comprising
interfaces which include, but are not limited to, direct instrument
acquisition and control, application translation, direct object
query, result generation, and legacy synchronization; e. a master
query component (MQC), create complex, multidimensional queries,
containing pre-defined, configurable subsets of forms commonly
used, but not restricted to, in diverse areas of Life Sciences; f.
an IMO generator (IMO-G), an event-driven component to acquire data
from heterogeneous data resources, including from ongoing data
acquisition, in real-time and transforms device outputs and
heterogeneous data types to IMO data; g. an IMO handler (IMO-H),
which enables user management of IMO data utilizing integrated
meta-data tags and pointers; h. an IMO application framework
(IMO-A), which provides integration and access protocols to
heterogeneous applications and databases on the object level; i. an
application definition generator (ADG), which automates the query
and generation of application and defines computing environments
for the IMO data translation; j. at least one data type translator
(DTT), which define the data type dependencies for the IMO-G
according to the applications and database environments defined by
the ADG; and k. an automated application assembly component (AAA),
which provides for just-in-time (JIT) module linking.
[0161] In yet another embodiment or aspect, the architecture is
further defined such that the IOP includes one or more of: a. sets
of Intra-Pools (iPools) 155, regulated by boundary protocols, which
provide data subset management and the define integrity and
persistence of IMO relationships; b. iPool security authentication
protocols (iPSA), which authenticates iPool data requests according
to user login and object data identification; c. iPool availability
monitoring protocols (iPAM), which define the iPool availability
and access requirements of diverse data subsets; d. iPool exchange
protocols (iPEP), which determine and govern iPool data exchange
protocols according to user-defined criteria; e. an object
integrity assessment component (OIA), which assess object integrity
for security and QA/QC; f. sets of engines and interfaces to access
and generate ranked results within the IOP, including but not
limited to an integrity assessment interface, a real-time meta-data
linking interface and an iPool-to-iPool query interface; g. an
iPool meta-data index (iMDX), which provides dynamic, automated,
and user-defined meta-data indices at the iPool level; h. an
aggregate meta-data index (aMDX), which provides dynamic,
automated, and user-defined meta-data indices at the aggregate IMO
level, inclusive of all relevant data resources; i. an
object-to-object query meta-data sorter (OQM), to generate
temporary tables based on dynamic, automated, and user-defined
meta-data indices; and j. an aggregate real-time significance
generator (aRSG), which provides for significance detection of
values based on query parameters, meta-data indices when relevant,
and IMO data ranking.
[0162] In one additional particular embodiment, the engines within
the architectural platform and method are further defined to
include: a. an object state engine (OSE), which provides a
continuously-running (always on) set of processes, which monitor
and govern activities of IMO data, performing real-time recording,
updating and logging functions in GLP/GMP-compliant format.
[0163] In another alternative embodiment, one or more engines
within the architecture are further defined to include one or more
of: a. a set of IMO standardization techniques (IMO-S), comprising
engines which provide algorithms for tracking, standardization
and/or normalization of object data; b. an generic object
normalization engine (ONE), which extracts variable and
non-variable regions within any set of object data and generates a
global standard to which all data can be referred; c. an engine for
global image normalization (GIN), which extracts variable and
non-variable regions within any set of image data and generates a
global standard to which all data can be referred; d. an object
translation engine (OTE), which is comprised of methods and
functions for real-time meta-data extraction and table generation
of raw data matrix, data object, data field, data structure, data
functional information, data type, database type, and application
type definitions for the OPD; e. a distributed learning engine
(DLE), which provides algorithms for dynamic, automated, and
user-defined hypothesis generation utilizing global data resources;
and, f. a knowledge extraction engine (KEE), which provides
algorithms for dynamic, automated, and user-defined significance
discovery and report generation.
[0164] In a further embodiment of the architecture, the engines may
include: a. a result aggregation engine (RAE) 224, 475, to
validate, assemble, rank and tabulate results passed from the IOH
202 and to generate output reports across diversified data
subsets.
[0165] In yet a further embodiment of the architecture, the
interfaces may include: a. a direct information interchange
interface (DII), which allows for rapid analysis and results
aggregation by providing the interface for object-to-object and
object-to-analysis tools via such related interfaces and engines
including, but not limited to the OQI, OTE, and the DLE.
[0166] In yet a further embodiment of the architecture, the
interfaces may include one or more of: a. a graphical user
interface (GUI), utilizing web-enabling standards including but not
limited to Java and XML; b. a direct instrument acquisition and
control interface (DIAC), which provides bi-directional real-time
communication between the IOH 202, the IMO 200 and diverse
instrumentation; c. an application translation interface (ATI), to
provide automated real-time detection of diverse data and
applications and gate bi-directional access to the OTE, thus
enabling functional, standardized integration of IMO data within
heterogeneous data and applications environments; d. an object
query interface (OQI), comprising an interface for direct
information interchange (DII) with IMO data, which initiates query
analysis and results aggregation; e. a result generation interface
(RGI) to provide validated, assembled, ranked and tabulated results
to the RAE, thus enabling the generation of output reports across
diversified data subsets; and f. a legacy synchronization interface
(LSI), to provide persistence and synchronization of offline legacy
data.
[0167] In still another embodiment of the architecture, the
interfaces are further defined to include one or more of: a. an
iPool integrity assessment interface (iPIA), to asses data
integrity within a defined iPool for security and QA/QC; b. a
real-time meta-data link interface (RML), which provides for rapid
relevant data access based on query parameters and MDX information;
c. a pool-to-pool query interface (PPQ), which provides for query
optimization based on query parameters and relevant iPool data and
meta-data intercommunication; and, d. an IMO Zoomer (IMO-Z), which
defines proximity and functional ranking of individual IMO data
within the IOP and enables multidimensional IMO data viewing to
represent object relationships within the pool and in relationship
to other iPools 155.
[0168] In another embodiment, the invention provides a software or
combination software and hardware architectural platform that uses
objects for real-time, efficient, multidimensional, interdependent
intelligent queries.
[0169] In yet another embodiment, this software architectural
platform and the associated methods and procedures are implemented
on general purpose computers, information appliances, and the like
information and/or computation devices, at least some of which are
coupled to similar devices and servers on an interconnected
network, such as the Internet.
[0170] When implemented as software, the software may be resident
within a memory of the computer or information device and execute
within a processor, microprocessor, or CPU of such device. Any
conventional computer or information appliance having suitable
memory, processor, and interface capabilities may be used, many of
which types are known in the art.
[0171] An alphabetic list of IMO Information Technology Platform
acronyms as used in this description is listed below. In some
instances these acronyms are modified by prefixes or suffixes but
the meaning is generally clear from the description or drawing in
which it appears and all variations are not provided in this in the
interest of brevity.
1 AAA Automated Application Assembly ADG Application Definition
Generator aMDX Aggregate Meta-data Index aRSG Aggregate Real-time
Significance Generator ATI Application Translation Interface ATL
Application Translator Link DIAC Direct Instrument Acquisition
& Control Interface DII Direct Information Interchange DLE
Distributed Learning Engine DTT Data Type Translators GIN Global
Image Normalization GUI Graphical User Interface ICR Interactive
Content Router iMDX iPool Metadata Index IMO Intelligent Molecular
Object IMO-A IMO Application Framework IMO-G IMO Generator IMO-H
IMO Handler IMO-S IMO Standardization Technique IMO-Z IMO Zoomer
IOH Intelligent Object Handler IOP Intelligent Object Pool iPAM
iPool Availability Monitoring iPEP iPool Exchange Protocols iPIA
iPool Integrity Assessment iPool Intra-Pool (data subsets) iPSA
iPool Security Authentication KEE Knowledge Extraction Engine LSI
Legacy Synchronization Interface MDX Meta-data Indices MQC Master
Query Component MSD Matrix Structure Definition OAM Object Access
Manager OGP Object Graph Preview OIA Object Integrity Assessment
ONE Object Normalization Engine OPD Object Pane Descriptors OQI
Object Query Interface OQM Object-to-Object Query Meta-data ORR
Object Root Router OSE Object State Engine OTE Object Translation
Engine PPC Property Pane Controller PPQ Pool-to-Pool Query RAE
Result Aggregation Engine RDM Raw Data Matrix RGI Result Generation
Interface RML Real-Time Metadata Link SMC Status Management
Component UDA User Definition and Administration Shell UID Unique
Identifier UPL Unified Presentation Layer VSS Vector Subsets
[0172] Attention is now directed to particular embodiments of the
inventive structure and method, including exemplary embodiments of
the intelligent object pool (IOP) as well as aspects and
embodiments of intelligent objects (IMO) and intelligent object
handler (IOH).
[0173] System, methods, procedures, computer programs and computer
program products are provided that define and describe exemplary
embodiments of an information technology platform system and
architecture utilizing an Intelligent Object or Intelligent
Molecular Object (IMO) 200, Intelligent Object handler (IOH) 202
and Intelligent Object Pool (IOP) 204 and Intelligent Object data
structures as core processing components.
[0174] At least in part because textual description is a linear or
sequential process, the inventive methods, procedures, and
techniques as well as computer program and operation where such
methods, procedures, and/or techniques are implemented in such
computer programs, these methods, components and processes will be
described in a fashion that may not always describe every facet of
an action, operation, or process as it occurs when such are
simultaneous or concurrent, and/or interactive actions as they
occur. However, it should be noted that the system and method
herein described includes bi-directionally or multi-directionally
interactive components, processes, and interfaces which perform
certain tasks simultaneously, concurrently with overlap, or in a
rapidly alternating fashion.
[0175] Examples of exemplary enabling computer software or firmware
program code are provided to define and describe a particular
exemplary embodiment, which utilizes Microsoft C++ as the exemplary
programming language. Additionally, software development tools for
some embodiments include Visual C++, Microsoft Foundation Classes
(MFC), DIB image transformations and matrix-based graphical content
generation. The features, capabilities, methods, and structures
described herein are not limited to any particular programming
language or code, and those workers having ordinary skill in the
art will in conjunction with the description provided herein
appreciate that other conventional programming languages may be
used, and that as new programming languages appear, such languages
will also likely support the inventive methods and procedures
described herein. It will also be appreciated that the overall
architecture, its application across varied domains, its processing
engines and its access interfaces are in no way limited to the
utilization of Microsoft C++ or the Windows 32-bit operating system
environment. It will further be appreciated that other enabling
software codes or enabling techniques may also be used, including
for example Java, XML and other markup languages, and/or other
similar techniques; and that other operating systems such as
UNIX-based, Linux-based, or Windows-based, Macintosh Apple-based,
or other operating systems may be utilized and/or are interoperable
with aspects and features of the inventive system and method.
[0176] FIG. 1A is a diagrammatic representation of an embodiment of
a general IMO IT architecture, depicting relationships between
framework, component engines, interfaces and external data and
applications resources.
[0177] FIG. 1B is a diagrammatic representation of another
embodiment of the IMO IT architecture having different
localizations of components and interfaces, and focusing on the
integrated data and applications handling aspect of the IMO IT
Platform.
[0178] FIG. 2 is a diagrammatic representation of an embodiment of
the invention showing some functional relationships between
instruments, applications, and intelligent object pool (IOP) 204
and diverse databases within an embodiment of the intelligent
molecular object (IMO) architecture and topology.
[0179] FIG. 3 is a diagrammatic representation of an embodiment of
a typical multi-user network collaborative research effort
involving geographically diverse data acquisition and query
sites.
[0180] FIG. 4 is a diagrammatic representation of a flow chart of
typical data processing within the IMO IT architecture.
[0181] The illustration in FIG. 5 is a diagrammatic representation
of an exemplary hardware configuration for the intelligent object
pool (IOP) 204 and its enabling architecture. All major elements
within the diagram below may be bi-directionally connected over a
variety of network protocols. The minimum hardware requirement is
defined by a single machine. In an exemplary embodiment, as below,
two laptop computers are connected in a client/server configuration
to a workstation, to each other in a peer-to-peer manner, and via
the workstation directly to a laboratory instrument, such as a gene
sequencer or gel electrophoresis machine.
[0182] With further reference to FIG. 5, dotted bi-directional
lines 248 represent options for "any-to-any" network connectivity
enabled via use of intelligent objects as central accessing and
routing components. Any-to-any options include but are not limited
to LAN, WAN, peer-to-peer (e.g. data, applications, memory and
processor sharing between two or more laptops, workstations, etc.),
server-server, Portal, ASP and other unified, distributed, parallel
and grid network options. Connectivity protocols include and are
not limited to PPP, http, TCP/IP, and ftp over multiple
platforms.
[0183] FIG. 6A is a diagrammatic representation showing the
relationship of the intelligent object to an external intelligent
object handler ("IOH"), its components and access interfaces, the
legacy domain of existing data content, applications, and devices,
and an external intelligent object pool (IOP) 204.
[0184] In FIG. 6A, unbroken lines ending with arrows on each end
490 represent bi-directional communication between exemplary
property panes, components and access interfaces. Dashed lines
ending with arrows on each end 492 represent bi-directional
communication between optional property panes, components and
access interfaces. Crossed lines do not represent connections.
[0185] FIG. 6B is a diagrammatic representation of Intelligent
Objects providing a conceptual overview of interactive content
routing for multiple dimensions of (or "multidimensional") direct
information interchange within and between Intelligent Objects.
[0186] With further reference to FIG. 6B, each property pane
provides an overview of certain properties of the comprised data
and its relationships. For example, property panes describe
Intelligent Object ownership and activity history, but also
complex, multiple relationships to other data and applications.
Vectorized data content information and results of data content
comparison and analysis, data annotation, text references,
validation status and the like may be flexibly interconnected in a
functional manner via these panes and their related components and
access interfaces. User viewing and interactivity to define or
refine (without writing to the data content) Intelligent Object
property pane content presentation and relationship connectivity
for new queries, customization and the like takes place through
property panes presented at the unified presentation layer.
[0187] FIG. 7A is a diagrammatic illustration depicting aspects and
embodiments of the invention including exemplary embodiments
comprising a Unified Presentation Layer, modules comprising
components and access interfaces, and their relationships; as well
as the relationship of the Intelligent Object Pool ("IOP") to its
architectural elements, such as for example an external Intelligent
Object Handler 202 and/or Handlers (IOH) 202 and to their
components, access interfaces and users, to Intelligent Objects
("IMOs"), and via the Intelligent Object Handler 202 and
Intelligent Objects to the external "Legacy" domain of existing
heterogeneous data content or databases, applications, systems
and/or devices.
[0188] With further reference to the embodiment in FIG. 7A,
unbroken lines ending with arrows on each end 490 represent
bi-directional communication between exemplary property panes,
components and access interfaces. Dashed lines ending with arrows
on each end 492 represent bi-directional communication between
optional property panes, components and access interfaces.
(Generally, crossed lines 491 do not represent connections.)
[0189] FIG. 7B is a diagrammatic representation of an intelligent
object pool (IOP) comprising intelligent objects (IMO) 200 and a
data subset intra-Pool (iPool) 155 of intelligent objects defined
according to ownership, similarity or other boundary
definitions.
[0190] FIG. 8 is a diagrammatic representation of an exemplary
embodiment of the claimed elements and their relationships, showing
the relationship of the Intelligent Object Handler to Intelligent
Objects, comprised components and access interfaces, the legacy
domain of existing data content, applications, and devices, and an
external Intelligent Object Pool ("IOP").
[0191] In FIG. 8, unbroken lines ending with arrows on each end 490
represent bi-directional communication between exemplary property
panes, components and access interfaces. Dashed lines ending with
arrows on each end 492 represent bi-directional communication
between optional property panes, components and access interfaces.
(Generally, crossed lines 491 do not represent connections.)
[0192] With reference to FIG. 9, in a global, heterogeneous
environment, data from diversified sources are functionally
integrated via intelligent objects and their intelligent object
pool (IOP) 204, utilizing automated access definition protocols,
multiple alias integration and addressing, data field mapping and
state management to allow for integrated data access, analysis and
exchange utilizing distributed data content presented within the
Client iPool.
[0193] The chart in FIG. 9 depicts user relationships to the data
on database and iPool levels. In the figure, the upper section of
the diagram (i.e. "Client iPool", "Restricted", "Client Data",
"Resources") depicts data within intranets, LANs and the like,
while the lower, larger part of the diagram contains several
different forms of public and/or otherwise web-accessible data
sources.
[0194] The depiction below in FIG. 10 represents a process model
describing interactions of modules for an exemplary embodiment of
the intelligent object pool (IOP) 204.
[0195] In FIG. 10, data from a global data resource are passed
through an access interface consisting of a security layer 404, a
set of access and/or exchange protocols 406 and integrity
assessment procedures 408, to the intelligent objects comprised
within the intelligent object pool 204 for unified queries and
analyses. The pathways involved in unified direct intelligent
object-to-intelligent object 455-456 and iPool-to-iPool 457-458
data interaction generate the real-time answer and pass it back
through processes including aggregate meta-data indexing 422,
significance generation and aggregation 473-476 and security 404 to
the intelligent object handler 202.
[0196] The depiction below in FIG. 11 is a representation of the
graphical user interface window within the unified presentation
layer displaying algorithms included for clustering of intelligent
object data. A number of property panes showing dendrograms results
are seen behind the clustering algorithms menu in FIG. 11.
[0197] The depiction below in FIG. 12 shows an interface iPool Zoom
viewer for viewing iPool data relationships, utilizing techniques
that include dendrograms and self-organizing maps (SOM).
[0198] Attributes and interactions between individual intelligent
object (IMO) data within the set of objects are outlined in the
margins of this exemplary display, FIG. 12.
[0199] The depiction below in FIG. 13 shows an iPool Zoom interface
for viewing iPool data relationships, utilizing Principal
Components Analysis (PCA).
[0200] In one exemplary and advantageous embodiment the Intelligent
Object Pool (IOP) 204, comprised by an information technology
platform (ITP) 151 (such as for example, the Sentient IT Platform
version 1.0.4 beta, made by Biosentients, Inc. of 1325 61.sup.st
Street, Emeryville, Calif. 94608-2117) advantageously enabled in
software, provides an organizational architecture and framework for
Intelligent Object (IMO) data architecture. Core architectural
elements of the information technology platform 151 include the
Intelligent Object Pool 204, an external Intelligent Object Handler
202, and Intelligent Objects 200, each comprising some combination
of components, access interfaces, some or all coupled or otherwise
interacting via included methods and processing routines and
subroutines. In some embodiments, intra-Pools (iPools) 155,
comprising subsets of Intelligent Object Data within the
Intelligent Object Pool 204 data pool may also be considered as a
core architectural element. Some functionally meaningful
combinations of components and/or access interfaces may also be
defined as stand-alone modules or as customizable plug-ins to other
architectures or systems. Other optional elements may also be
provided within or coupled with the core architectural
elements.
[0201] Intelligent Object Pool 204
[0202] With reference to an embodiment illustrated in FIG. 7A, the
Intelligent Object Pool 204 comprises Intelligent Objects,
representing the global entity (or portion subset of global entity
called an intra-Pool or iPool 155) of accessible Intelligent Object
200 data content presented within a functionally integrated Unified
Presentation Layer 206. In addition to interaction with the
Intelligent Objects 200, the Intelligent Object Pool 204 may be
activated and directed by the Intelligent Object Handler 202. It is
noted that in FIG. 7A, unbroken lines ending with arrows on each
end represent bi-directional communication between exemplary
property panes, components and access interfaces; dashed lines
ending with arrows on each end represent bi-directional
communication between optional property panes, components and
access interfaces; and lines that may cross do not represent
connections.
[0203] The Intelligent Object Pool 204, Intelligent Object Handler
202 and Intelligent Objects (IMO) 200 comprise a unified
presentation layer; components comprising defined methods,
procedures, and processes; and access interfaces. The access
interfaces provide unique conventions used to allow communication
between components and in some cases other interfaces, for direct
linking of vectorized pointers to accessible Intelligent Objects
(IMO) 200 data content, for direct linking of meta-data; and for
linking and activation of component information and processes.
[0204] Other optional but advantageous data-enabling methods and
processes may for example include one or more of the following,
such as for example the customizable exchange of various
combinations of information required for queries and commands
within heterogeneous data and applications environments, and
modules for such as automated distributed learning (DLE) and
knowledge extraction (KEE).
[0205] The inventive information technology platform architecture
utilizes Intelligent Object data content properties and values for
core functionality, such as for example, aggregation, organization
and other processing of results of vectorized data content
analysis, or other information interchanged via components and
interfaces, such as dynamically definable meta-data
definitions.
[0206] With reference to FIG. 6A a raw data matrix property pane
(RDM) 1042 included within Intelligent Objects (IMO) defines data
content addressing and provides vectorized access information for
linking of diverse types of data content. Additionally, the matrix
structure descriptor (MSD) 1046 provides processes required by the
application/database definition router (ADDR) 1028 and data link
insertion component (DLI) 1044 for data field mapping and gating of
vectorized access to individual data fields between objects,
applications and databases. Examples of exemplary enabling code for
the raw data matrix property pane (RDM) 1042 and matrix structure
descriptor (MSD) 1046 interface are provided in a concurrently
filed copending Utility U.S. patent application Ser. No. 09/___,___
(Attorney Docket No. A-70135/RMA) entitled and describing among
other subject matter embodiments of an Intelligent Object (IMO) 200
application.
[0207] Vectorized access, routing, translation, linking and
comparison of data content include, for example data content
information such as RGB values or data field contents such as
literature reference pointers and data content subsets
("workspaces") defined to granularity from entire data files to
data content subsets as small as single byte workspaces, such as a
single band, spot or a set of bands or spots in a gel
electrophoresis pattern. Direct interchange of information between
data objects and between data objects, heterogeneous applications
and a various combinations of components, access interfaces, and
presentation layers includes processing information, commands,
queries, meta-data, data content and said defined workspace
subsets. Meta-data includes, for example, alphanumeric information
defining the data content and its relationships, definitions for
linking Intelligent Objects according to their detected functional
or semantically defined relationships, and lookup tables for data
content definition and presentation information.
[0208] Comprised meta-data index information may be utilized to
optimize linking, organization and direction of the vectorized data
content access and routing requests, processing commands, and
information interchange between such as Intelligent Objects,
heterogeneous and/or homogeneous data objects and data content,
applications, data resources and/or databases. An object pane
descriptor (OPD) provides an overview of information for any
particular Intelligent Object state and its relationships, for easy
user review and interactivity with functional linking and
vectorized accessing and routing of data content, meta-data and
information such as queries, reports and processing results.
[0209] A status management component (SMC) within each Intelligent
Object (IMO) 200 initiates direct object-to-object interchange of
information including data content, meta-data, queries and
processing commands in parallel with corresponding Intelligent
Objects via their comprised object query interfaces. The status
management component (SMC) directs the interactive content routing
of specified data vectors and/or meta-data index information
between object query interfaces and of other Intelligent Objects
for linking, comparison and relationship definition according to
comprised algorithms described and defined elsewhere, and relays
the data content information to the external object state engine
(OSE) and result aggregation engine interfaces (RAEi) within the
Intelligent Object Pool (IOP) 204.
[0210] Direct information interchange (DII) may include various
methods, procedures, and processes that provide such functionality
such as one or more of: (a) activity synchronization, (b)
generation of authentication and availability states, (c)
verification of integrity states for quality assurance, (d)
maintenance of exchange and ownership histories, and (e) status
updating.
[0211] Activities may for example include one or more of the
following: fielding and permission for access privileges;
recordation and reporting of royalties or fees for data exchange;
including recordation of detail including data attributes accessed,
requests for linking of data attributes; assessment of Intelligent
Object (IMO) 200 data integrity, of the integrity of the entire
Intelligent Object Pool contents, and/or of iPool data subsets; or
other activities provided by the system.
[0212] In one embodiment, activity synchronization states may
include for example information required for synchronizing two or
more processes that depend upon the simultaneous or closely linked
occurrence of specific related events in time, or that comprise
closely related properties or functions, such as for example:
synchronized linking for common responses to queries; commands for
window scrolling linked across data; requests for batch information
updating; automated batch information updating; linking to common
timing signals; and the like. Activity synchronization states may
be required by and provided to various combinations of Intelligent
Objects, components and access interfaces and are generated, for
example, by the object state engine (OSE) 208 within the
Intelligent Object Handler (IOH) 202, interacting with the status
management component (SMC) 1016 of the Intelligent Molecular Object
(IMO) 200. Activity synchronization may also be generated by
additional components and interfaces such as are comprised within
the Intelligent Object Pool (IOP) 204 for synchronization of
meta-data linking (RML) and (RMLi), and for synchronizing
iPool-to-iPool queries (PPQ) and (PPQi).
[0213] Availability states may include for example, on-line or
offline status; ranked or otherwise sorted aliases and/or multiple
addresses for data content access; validation ranking to determine
availability of data for such as regulatory filing requests; and
the like. Availability states are provided to the comprised iPool
Availability Monitor by external components and access interfaces,
such as for example the object access manager (OAM) 1036 comprised
within the Intelligent Object (IMO) 200; the application/database
definition generator (ADG) and legacy synchronization interface
(LSI) 2018 comprised within the Intelligent Object Handler (IOH)
202; and the iPool availability monitor (iPAM) and (iPAMi)
comprised within the Intelligent Object Pool (IOP) 204.
[0214] Authentication states may include for example, the status of
verification regarding the unique identity of an Intelligent
Object, a person, a machine, a session or a process; the access or
availability status of the uniquely identified Intelligent Object,
person, machine, or process; verification that information, queries
or commands really come from their stated source; and the like. The
generation of authentication states involves processes comprised
within the Intelligent Object Handler (IOH) 202 such as for user
definition and administration (UDA) 2000; within the Intelligent
Object (IMO) 200 such as for unique identification, accessing and
routing (1006-1012) and status management (SMC) 1016; and within
the Intelligent Object Pool (IOP), such as for the iPool security
authentication module comprising iPSA and iPSAi.
[0215] Integrity states for quality assurance may for example
include or be selected from one or more of the following integrity
states: data integrity verified; data integrity altered; alerts
regarding Intelligent Object integrity violation status; alerts
regarding processes that may or will violate Intelligent Object
integrity; activation of an interactive user interface providing a
description of the alert; provision of optional processing methods
and/or cancellation of the processes in case of integrity violation
alerts; and the like. Verification of these integrity states for
quality assurance involves processes comprised within the
Intelligent Object Handler (IOH) 202 such generated by the object
state engine (OSE) 208 and legacy synchronization interface (LSI)
2018; within the Intelligent Object (IMO) 200 such as for status
management (SMC) 1016 and raw data matrix definition (RDM) 1042;
and within the Intelligent Object Pool (IOP), such as for
Intelligent Object integrity assessment (OIA) and (OIAi), and iPool
integrity assessment (iPIA) and (iPIAi).
[0216] Exchange and ownership histories include for example
histories chronicling: Intelligent Object and iPool ownership
status; Intelligent Object and iPool ownership privileges and
parameters for exchange; recorded criteria for exchange such as
licensing, subscription, royalty or fee agreements; histories of
Intelligent Object data attribute access and attribute linking;
calculated exchange charges, fees or royalties; or other records of
exchange accounting. Generation and maintenance of exchange and
ownership histories involves processes comprised within the
Intelligent Object Handler (IOH) 202 such as for example, the user
definition and administration (UDA) 2000 component and object state
engine (OSE) 208; within the Intelligent Object (IMO) 200 such as
for unique identification and (UID) 1006 and (UIDi) 1012; and
within the Intelligent Object Pool (IOP), such as for implementing
and monitoring exchange protocols (iPEP) and (iPEPi).
[0217] Additionally the Intelligent Object Pool 204 comprises
methods, procedures, and processes for one or more of the
following: fielding queries, iPool 155 boundary management, iPool
data integrity management, data exchange management, data integrity
assessment, data definition, query and processing optimization,
data and data subset management, significance generation, result
aggregation, and data relationship viewing. These methods,
procedures, or processes are activated and implemented according to
predetermined or dynamically determined rules, policies, and
procedures. The rules, policies, and procedures may be any one or
more of automated, event-driven, interactive, and/or user-directed,
secure, audited, and validated.
[0218] Methods for result aggregation (RAE), and (RAEi) are
generally provided in a minimal embodiment of the invention, while
other methods including for example exchange protocols (iPEP,
(iPEPi); meta-data indices methods (PPQ), and (PPQi) ; and/or pool
content access methods (DLE), and (DLEi) are optional but
advantageous in most cases. Additionally, stand-alone functional
modules constructed out of the defined components and access
interfaces may be assembled to provide customized
functionality.
[0219] With further reference to the embodiment of the Intelligent
Object Pool 202 in FIG. 7B, at a top-level, the Intelligent Object
Pool 204 comprises methods, protocols, and definitions that enable
the functionality and utilization of the Intelligent Object Pool
204 and data subset iPools 155. Recall that, in at least one
embodiment of the invention, iPools 155 describe any subset of
Intelligent Object data comprised within an Intelligent Object Pool
204. The iPools may be flexibly defined by the user according to a
variety of parameters including ownership, data attributes,
experimental design, and the like. The protocols, definitions, and
methods include Pool boundary protocols 402, meta-data indices
indices 410 and definitions 411, and pool content access methods
and processes 420, as represented in FIG. 7A.
[0220] In one particular embodiment, an information technology
platform provides an object pool enabled in computer program
software for any object-oriented data architecture so that the
object pool architecture and methodology are not limited only to
the particular intelligent objects or intelligent molecular objects
specifically described elsewhere herein, but are merely
exemplary.
[0221] In other embodiments, additional modules, components, and
interfaces for a one or more optional but advantageous distributed
learning engines and/or knowledge extraction are provided. In the
description that follow and that are provided elsewhere in this
specification, the terms modules, components, and interfaces are
used to describe aspects of embodiments of the invention. While
some differences may generally exist between these modules,
components, and interfaces as the terms apply to difference aspects
of the inventive structure, method, and computer program and
computer program product, the following guidelines (though not
definitions) in understanding these phrases is provided.
[0222] A module is a top-level definition that defines an instance
of indicated functionality which may or may not be required and
provided for a specific user installation. Modules may be compiled
separately and a module's implementation can be changed without
requiring changes to other components, interfaces, or modules. A
module usually includes a component and an interface or some
combination of these, and may be designated by a "m" suffix
following the module acronym (such as RAE for the Results
Aggregation Engine component or RAEm for the Results Aggregation
Engine module). Components provide core processing activity
required for given functionality, comprising methods generic to the
overall architecture for advertising and enabling its
functionality, whether via interactive methods such procedures,
routines, or information interchange with some combination of
components and/or access interfaces. An interface provides unique
conventions used to allow communication between components and in
some cases other interfaces, for direct linking of vectorized
pointers to accessible Intelligent Objects (IMO) 200 data content,
for linking and activation of meta-data; and for linking and
activation of component information and processes, and may be
designated by a "i" suffix following the module acronym (such as
RAEi for the Results Aggregation Engine interface). It will be
appreciated that as each module, component, and interface is
different, reference should be made to the detailed descriptions of
each particular module, component, and interface to ascertain its
structure, function, and operation.
[0223] Also, for purposed of clarity, certain modules, components
and access interfaces may be grouped into functionally related
sets, such as Pool boundary protocols (PBP), Meta-Data Indices
(MDX), and Pool Content Access (PCA) definitions and methods.
[0224] Pool Boundary Protocols (PBP) 402
[0225] In one aspect, the pool boundary protocols (PBP) 402 are
primarily responsible for providing data security, providing and
enabling data exchange, and providing data integrity
assessment.
[0226] The Pool boundary protocol (PBP) 402 methods and definitions
described herein generally provide (i) pool security protocols and
procedures 404 that provide data security, (ii) pool boundary
exchange protocols and procedures 406 that provide definitions for
data ownership, royalty agreements, data access fee agreements and
enable detailed use-tracking and reporting to enable accurate
charging of fees, auditing of revenue, and other detailed
assessment and control for access and exchange of data subsets, or
iPools 155, and (iii) Pool integrity protocols and procedures 408
that provide and maintain data integrity assessment for individual
data objects as wells as data subset iPools 155. Recall that the
intelligent object data includes data content stored in global
and/or local data resources of potentially heterogeneous type
and/or structure. In one embodiment, the pool boundary protocols
(PBP) 402 include a plurality of functional modules comprising
components and access interfaces as described in greater detail
elsewhere herein.
[0227] In one embodiment, a set of pool boundary protocols (PBP)
402 are provided, comprising a plurality of modules: (i) an iPool
Security Authentication module (IPSAm), (ii) an iPool Availability
Monitor module (IPAMm), (iii) an iPool Exchange Protocol module
(IPEPm), (iv) an Object integrity assessment module (OIAm), and (v)
an iPool Integrity Assessment module (IPIAm).
[0228] The iPool Security Authentication module (IPSAm)
authenticates and permits or rejects iPool data requests according
to user login and object data identification, and in the embodiment
of FIG. 7A comprises an iPool security authentication component
(iPSAc) 431, and an iPool security authentication interface (iPSAi)
432).
[0229] The iPool Availability Monitor module (IPAMm) assures
availability of iPool data content, and in the embodiment of FIG.
7A comprises an iPool Availability Monitor component (iPAMc) 433,
and an iPool Availability Monitor interface (iPAMi) 434).
[0230] The iPool Exchange Protocol module (IPEPm) governs and
manages data ownership and exchange, and in the embodiment of FIG.
7A comprises an iPool Exchange Protocol component (iPEPc) 435, and
iPool Exchange Protocol interface (iPEPi) 436.
[0231] The Object integrity assessment module (OIAm) assesses
object integrity for auditing, security, validation, and quality
assurance/quality control; and in the embodiment of FIG. 7A
comprises an Object integrity assessment component (OIAc) 437, and
an Object integrity assessment interface (OIAi) 438.
[0232] Finally, the iPool Integrity Assessment module (IPIAm)
assesses data integrity within a defined iPool for security and
quality assurance and quality control and which provides alerts
regarding iPool validation status, and in the embodiment of FIG. 7A
comprises an iPool Integrity Assessment component (IPIAc) 439, and
an iPool Integrity Assessment interface (IPIAi) 440, which assesses
data integrity within a defined iPool for security and quality
assurance and quality control and which provides alerts regarding
iPool validation status.
[0233] Where the functionality provided by a particular module is
not required for a particular application, its provision is
optional to the IOP 204 and not required by these embodiments of
the invention. Furthermore, additional modules such as modules
comprising components 441, 443, and/or interfaces 442, 444 may
optionally be provided as the system, architecture, topology, and
methodology are adaptable and expandable to add additional
capabilities as the need arises.
[0234] In an exemplary embodiment, a set of Pool boundary protocols
402 provided within the Intelligent Object Pool 204 includes a set
or sets of computer program software instructions enabling
unidirectional and preferably bi-directional information
interchange with other system components and access interfaces
including but not limited to components and interfaces associated
with the external Intelligent Object Handler 202 (IOH), Intelligent
Object Pool (IOP) 204, iPools 155, and Intelligent Objects
including Intelligent Molecular Objects (IMO) 200. Recall that
although this description focuses on particular embodiment of the
IMO and IOH as described elsewhere herein, the structure and
methods described relative to the IOP and components of the IOP are
applicable to other objects and object handlers.
[0235] Defined data subsets or intra-Pools (iPools) 155, comprise
subsets of Intelligent Object data within the Intelligent Object
Pool 204. When the user requests access to the entire available
data included within the Intelligent Object Pool, or is accessing
the entire content of their Intelligent Object Pool for integrity
verification, the terms data pool, or simply pool may be used to
describe the entity of Intelligent Object data and their contents
included within the Intelligent Object Pool database structure.
Normally, however, the term iPool is used to describe any set of
data which has been defined, recognizing that even all data
comprised within the Intelligent Object Pool database structure
does not the define the global entity of data, but is rather a
subset (or intra-Pool) of the global data resource, or global data
pool. In most cases, iPool is used to define subsets of data
delimited within the Intelligent Object Pool 204 by Pool boundary
protocols such as ownership, conditions for access or exchange,
data interdependencies and content-based relationships, and the
like.
[0236] Pool boundary protocols, Meta-data Indices, and Pool Content
Access definitions provide descriptive headings referring to
groupings of included components and access interfaces within a
functional block of the Intelligent Object Pool 204. The Pool
boundary protocols, for example, comprise components and access
interfaces, which together may comprise functional modules for Pool
boundary protocols, definitions and activities and iPool boundary
protocols, definitions and activities. For example, the combination
of iPSA and iPSAi comprise a Pool boundary protocol module for
security. In analogous manner, the combination of iPAM with iPAMi
comprise a Pool boundary protocol module for security and
exchange.
[0237] The pool boundary protocols (PBP) comprise modules,
interfaces, components, methods and processes which enable data
subset pools or iPool 156 boundary definitions and user
interactivity with these data subset iPools 156. Generally, Pool
boundary protocols (PBP) provide access control, tracking,
recordation and management of data exchange, and data set integrity
assessment required for iPool subset 156 boundaries and parameters.
In particular, Pool boundary protocols allow for unified definition
and management of data subsets via included component and access
interface for (a) access and security; (b) integrity assessment and
verification for Intelligent Objects 200 and iPools 155; (c)
management of data content access for exchange and auditing; (d)
access and routing of global and/or local Intelligent Object data;
(e) data source availability management and provision of the
information to components and access interfaces as required for
state management of Intelligent Objects 200, iPools 155, and data
content information interchange.
[0238] Additionally, Pool boundary protocols may be utilized to
provide the same or similar functionality for the entire entity of
data comprised within the Intelligent Object Pool 202 database
structure, in which case the entity of data may be called a data
Pool or Pool. Because iPools 156 are subsets of the full data
"Pool", iPools may be flexibly defined according to the properties
of comprised data in relationship to access, availability,
ownership, integrity, and the like.
[0239] In one embodiment, the Pool boundary protocols desirably and
advantageously utilize object-oriented data structures, while other
embodiments use other than object-oriented data structures. The use
of object-orientated data structures is advantageous in some
information technology platform implementations, including the
Sentient Platform described in detail herein elsewhere, in part
because of the broad acceptance and existence heterogeneous
object-oriented data structures currently, and also because of the
existence of certain valuable data annotation, meta-data, or other
data-enabling techniques provided by the object-oriented data
structure. On the other hand, non-object-oriented data structures
may also be used in part because of the broad acceptance and
existence heterogeneous non-object data currently, and also because
the technologies defined herein are ideally suited to accessing and
defining non-object data content, and to translating the data
content for presentation within a variety of environments, both
object-oriented and non-object-oriented.
[0240] iPool Security Authentication Component (iPSAc) 431
[0241] The iPool Security Authentication component (iPSAc) 431 is
primarily responsible for permitting or denying a request for
access to features and capabilities of the system and modules,
components, and interfaces of the system, and/or to contents of the
system; optionally including a level of access when any such access
is permitted.
[0242] In an exemplary embodiment, an iPool Security Authentication
component (iPSAc) 431 contains a set of Security Authentication
software instructions comprising Pool boundary protocols, methods
and processes including bi-directional information interchange with
components and access interfaces including but not limited to the
following: Unified Presentation Layer; iPool Security
Authentication interface; and Pool boundary protocol components.
Additionally, a set of comprised definitions provide automated
and/or interactive methods and processes including definition of
iPool access permissions; definition of iPool ownership and
privileges and criteria for permission or denial of specific
access, routing and processing activities; including but not
limited to permission or denial to access and/or route subsets of
data content defined to the level of single bytes by activation of
vectorized pointers to the data content. Additionally, the iPool
Security Authentication component desirably includes methods and
processes including permission or denial to access and/or route
iPool meta-data information and definitions; including but not
limited to iPool content overviews; and iPool relationship
information. Furthermore, included methods and processes desirably
include automated and/or interactive authentication and permission
or denial of iPool access requests according to correspondence of
user login information; user definition and administration
permissions provided by an interface to an external Intelligent
Object Handler 202; iPool access permissions; and comprised
Intelligent Object data identification.
[0243] In an exemplary embodiment, the iPool Security
Authentication component (iPSAc) 431 comprises a iPool Security
Authentication module (IPSAm) within an information technology
platform 151 for Intelligent Objects. In another exemplary
embodiment, the iPool Security Authentication module (IPSAm)
optionally but advantageously comprises a module within an
information technology platform using object-oriented data
structures.
[0244] iPool Security Authentication Interface (IPSAi) 432
[0245] In one aspect, the iPool Security Authentication interface
(iPSAi) 432 is primarily responsible for enabling detection and
extraction of information required for security authentication by
the iPool Security Authentication component (iPSAc) 431 from
intelligent objects and their data content, the IOP 204, iPools
155, and components and interfaces thereof. It may also be
responsible for routing information derived by iPSAc 431 directly
to IOP 204, iPools 155, and components and interfaces thereof.
[0246] Additionally, methods and processes are provided, which
route information derived from the iPool Security Authentication
component directly to the Intelligent Object Pool 204; iPools 155;
components and access interfaces. Automated and/or interactive
methods and processes are provided, including but not limited to
access control for information interchange to and from the
Intelligent Objects, Intelligent Object Pool 204, iPools 155,
components and interfaces; provision of iPool Security
Authentication component activity information to access interfaces
and components as required for utility including but not limited to
synchronization of the component information interchange and
activity; recordation of the component information interchange and
activity; auditing of the component information interchange and
activity; validation of the component information interchange and
activity.
[0247] In an exemplary embodiment, an iPool Security Authentication
interface (iPSAi) 432 includes a set of Security Authentication
interface computer software code instructions including Pool
boundary protocols, methods and processes including bi-directional
information interchange with components and access interfaces
including but not limited to one, more, or all of the following: an
iPool Security Authentication component; one or more Intelligent
Object Pool access interfaces; an Intelligent Object Handler 202
(IOH); and Intelligent Molecular Objects 200 (IOM).
[0248] Information detection and extraction methods, procedures,
and processes are provided, which enable detection and extraction
of information required for iPool Security Authentication, provided
by the Intelligent Objects and their data content; Intelligent
Object Pool 204; iPools 155; components and access interfaces. The
information includes but is not limited to one, more, or all of:
Intelligent Object data content to be accessed via vector pointers;
meta-data indices; including meta-data for content information;
attribute information; relationship information; protocols;
ontologies; annotations; and/or other data-enabling information;
provided directly to the iPool Security Authentication
component.
[0249] Additionally, methods and processes may be provided, which
route information derived from the iPool Security Authentication
component directly (preferred) or indirectly to the Intelligent
Object Pool 204; iPools 155; components and access interfaces.
Automated and/or interactive methods and processes are provided,
including but not limited to access control for information
interchange to and from the Intelligent Objects, Intelligent Object
Pool 204, iPools 155, components and interfaces; provision of iPool
Security Authentication component activity information to access
interfaces and components as required for utility including but not
limited to synchronization of the component information interchange
and activity; recordation of the component information interchange
and activity; auditing of the component information interchange and
activity; validation of the component information interchange and
activity.
[0250] In an exemplary embodiment, the iPool Security
Authentication interface (iPSAi) is comprised as a module within an
information technology platform for Intelligent Objects. In another
embodiment, the iPool Security Authentication interface comprises a
module within an information technology platform using
object-oriented data structures.
[0251] iPool Availability Monitoring Component (iPAMc) 433
[0252] The iPool Availability Monitoring component (iPAMc) 433 is
primarily responsible for one or more of the following: monitoring
availability of iPool data content, managing access for iPool data
content, managing alias nomenclature, ranking and managing multiple
addressing, maintaining persistent meta-data definition
availability, and data and data subset cache definitions for data.
Depending upon system configuration and desired capabilities one,
several, or all of these responsibilities may be optional. It may
also optionally be responsible for data content backup for
specified or often used data and multiple-content addressing for
specified or often used data.
[0253] In an exemplary embodiment, an iPool Availability Monitoring
component (iPAMc) 433 contains or comprises a set of software
instructions for Pool boundary protocols, methods and processes
including bi-directional information interchange with components
and access interfaces including but not limited to the following
Unified Presentation Layer; iPool Availability Monitoring
interface; and Pool boundary protocol components. A set of Pool
boundary protocol definitions are included, which provide automated
and/or interactive methods and processes including availability
monitoring for iPool data content; access management for iPool data
content; nomenclature alias management; multiple addressing ranking
and management; maintenance of persistent meta-data definition
availability; and data and data subset cache definitions for data
including but not limited to specified Intelligent Object data;
Intelligent Object data content; offline Intelligent Object data;
and for data including but not limited to user defined; often used;
and recently used. An additional set of Pool boundary protocol
definitions may be provided which enable or provide automated
and/or interactive methods and processes including data content
backups for specified or often used data and multiple-content
addressing for specified or often used data.
[0254] In an exemplary embodiment, the iPool Availability
Monitoring component (iPAMc) 433 comprised as a module within an
information technology platform for Intelligent Objects. In an
optional aspect, the iPool Availability Monitoring component
comprised as a module within an information technology platform
using object-oriented data structures.
[0255] iPool Availability Monitoring Interface (iPAMi) 434
[0256] The iPool Availability Monitoring interface (iPAMi) 434 is
primarily responsible for detecting and extracting direct
information required for availability monitoring, such as may be
provided by the Intelligent Objects and their data content,
Intelligent Object Pool 204, iPools 155, and/or other components,
and/or interfaces. The iPAMi 434 may also be responsible for
routing information derived from the iPool Availability Monitoring
component directly to the Intelligent Object Pool 204, iPools 155,
and components and access interfaces. It may further optionally be
responsible for providing the iPool Availability Monitoring
component activity information to access interfaces and components
as required for utility. The utility desired may for example
include one or more of: synchronization of component information
interchange, activity recordation of both the component information
interchange and activity, auditing of the component information
interchange and activity, validation of the component information
interchange and activity, or other utility that may be desired and
provided within the system and method.
[0257] In an exemplary embodiment, an iPool Availability Monitoring
interface (iPAMi) 434 contains a set of software instructions
comprising Pool boundary protocols, methods and processes including
bi-directional information interchange with components and access
interfaces including but not limited to the following iPool
Availability Monitoring component; Intelligent Object Pool access
interfaces; Intelligent Object Handler 202 and Intelligent Objects.
Methods and processes are provided, which detect and extract direct
information required for availability monitoring, provided by the
Intelligent Objects and their data content; Intelligent Object Pool
204; iPools 155; components; and access interfaces; the information
including but not limited to Intelligent Object data content
accessed via vector pointers; meta-data indices; including meta-
data for content information; attribute information; relationship
information; protocols; ontologies; annotations and other
data-enabling information; provided directly to the iPool
Availability Monitoring component.
[0258] Additionally, methods and processes are provided, which
route information derived from the iPool Availability Monitoring
component directly to the Intelligent Object Pool 204; iPools 155;
components and access interfaces. Automated and/or interactive
methods and processes are provided, including but not limited to
access control for information interchange to and from the
Intelligent Objects, Intelligent Object Pool 204, iPools 155,
components and interfaces. Comprised methods and processes also
include provision of the iPool Availability Monitoring component
activity information to access interfaces and components as
required for utility including but not limited to synchronization
of the component information interchange and activity recordation
of the component information interchange and activity; auditing of
the component information interchange and activity; validation of
the component information interchange and activity.
[0259] In an exemplary aspect, the iPool Availability Monitoring
interface (iPAMi) 434 comprised as a module within an information
technology platform for Intelligent Objects. In an optional aspect,
the iPool Availability Monitoring interface comprised as a module
within an information technology platform using object-oriented
data structures.
[0260] iPool Exchange Protocol Component (iPEPc) 435
[0261] The iPool Exchange Protocol Component (iPEPc) 435 is
primarily responsible for defining iPool ownership status; defining
iPool ownership privileges and parameters; and for providing
detailed definitions regarding criteria for data and/or data
attribute or other information exchange. The iPEPc 435 may also be
responsible for managing user ownership, user ownership
recordation, authentication and permission or denial of iPool
access requests, and other administrative functions.
[0262] In an exemplary embodiment, an iPool Exchange Protocol
component (iPEPc) 435 contains a set of software instructions
comprising Pool boundary protocols, methods and processes including
bi-directional information interchange with components and access
interfaces including but not limited to the following Unified
Presentation Layer; iPool Exchange Protocol interface; and Pool
boundary protocol components. A comprised set of Pool boundary
protocol definitions provide automated and/or interactive methods
and processes including secure and interactive definition of iPool
ownership status; definition of iPool ownership privileges and
parameters; and detailed definition regarding criteria for
exchange, including protocols and/or algorithms for Intelligent
Object data attribute access; protocols and/or algorithms for such
as ownership, licensing, and subscriptions; protocols and/or
algorithms for such as exchange charges, and royalties; protocols
and/or algorithms for exchange accounting; and account definitions.
Additionally, a set of Pool boundary protocol definitions are
comprised which provide automated and/or interactive methods and
processes including user ownership management user ownership
recordation; authentication and permission or denial of iPool
access requests; according to correspondence of user ownership
verification to comprised definitions such as for access, routing
and processing; user exchange activity history provided by an
interface to an external Intelligent Object Handler 202; data
attribute access and linking activities; ownership, licensing, and
subscription status; fees, charges and royalty status; including
previewing of fees, charges and/or royalty terms and conditions
according to proposed queries and/or user commands.
[0263] In an exemplary embodiment, the iPool Exchange Protocol
component (iPEPc) 435 comprises a module within an information
technology platform for Intelligent Objects. In another embodiment,
the iPool Exchange Protocol component is optionally but
advantageously comprised as a module within an information
technology platform using object-oriented data structures.
[0264] iPool Exchange Protocol Interface (iPEPi) 436
[0265] The iPool Exchange Protocol Interface (iPEPi) 436 is
primarily responsible for detecting and extracting direct
information required for exchange protocols, provided by the
Intelligent Objects and their data content, Intelligent Object Pool
204, iPools 155, and various components and access interfaces. The
iPool Exchange Protocol Interface (iPEPi) 436 may additionally be
responsible for routing information derived from the iPool Exchange
Protocol component directly to the Intelligent Object, Intelligent
Object Handler 202, Pool 154 and iPools 155, components and access
interfaces. It may further optionally be responsible for providing
access and/or other control for information interchange to and from
the Intelligent Objects, Intelligent Object Pool 204, iPools 155,
components and interfaces. It may additionally be responsible for
providing iPool Exchange Protocol component activity information to
access interfaces and components as required for utility. The
utility required or otherwise provided it not required, may for
example include one or more of the following: synchronization of
the component information interchange, activity recordation of the
component information interchange and activity, auditing of the
component information interchange and activity, validation of the
component information interchange and activity, and combinations
thereof.
[0266] In an exemplary embodiment, an iPool Exchange Protocol
interface (iPEPi) 436 contains a set of software instructions
comprising Pool boundary protocols, methods and processes including
bi-directional information interchange with components and access
interfaces of and/or including but not limited to the following
iPool Exchange Protocol component; Intelligent Object Pool access
interfaces; Intelligent Object Handler 202; and Intelligent
Molecular Objects 200. Methods and processes are provided, which
detect and extract direct information required for exchange
protocols, provided by the Intelligent Objects and their data
content; Intelligent Object Pool 204; iPools 155; components; and
access interfaces; the information including one or more of
Intelligent Object data content accessed via vector pointers;
meta-data indices; including meta-data for content information;
attribute information relationship information; protocols;
ontologies; annotations; and other data-enabling information;
provided directly to the iPool Exchange Protocol component.
[0267] Additionally, methods and processes are provided, which
route information derived from the iPool Exchange Protocol
component directly to the Intelligent Object, Intelligent Object
Handler 202, Pool 154 and iPools 155, components and access
interfaces. Automated and/or interactive methods and processes are
provided, including but not limited to access control for
information interchange to and from the Intelligent Objects,
Intelligent Object Pool 204, iPools 155, components and interfaces.
Comprised methods and processes also include provision of iPool
Exchange Protocol component activity information to access
interfaces and components as required for utility including but not
limited to synchronization of the component information interchange
and activity recordation of the component information interchange
and activity; auditing of the component information interchange and
activity; validation of the component information interchange and
activity.
[0268] In an exemplary embodiment, the iPool Exchange Protocol
interface (iPEPi) 436 comprises a module within an information
technology platform for Intelligent Objects. In another embodiment,
the iPool Exchange Protocol interface optionally comprises a module
within an information technology platform using object-oriented
data structures.
[0269] Object Integrity Assessment Component (OIAc) 437
[0270] The Object Integrity Assessment Component (OIAc) 437 is
primarily responsible for the application of comprised methods and
processes to Intelligent Object state histories and/or data content
status, such as for example data checksums; and review of
Intelligent Object state histories in order to provide one or more
of auditing, security, validation, and quality assurance/quality
control. It may also be responsible for providing alerts regarding
Intelligent Object integrity violation status and alerts regarding
processes that may or will violate Intelligent Object integrity,
and optionally for activating an interface for the alert and
optionally providing processing methods and/or cancellation of the
processes for which the alert was generated.
[0271] In an exemplary embodiment, an object integrity assessment
component (OIAc) 437 contains a set of software instructions
comprising pool boundary protocols methods and processes including
bi-directional information interchange with components and access
interfaces including but not limited to the following unified
presentation layer; iPool Integrity Assessment interface; and other
Pool boundary protocol components. A set of Pool boundary protocol
definitions are included, which provide automated and/or
interactive methods and processes including assessment of
Intelligent Object and data content integrity based on techniques
including but not limited to iPool checksums; and application of
comprised methods and processes to iPool histories and status,
including auditing; security; validation; and quality
assurance/quality control. Additionally, a set of Pool boundary
protocol definitions are included, which provide automated and/or
interactive methods and processes including alerts regarding iPool
integrity violation status; alerts regarding processes that may or
will violate iPool integrity; and activation of an interactive user
interface providing a description of the alert and providing
optional processing methods and/or cancellation of the
processes.
[0272] In an exemplary aspect, the Object integrity assessment
component comprised as a module within an information technology
platform for Intelligent Objects. In an optional aspect, the Object
integrity assessment component comprised as a module within an
information technology platform using object-oriented data
structures.
[0273] Object Integrity Assessment Interface (OIAi) 438
[0274] The Object Integrity Assessment Interface (OIAi) 438 is
primarily responsible for detecting and extracting direct
information required for object integrity assessment provided by
the Intelligent Objects and their data content, Intelligent Object
Pool 204, iPools 155 and components and access interfaces; where
the information may include but not be limited to Intelligent
Object data content accessed via vector pointers, meta-data
indices, meta-data for content information, attribute information,
relationship information, protocols, ontologies, annotations, and
other data-enabling information provided to the Object integrity
assessment component. It may also be responsible for routing
information derived from the Object integrity assessment component
directly to the Intelligent Object, Intelligent Object Handler 202,
Pool and iPools 155, components and access interfaces. It may
further be responsible for providing access control for information
interchange to and from the Intelligent Objects, Intelligent Object
Pool 204, iPools 155, components and interfaces. It may
additionally be responsible for providing object integrity
assessment component activity information to access interfaces and
components as required for utility. Such utility may for example
include but not be limited to synchronization of the component
information interchange, and activity recordation of the component
information interchange and activity, auditing of the component
information interchange and activity, validation of the component
information interchange and activity, and combinations thereof.
[0275] In an exemplary embodiment, an Object integrity assessment
interface (OIAi) 438 contains a set of software instructions
comprising Pool boundary protocols, methods and processes including
bi-directional information interchange with components and access
interfaces including but not limited to the following Object
integrity assessment component; Intelligent Object Pool access
interfaces; Intelligent Object Handler 202; and Intelligent
Molecular Objects 200. Methods and processes are provided, which
detect and extract direct information required for object integrity
assessment provided by the Intelligent Objects and their data
content; Intelligent Object Pool 204; iPools 155; components; and
access interfaces; the information including but not limited to
Intelligent Object data content accessed via vector pointers;
meta-data indices; including meta-data for content information;
attribute information; relationship information; protocols;
ontologies; annotations; and other data-enabling information;
provided directly to the Object integrity assessment component.
[0276] Additionally, methods and processes are provided, which
route information derived from the Object integrity assessment
component directly to the Intelligent Object, Intelligent Object
Handler 202, Pool and iPools 155; components and access interfaces.
Automated and/or interactive methods and processes are provided,
including but not limited to access control for information
interchange to and from the Intelligent Objects, Intelligent Object
Pool 204, iPools 155, components and interfaces. Comprised methods
and processes also include provision of Object integrity assessment
component activity information to access interfaces and components
as required for utility including but not limited to
synchronization of the component information interchange and
activity recordation of the component information interchange and
activity; auditing of the component information interchange and
activity; validation of the component information interchange and
activity.
[0277] In an exemplary aspect, the iPool Object integrity
assessment interface (OIAi) 438 comprised as a module within an
information technology platform for Intelligent Objects. In an
optional aspect, the Object integrity assessment interface
comprised as a module within an information technology platform
using object-oriented data structures.
[0278] iPool Integrity Assessment Component (iPIAc) 439
[0279] The iPool Integrity Assessment Component (iPIAc) 439 is
primarily responsible for assessment of Intelligent Object and data
content integrity, such as assessing intelligent object and data
content integrity using iPool checksums, and iPool related
auditing, security, validation, and quality assurance/quality
control. It may also be responsible for generating and/or
processing alerts regarding iPool integrity violation status,
alerts regarding processes that may or will violate iPool
integrity, and activation of a user interface for identifying the
nature of the alert and presenting options for processing the alert
and or the process generating the alert.
[0280] In an exemplary embodiment, an iPool Integrity Assessment
component 439 contains a set of software instructions comprising
Pool Boundary Protocols, methods and processes including
bi-directional information interchange with components and access
interfaces including but not limited to the following Unified
Presentation Layer; iPool Integrity Assessment interface; and Pool
Boundary Protocol components. A set of Pool Boundary Protocol
definitions are comprised, which provide automated and/or
interactive methods and processes including assessment of
Intelligent Object and data content integrity based on techniques
including but not limited to iPool checksums; and application of
comprised methods and processes to iPool histories and status,
including auditing; security; validation; and quality
assurance/quality control. Additionally, a set of Pool Boundary
Protocol definitions are comprised, which provide automated and/or
interactive methods and processes including alerts regarding iPool
integrity violation status; alerts regarding processes that may or
will violate iPool integrity; and activation of an interactive user
interface providing a description of the alert and providing
optional processing methods and/or cancellation of the
processes.
[0281] In an exemplary embodiment, the iPool Integrity Assessment
component (iPIAc) 439 comprises a module within an information
technology platform for Intelligent Objects. In another embodiment,
the iPool Integrity Assessment component comprises a module within
an information technology platform that optionally uses
object-oriented data structures.
[0282] iPool Integrity Assessment Interface (iPIAi) 440
[0283] The iPool Integrity Assessment Interface (iPIAi) 440 is
primarily responsible for detecting and extracting direct
information used for iPool integrity assessment provided by the
Intelligent Objects and their data content, Intelligent Object Pool
204, iPools 155, and components and access interfaces, and provided
directly to the iPool Integrity Assessment component. The
information may include or be selected from (but not limited to)
the set of information consisting of Intelligent Object data
content accessed via vector pointers, meta-data indices, including
meta-data for content information attribute information,
relationship information, protocols, ontologies, annotations, and
other data-enabling information. The iPool Integrity Assessment
Interface (iPIAi) 440 may also be responsible for routing
information derived from the iPool Integrity Assessment component
directly to the Intelligent Object, Intelligent Object Handler 202,
Pool and iPools 155, and components and access interfaces.
[0284] In an exemplary embodiment, an iPool Integrity Assessment
interface (iPLAi) 440 contains a set of software instructions
comprising Pool boundary protocols, methods and processes including
bi-directional information interchange with components and access
interfaces including but not limited to the following iPool
Integrity Assessment component; Intelligent Object Pool access
interfaces; Intelligent Object Handler 202; and Intelligent
Molecular Objects 200. Methods and processes are provided, which
detect and extract direct information required for iPool integrity
assessment provided by the Intelligent Objects and their data
content; Intelligent Object Pool 204; iPools 155; components; and
access interfaces; the information including but not limited to
Intelligent Object data content accessed via vector pointers;
meta-data indices; including meta-data for content information;
attribute information; relationship information; protocols;
ontologies; annotations; and other data-enabling information;
provided directly to the iPool Integrity Assessment component.
[0285] Additionally, methods and processes are provided, which
route information derived from the iPool Integrity Assessment
component directly to the Intelligent Object, Intelligent Object
Handler 202, Pool and iPools 155; components and access interfaces.
Automated and/or interactive methods and processes are provided,
including but not limited to access control for information
interchange to and from the Intelligent Objects, Intelligent Object
Pool 204, iPools 155, components and interfaces. Comprised methods
and processes also include provision of iPool Integrity Assessment
component activity information to access interfaces and components
as required for utility including but not limited to
synchronization of the component information interchange and
activity recordation of the component information interchange and
activity; auditing of the component information interchange and
activity; validation of the component information interchange and
activity.
[0286] In an exemplary aspect, the iPool Integrity Assessment
interface (iPIAi) 440 comprised as a module within an information
technology platform for Intelligent Objects. In an optional aspect,
the iPool Integrity Assessment interface comprised as a module
within an information technology platform using object-oriented
data structures.
[0287] Meta-data Indices, Methods, Procedures, and Processes
[0288] The meta-data indices are primarily responsible for enabling
Intelligent Objects to communicate via data-enabling meta-data
algorithms.
[0289] Embodiments of the meta-data indices and meta-data indices
associated methods, procedures, and processes defined herein
comprise the components and access interfaces defined and described
in detail below, which enable Intelligent Objects to communicate
via data-enabling meta-data algorithms, which include for example
data object attribute definition, attribute linking, access
optimization, routing optimization and processing optimization.
Meta-data indices include modules comprising components and access
interfaces for automated, semi-automated, and/or manual or
interactive meta-data indexing, dynamic meta-data linking, active
object-to-object query meta-data and iPool-to-iPool meta-data
indices protocols.
[0290] In one embodiment, the Meta-data Indices include one or more
of the following modules: an iPool Meta-data Index module (iMDXm),
a Real-time Meta-data Link module (RMLm), an Object-to-Object Query
module (OQMm), and an iPool-to-iPool Query module (PPQm).
[0291] The iPool Meta-data Index module (iMDXm), comprising an
iPool Meta-data Index component (iMDXc) 451, and an iPool Meta-data
Index interface (iMDXi) 452, provides Meta-data definitions for
iPool data subsets based on global object meta-data index content
such as data field information, data annotation and other existing
meta-data information further defined and described below.
[0292] The Real-time Meta-data Link component (RMLc) 453, provides
for relevant data access based on query parameters and Intelligent
Object meta-data index based on global object meta-data index
content such as data fields, data annotation and other existing
meta-data information further defined and described below.
[0293] The Object-to-Object Query component (OQMc) 455, provides
for query optimization and data-enabled parallel processing based
on data object intercommunication regarding query parameters and
global object meta-data index content such as data fields, data
annotation and other existing meta-data information further defined
and described below.
[0294] The iPool-to-iPool Query component (PPQc) 457, provides for
data-enabled parallel processing and query optimization based on
data object intercommunication regarding query parameters and iPool
data intercommunication, global object meta-data index content such
as data fields, data annotation and other existing meta-data
information provided by and interchanged between iPool Meta-data
Indices as further defined and described below.
[0295] In an exemplary embodiment, a set of meta-data indices
contain sets of software instructions that provide or enable
definitions, methods, procedures and/or processes, and include
bi-directional information interchange with components and access
interfaces including but not limited to an external Intelligent
Object Handler 202, Intelligent Object Pool 204, iPools 155, and
Intelligent Objects. The meta-data indices definitions interact
with the Intelligent Object Handler 202 and Intelligent Objects to
acquire and direct information required for methods and processes
including but not limited to Intelligent Object data definition,
translation and integration; integration of analytical processes;
and meta-data based optimization of queries and processing at the
level of Intelligent Objects; and iPools 155; and optimized methods
for Intelligent Object-to-Intelligent Object and iPool-to-iPool
intercommunication; utilizing Intelligent Object meta-data and
aggregated iPool meta-data applied to interactive presorting and
exclusion algorithms, object clustering algorithms, meta-data
definition and linking modules; and object-to-object and iPool to
iPool meta-data interaction definitions. In an optional aspect, the
meta-data indices are comprised within an information technology
platform using object-oriented data structures that include but are
not limited to Intelligent Objects.
[0296] iPool Meta-data Index Component (iMDXc) 451
[0297] The iPool Meta-data Index Component (iMDXc) 451 is primarily
responsible for enabling optimized result aggregation and real-time
exclusion of irrelevant object data layers. It may also be
responsible for the provision of meta-data definitions for iPool
data subsets.
[0298] In an exemplary embodiment, an iPool Meta-data Index
component (iMDXc) 451 contains a set of software instructions
comprising meta-data indices methods and processes including
bi-directional information interchange with components and access
interfaces including but not limited to a Unified Presentation
Layer; iPool Meta-data Index interface; and meta-data indices
components to enable optimized result aggregation and real-time
exclusion of irrelevant object data layers. A set of meta-data
indices definitions, which provide automated and/or interactive
methods and processes including provision of meta-data definitions
for iPool data subsets; including but not limited to automatically
provided and/or user-defined information regarding data subset
content attributes; legacy data type, structure, and access
dependencies; data ownership and access information; and data
relationship information made available according to available
algorithms for determination of such as, but not limited to
similarity; association; contiguity; proximity; dependency;
functionality; data activity ranking; data significance ranking;
data validation ranking; query optimization based on correspondence
of query parameters to iPool Meta-data index content; provision of
iPool meta-data information required for methods and processes
including but not limited to iPool activation, iPool sorting,
iPool-to-iPool queries, iPool data presentation and result
generation.
[0299] In an exemplary embodiment, the iPool Meta-data Index
component (iMDXc) 451 comprises a module within an information
technology platform for Intelligent Objects. In another embodiment,
the iPool Meta-data Index component optionally comprises a module
within an information technology platform using object-oriented
data structures.
[0300] iPool Meta-data Index Interface (iMDXi) 452
[0301] The iPool Meta-data Index Interface (iMDXi) 452 is primarily
responsible for detecting and extracting direct information
required for meta-data indices, provided by the Intelligent Objects
and their data content, Intelligent Object Pool 204, iPools 155,
components, and access interfaces. In some embodiments the
information includes or is selected from (but is not limited to)
Intelligent Object meta-data indices including meta-data for
content information, attribute information, relationship
information, protocols, ontologies, annotations and other
data-enabling information. Typically, the information is provided
directly to the iPool Meta-data Index component. It may also be
responsible for routing information derived from the iPool
Meta-data Index component to the Intelligent Object Pool 204,
iPools 155, components and/or access interfaces. It may further be
responsible for controlling access for information interchange to
and from the Intelligent Objects, Intelligent Object Pool 204,
iPools 155, and/or components and interfaces; and/or for provision
of the iPool Meta-data Index component activity information to
access interfaces and components as required for desired
utility.
[0302] In an exemplary embodiment, an iPool Meta-data Index
interface (iMDXi) 452 contains a set of software instructions
comprising meta-data indices definitions, methods and processes
including bi-directional information interchange with components
and access interfaces including but not limited to the following
iPool Meta-data Index component; Intelligent Object Pool access
interfaces; Intelligent Object Handler 202 and Intelligent Objects.
Methods and processes are provided, which detect and extract direct
information required for meta-data indices, provided by the
Intelligent Objects and their data content; Intelligent Object Pool
204 ; iPools 155; components; and access interfaces; the
information including but not limited to Intelligent Object
meta-data indices; including meta-data for content information;
attribute information; relationship information; protocols;
ontologies; annotations and other data-enabling information;
provided directly to the iPool Meta-data Index component.
[0303] Additionally, methods and processes are provided, which
route information derived from the iPool Meta-data Index component
directly to the Intelligent Object Pool 204; iPools 155; components
and access interfaces. Automated and/or interactive methods and
processes are provided, including but not limited to access control
for information interchange to and from the Intelligent Objects,
Intelligent Object Pool 204, iPools 155, components and interfaces.
Comprised methods and processes also include provision of the iPool
Meta-data Index component activity information to access interfaces
and components as required for utility including but not limited to
synchronization of the component information interchange and
activity recordation of the component information interchange and
activity; auditing of the component information interchange and
activity; validation of the component information interchange and
activity.
[0304] In an exemplary embodiment, the iPool Meta-data Index
interface (iMDXi) 452 comprises a module within an information
technology platform for Intelligent Objects. In another embodiment,
the iPool Meta-data Index interface (iMDXi) 452 optionally
comprises a module within an information technology platform using
object-oriented data structures.
[0305] Real-time Meta-data Link component (RMLc) 453
[0306] The Real-time Meta-data Link component (RMLc) 453 is
primarily responsible (along with real-time meta-data link
interface (RMLi) 454 for linking of the Intelligent Object (IMO)
200 data according to: (i) their own meta-data definitions, (ii)
their participation within data subset iPools, and (iii) optionally
to other data access and/or applications definitions (e.g. other
meta-data) regarding relationships and interdependencies; so that
the data is organized automatically according to its
characteristics, provides information to each data object regarding
its position within the overall data environment, and provides a
dynamically and automatically organized environment for optimal
accessing and querying of data and optimized, functional
interaction between heterogeneous data objects and
applications.
[0307] The real-time meta-data link component (RMLc) 453 and
interface (RMLi) 454 provide the functionality required for linking
of the Intelligent Object (IMO) 200 data according to their own
meta-data definitions, according to their participation within data
subset iPools and according to other useful data access and/or
applications definitions regarding relationships and
interdependencies commonly understood as meta-data. This provides
for automated organization of the data according to its
characteristics, provides information to each data object regarding
its position within the overall data environment, and provides a
dynamically and automatically organized environment for optimal
accessing and querying of data and optimized, functional
interaction between heterogeneous data objects and
applications.
[0308] In an exemplary embodiment, a Real-time Meta-data Link
component (RMLc) 453 contains a set of software instructions
comprising meta-data indices methods and processes including
bi-directional information interchange with components and access
interfaces including but not limited to the unified presentation
layer; real-time meta-data link interface (RMLi) 454 and meta-data
indices components.
[0309] A set of meta-data indices definitions provide automated
and/or interactive methods and processes including detection,
fielding and organization of meta-data presented via external
components, access interfaces, application and user actions
including automated and/or user-directed queries; commands and
processing requests to provide automated and organized activation
of methods and processes corresponding to the meta-data. These
meta-data definitions may include validation and integrity
rankings; data attributes; annotations; definitions; tables; lists;
addressing protocols; access and routing protocols; processing
protocols; histories of query parameters presented and provide for
optimized provision of relevant data access and activities based on
correspondence of the query and/or processing request meta-data
parameters to the pre-organized Intelligent Object (200) and iPool
linking of said meta-data definitions. A set of methods and
processes are advantageously provided which interact with a
Real-time Meta-data Link interface, to provide updating of
meta-data index linkages for optimization of information
interchange between Intelligent Objects (IMO) 200, and between
Intelligent Objects (IMO) 200, iPools 155, and related
applications, components and interfaces. The information
interchange is enabled for example as an object state engine (OSE)
208 included within the Intelligent Object Handler (IOH) 202; and a
status management component (SMC) 1016 and object query interface
(OQI) 1018 comprised within Intelligent Objects (IMO) 200 which
provide for direct comparison of data content accessed via vector
subset pointers; as well as of meta-data information; and provision
of the data content information and meta-data information for
updating of the meta-data indices.
[0310] In an exemplary embodiment, the Real-time Meta-data Link
component (RMLc) 453 comprises a module within an information
technology platform for Intelligent Objects. In another embodiment,
the Real-time Meta-data Link component (RMLc) 453 optionally
comprises a module within an information technology platform using
object-oriented data structures.
[0311] Real-time Meta-data Link interface (RMLi) 454
[0312] The Real-time Meta-data Link interface (RMLi) 454 is
primarily responsible for detecting and extracting direct
information required for meta-data linking and automated
organization of Intelligent Objects according to criteria provided
by the Intelligent Objects and their data content, Intelligent
Object Pool 204, iPools 155, components, access interfaces, and/or
specific automated or user-directed queries or commands. It may
also be responsible for routing information derived from the
Real-time Meta-data Link component to the Intelligent Object Pool
204, iPools 155, and components and access interfaces. It may
further be responsible for controlling access for information
interchange to and from the Intelligent Objects, Intelligent Object
Pool 204, iPools 155, components and interfaces; and/or for the
provision of the Real-time Meta-data Link component activity
information to access interfaces and components as required for
organization and optimization of processes.
[0313] In an exemplary embodiment, an Real-time Meta-data Link
interface (RMLi) 454 contains a set of software instructions
comprising meta-data index definitions and methods and processes
including bi-directional information interchange with components
and access interfaces including but not limited to the following
Real-time Meta-data Link component; other Intelligent Object Pool
access interfaces; Intelligent Object Handler 202 and Intelligent
Objects. Methods and processes are provided, which detect and
extract direct information required for meta-data linking and
automated organization of Intelligent Objects according to criteria
provided by the Intelligent Objects and their data content;
Intelligent Object Pool 204; iPools 155; components; and access
interfaces, and specific automated and/or user-directed queries or
commands. The information required for organization of data and
received by the real-time meta-data link interface (RMLi) includes
for example Intelligent Object data content accessed via vector
pointers; meta-data index definitions including meta-data for
content information, attribute information, and other relationship
information; accessing, routing and exchange protocols; data
ontologies; software routines comprising pre-defined experimental
procedures; data annotations and other data-enabling information;
and is provided directly to the Real-time Meta-data Link
component.
[0314] Additionally, methods and processes are provided, which
route information derived from the Real-time Meta-data Link
component directly to the Intelligent Object Pool 204; iPools 155;
components and access interfaces. Automated and/or interactive
methods and processes are provided, including but not limited to
access control for information interchange to and from the
Intelligent Objects, Intelligent Object Pool 204, iPools 155,
components and interfaces. Comprised methods and processes also
include provision of the Real-time Meta-data Link component
activity information to access interfaces and components as
required for organization and optimization of processes such as for
example synchronization of the component information interchange
and activity recordation of the component information interchange
and activity; auditing of the component information interchange and
activity; validation of the component information interchange and
activity.
[0315] In an exemplary aspect, the Real-time Meta-data Link
interface (RMLi) 454 comprised as a module within an information
technology platform for Intelligent Objects. In an optional aspect,
the Real-time Meta-data Link interface comprised as a module within
an information technology platform using object-oriented data
structures.
[0316] Intelligent Object-to-Intelligent Object Query Component
(OQMc) 455
[0317] The Intelligent Object-to-Intelligent Object Query component
(OQMc) 455 is primarily responsible for detailed mapping and
integration of Intelligent Object meta-data and provides the
organizational activities needed or desired for the real-time
meta-data linking (RML and RMLi), and optionally for other
activities. This mapping and integration may further provide for
updating meta-data definitions for Intelligent Object(s), to enable
components for meta-data linking and query optimization. It may
also be responsible for Intelligent Object-to-Intelligent Object
meta-data information interchange optimization. The Intelligent
Object-to-Intelligent Object Query meta-data interface (OQMi) may
also be responsible for providing information required for the
real-time meta-data linking (RML) actions including direct
meta-data to meta-data linking between objects, and meta-data
information interchange corresponding to automated and/or
user-directed queries.
[0318] In an exemplary embodiment, an Intelligent
Object-to-Intelligent Object Query component (OQMc) 455, provides a
set of software instructions comprising meta-data indices methods
and processes including bi-directional information interchange with
components and access interfaces including but not limited to the
following Unified Presentation Layer; Intelligent
Object-to-Intelligent Object Query interface (OQMi) 456 and
meta-data indices components.
[0319] The Object-to-Object Query meta-data index component (OQM)
provides detailed mapping and integration of Intelligent Object
meta-data and provides the organizational activities required for
the real-time meta-data linking (RML and RMLi), as well as for
other activities. Methods are comprised acquisition and provision
of dynamically updated and/or user-defined meta-data definitions
for each comprised Intelligent Object, to enable components for
meta-data linking and query optimization; including but not limited
to definitions for Intelligent Object state; Intelligent Object
data and data content addressing; association; linking; weighting;
sorting; ranking of such as attribute; annotation; structure;
function; type; matrix definition; field mapping and vector
pointers. These meta-data definitions for Intelligent
Object-to-Intelligent Object information interchange comprise for
example such as previously mentioned validation and integrity
rankings; data attributes; annotations; definitions; tables; lists;
addressing protocols; access and routing protocols; processing
protocols; and histories of query parameters presented. This
information may be provided via meta-data comprised within object
data comprised within Intelligent Object data, as well as by
various components and access interfaces. Processing is provided
for Intelligent Object-to-Intelligent Object meta-data information
interchange optimization; comprising organization of the
Intelligent Object meta-data linking according to information
including but not limited to content attributes; validation state;
ranking; relationships; associations; and the like, and for query
optimization; based on dynamic updating of the Intelligent Object
organization and linking for information interchange; according to
correspondence between the information and queries; query
histories; commands; command histories; and/or other access,
routing and processing actions. Additionally, the Intelligent
Object-to-Intelligent Object Query meta-data interface (OQMi)
provides information required for the real-time meta-data linking
(RML) actions including direct meta-data to meta-data linking
between objects; and meta-data information interchange
corresponding to automated and/or user-directed queries.
[0320] In an exemplary embodiment, the Intelligent
Object-to-Intelligent Object Query component (OQMc) 455 comprises a
module within an information technology platform for Intelligent
Objects. In another embodiment, the Intelligent
Object-to-Intelligent Object Query component (OQMc) 455 comprises a
module within an information technology platform optionally using
object-oriented data structures.
[0321] Intelligent Object-to-Intelligent Object Query Interface
(OQMi) 456
[0322] The Intelligent Object-to-Intelligent Object Query interface
(OQMi) 456 is primarily responsible for detecting and extracting
direct information desired and/or required for object-to-object
queries provided by the Intelligent Objects and their data content;
Intelligent Object Pool 204, iPools 155, components, and access
interfaces; where the information may include but is not limited to
Intelligent Object data content accessed via vector pointers,
meta-data indices (including meta-data for content information),
attribute information; relationship information, protocols,
ontologies, annotations and other data-enabling information. As
with several of the other inventive interfaces, this information
may advantageously be provided directly, in this case directly to
the Intelligent Object-to-Intelligent Object Query component. It
may also be responsible for routing information derived from the
Intelligent Object-to-Intelligent Object Query component to the
Intelligent Object Pool 204, iPools 155, components and access
interfaces; and/or, for providing access control for information
interchange to and from the Intelligent Objects, Intelligent Object
Pool 204, iPools 155, components and interfaces.
[0323] In an exemplary embodiment, an Intelligent
Object-to-Intelligent Object Query interface (OQMi) 456 contains a
set of software instructions comprising meta-data indices
definitions, methods and processes including bi-directional
information interchange with components and access interfaces
including but not limited to the following Intelligent
Object-to-Intelligent Object Query component (OQMc) 455;
Intelligent Object Pool access interfaces; Intelligent Object
Handler 202 and Intelligent Objects 200. Methods and processes are
provided, which detect and extract direct information required for
object-to-object queries provided by the Intelligent Objects and
their data content; Intelligent Object Pool 204 ; iPools 155;
components; and access interfaces; the information including but
not limited to Intelligent Object data content accessed via vector
pointers; meta-data indices; including meta-data for content
information; attribute information; relationship information;
protocols; ontologies; annotations and other data-enabling
information; provided directly to the Intelligent
Object-to-Intelligent Object Query component.
[0324] Additionally, methods and processes are provided, which
route information derived from the Intelligent
Object-to-Intelligent Object Query component directly to the
Intelligent Object Pool 204; iPools 155; components and access
interfaces. Automated and/or interactive methods and processes are
provided, including but not limited to access control for
information interchange to and from the Intelligent Objects,
Intelligent Object Pool 204, iPools 155, components and interfaces.
Comprised methods and processes also include provision of the
Intelligent Object-to-Intelligent Object Query component (OQMc) 455
activity information to access interfaces and components as
required for utility including but not limited to synchronization
of the component information interchange and activity recordation
of the component information interchange and activity; auditing of
the component information interchange and activity; validation of
the component information interchange and activity.
[0325] In an exemplary aspect, the Intelligent
Object-to-Intelligent Object Query interface (OQMi) 456 comprised
as a module within an information technology platform for
Intelligent Objects. In an optional aspect, the Intelligent
Object-to-Intelligent Object Query interface (OQMi) 456 comprised
as a module within an information technology platform using
object-oriented data structures.
[0326] iPool-to-iPool Query Component (PPQc) 457
[0327] The iPool-to-iPool Query component (PPQc) 457 is primarily
responsible for query optimization based on provision of meta-data
definitions for accessing, routing, and processing, of direct
iPool-meta-data to iPool-meta-data information interchange,
utilizing direct interchange of linked meta-data index information;
corresponding to automated and/or user-directed queries and/or
processing requests.
[0328] In an exemplary embodiment, an iPool-to-iPool Query
component (PPQc) 457 contains a set of software instructions
comprising meta-data indices methods and processes including
bi-directional information interchange with components and access
interfaces including but not limited to the following Unified
Presentation Layer; iPool-to-iPool Query interface______; and
meta-data indices components. A set of meta-data indices
definitions, which provide automated and/or interactive methods and
processes including query optimization based on provision of
meta-data definitions for accessing; routing and processing; of
direct iPool meta-data-to-iPool meta-data information interchange;
utilizing direct interchange of linked meta-data index information;
corresponding to automated and/or user-directed queries and/or
processing requests. A set of meta-data indices definitions, which
provide automated and/or interactive methods and processes
including IPool-to-IPool information interchange optimization based
on organization of the IPool content and meta-data linking
according to information including but not limited to content
attributes; validation state; ranking; relationships; associations;
and the like, and for query optimization based on dynamic updating
of the IPool organization for information interchange according to
correspondence between the meta-data information and queries; query
histories; commands; command histories; and/or other access,
routing and processing actions. A set of meta-data indices
definitions, which provide automated and/or interactive methods and
processes including query optimization based on user defined
updating and management of the IPool organization for information
interchange.
[0329] In an exemplary aspect, the iPool-to-iPool Query component
(PPQc) 457 comprised as a module within an information technology
platform for Intelligent Objects. In an optional aspect, the
iPool-to-iPool Query component (PPQc) 457 comprised as a module
within an information technology platform 151 using object-oriented
data structures.
[0330] iPool-to-iPool Query Interface (PPQi) 458
[0331] The iPool-to-iPool Query interface (PPQi) 458 is primarily
responsible for detecting and extracting direct information desired
or required for iPool-to-iPool queries provided by the Intelligent
Objects 200 and their data content, Intelligent Object Pool 204,
iPools 155, components, and access interfaces; where the
information may include but not limited to one or more of meta-data
indices including meta-data for content information, attribute
information, relationship information, protocols, ontologies,
annotations and other data-enabling information; provided directly
to the iPool-to-iPool Query component. It may also be responsible
for routing information derived from the iPool-to-iPool Query
component (PPQc) 457 directly to the Intelligent Object Pool (IOP)
204, iPools 155, components and access interfaces.
[0332] In an exemplary embodiment, an iPool-to-iPool Query
interface (PPQi) 458 contains a set of software instructions
comprising meta-data indices definitions, methods and processes
including bi-directional information interchange with components
and access interfaces including but not limited to the following
iPool-to-iPool Query component (PPQc) 457; Intelligent Object Pool
access interfaces; Intelligent Object Handler 202 and Intelligent
Objects 200. Methods and processes are provided, which detect and
extract direct information required for iPool-to-iPool queries
provided by the Intelligent Objects 200 and their data content;
Intelligent Object Pool 204; iPools 155; components; and access
interfaces; the information including but not limited to meta-data
indices; including meta-data for content information; attribute
information; relationship information; protocols; ontologies;
annotations and other data-enabling information; provided directly
to the iPool-to-iPool Query component.
[0333] Additionally, methods and processes are provided, which
route information derived from the iPool-to-iPool Query component
(PPQc) 457 directly to the Intelligent Object Pool (IOP) 204;
iPools 155; components and access interfaces. Automated and/or
interactive methods and processes are provided, including but not
limited to access control for information interchange to and from
the Intelligent Objects 200, Intelligent Object Pool 204, iPools
155, components and access interfaces. Methods and processes also
include provision of the iPool-to-iPool Query component (PPQc) 457
activity information to access interfaces and components as
required for utility including but not limited to synchronization
of the component information interchange and activity recordation
of the component information interchange and activity; auditing of
the component information interchange and activity; validation of
the component information interchange and activity.
[0334] In an exemplary embodiment, the iPool-to-iPool Query
interface (PPQi) 458 comprises a module within an information
technology platform for Intelligent Objects. In another embodiment,
the iPool-to-iPool Query interface (PPQi) 458 comprises an optional
module within an information technology platform (ITP) 151 using
object-oriented data structures 152.
[0335] Other modules, components, and interfaces 459, 460, 461,
462, 463, 464 may optionally be provided to satisfy particular
application, data handling, processing, interfacing, or other needs
as the system and architecture, as well as the method are readily
expandable.
[0336] Exemplary Pool Content Access Architecture, Methods and
Protocols
[0337] The Pool Content Access architecture, methods and protocols
are primarily responsible for providing content access and
presentation definitions for significance detection,
meta-definition aggregation, and result aggregation, results
generation, and relationship viewing. It may also provide for
distributed learning and knowledge extraction.
[0338] The Pool Content Access architecture and Pool Content Access
methods defined herein provide content access and presentation
definitions for significance detection, result aggregation and
results generation. Pool Content Access protocols include modules
comprising components and access interfaces for aggregate
meta-definition, result aggregation and relationship viewing. In
optional and preferred embodiments, the Pool Content Access
Protocols may include modules for distributed learning and
knowledge extraction; such as Distributed Learning Engines
(DLEc/DLEi) 479, 480 and Distributed Extraction Engine interfaces
(DLEi) 480; and/or Knowledge Extraction Engines (KEEc/KEEi) 481,
482 and Knowledge Extraction Engine interfaces (KEEi) 482.
[0339] Data Pool Content Access and Presentation Protocols 420
[0340] The Data Pool Content Access and presentation protocols 420
are primarily responsible for one or more of the following:
providing meta-data index generation of aggregated Intelligent
Object meta-data based on query parameters, significance detection
of values located within the global data pool based on query
parameters and global object meta-data index content, organizing
meta-data index based object relationships within individual ipools
to allow for real-time result aggregation and real-time exclusion
of irrelevant object data layers, and optionally distributed
learning and knowledge extraction.
[0341] In one embodiment of the inventive system, method, and
computer program product, the Data Pool Content Access and
presentation protocols, comprise: (a) an Aggregate Meta-data Index
Generator which provides for meta-data index generation of
aggregated Intelligent Object meta-data, based on query parameters;
(b) an Aggregate Real-time Significance Generator which provides
for significance detection of values located within the global data
pool based on query parameters and global object meta-data index
content; (c) an Object Property-Selective Pre-sorting Tool referred
to as the IMO Zoom (IMO-Z), which organizes meta-data index based
object relationships within individual iPools to allow for
real-time result aggregation and real-time exclusion of irrelevant
object data layers; and (d) in optional and advantageous
embodiments, additional modules that provide for distributed
learning and knowledge extraction.
[0342] In an exemplary embodiment, a set of Pool Content Access
definitions contains sets of software instructions comprising
methods and processes, including bi-directional information
interchange with components and access interfaces including but not
limited to the following: an external Intelligent Object Handler
202 (IOH); Intelligent Object Pools (IOP) 204; iPools 155; and
Intelligent Objects or Intelligent Molecular Objects 200. The Pool
Content Access definitions include automated and/or user-directed
processes, methods and algorithms including but not limited to
real-time significance detection; results aggregation; methods
application; relationship viewing; and report generation utilizing
results generated by the Pool Content Access definition modules
including by object-to-analysis tools interactions; real-time
significant answer generation; result merging algorithms and
clustering algorithms; utilizing calculation methods not limited to
Jacquard coefficient; Dice coefficient; Jeffrey coefficient;
Pearson coefficient; simple matching; product moment correlation
coefficient; mean square difference and absolute difference; and
clustering methods not limited to minimum variance; single,
complete, average and weighted average linking; median method;
centroid method; neighbor joining method; Fitch-Margoliash least
square method and Fitch-Margoliash evolutionary clock method.
[0343] In advantageous and optional embodiments, modules including
one or more of learning and knowledge extraction algorithms are
provided. In another embodiment, the Pool Content Access
definitions are provided within an information technology platform
(ITP) 151 using object-oriented data structures 152.
[0344] Aggregate Meta-data Index Generator Component (aMDXc)
471
[0345] The Aggregate Meta-data Index Generator component (aMDXc)
471 is primarily responsible for providing Pool Content Access
enabling bi-directional information interchange with components and
access interfaces, and providing automated and/or interactive
methods and processes including generation of meta-data definitions
for iPool data subsets.
[0346] In an exemplary embodiment, an Aggregate Meta-data Index
Generator component (aMDXc) 471 contains a set of software
instructions comprising Pool Content Access enabling bi-directional
information interchange with components and access interfaces
including but not limited to Unified Presentation Layer; Aggregate
Meta-data Index Generator interface (aMDXi) 472; and Pool Content
Access components. Additionally, a set of Pool Content Access
definitions are comprised which provide automated and/or
interactive methods and processes including generation of meta-data
definitions for iPool data subsets; including but not limited to
automatically provided and/or user-defined information regarding
Intelligent Object and iPool data Intelligent Object data content
accessed via vector pointers and meta-data index definitions;
including meta-data for iPool subset attributes; annotations;
protocols; ontologies; content attributes; data type, structure,
and access dependencies; ownership and access information;
relationship information; activity ranking; significance ranking
and validation ranking.
[0347] For meta-data index generation, the information is
organized, sorted and ranked according to comprised algorithms for
determination of such as not limited to structure, function,
validation, dependency, similarity; association; contiguity;
proximity, weight and frequency, for such as query optimization
requirements, based on correspondence of submitted query
parameters; and query histories; to iPool Meta-data index content;
and iPool access organization. Generation of iPool meta-data
information required for methods and processes including but not
limited to iPool activation; iPool sorting; iPool-to-iPool queries;
iPool data presentation and result generation.
[0348] In an exemplary aspect, the Aggregate Meta-data Index
Generator component (aMDXc) 471 comprised as a module within an
information technology platform for Intelligent Objects. In an
optional aspect, the Aggregate Meta-data Index Generator component
(aMDXc) 471 comprised as a module within an information technology
platform using object-oriented data structures.
[0349] Aggregate Meta-data Index Generator Interface (aMDXi)
472
[0350] The Aggregate Meta-data Index Generator interface (aMDXi)
472 is primarily responsible for detecting and extracting direct
information required for meta-data index generation, provided by
the Intelligent Objects 200 and their data content; Intelligent
Object Pool 204, iPools 155, components and access interfaces. It
may also be responsible for routing information derived from the
Aggregate Meta-data Index Generator component (aMDXc) 471 directly
to the Intelligent Object 200, Intelligent Object Handler 202,
Pool, and iPools 155, components and access interfaces.
[0351] In an exemplary embodiment, an Aggregate Meta-data Index
Generator interface (aMDXi) 472 contains a set of software
instructions comprising Pool Content Access methods and processes
including bi-directional information interchange with components
and access interfaces of and/or including but not limited to the
following Aggregate Meta-data Index Generator component (aMDXc)
471; Intelligent Object Pool access interfaces; Intelligent Object
Handler 202; and Intelligent Molecular Objects 200. Methods and
processes are provided, which detect and extract direct information
required for meta-data index generation, provided by the
Intelligent Objects 200 and their data content; Intelligent Object
Pool 204; iPools 155; components; and access interfaces; the
information including but not limited to Intelligent Object data
content accessed via vector pointers; meta-data indices; including
meta-data for content information; attribute information
relationship information; protocols; ontologies; annotations; and
other data-enabling information; provided directly to the Aggregate
Meta-data Index Generator component (aMDXc) 471.
[0352] Additionally, methods and processes are provided, which
route information derived from the Aggregate Meta-data Index
Generator component (aMDXc) 471 directly to the Intelligent Object
200, Intelligent Object Handler 202 , Pool and iPools 155;
components and access interfaces. Automated and/or interactive
methods and processes for . . . are provided, including but not
limited to access control for information interchange to and from
the Intelligent Objects 200, Intelligent Object Pool 204, iPools
155, components and interfaces. Comprised methods and processes
also include provision of Aggregate Meta-data Index Generator
component (aMDXc) 471 activity information to access interfaces and
components as required for utility including but not limited to
synchronization of the component information interchange and
activity recordation of the component information interchange and
activity; auditing of the component information interchange and
activity; validation of the component information interchange and
activity.
[0353] In an exemplary aspect, the Aggregate Meta-data Index
Generator interface (aMDXi) 472 comprised as a module within an
information technology platform for Intelligent Objects. In an
optional aspect, the Aggregate Meta-data Index Generator interface
(aMDXi) 472 comprised as a module within an information technology
platform using object-oriented data structures.
[0354] Aggregate Real-time Significance Generator Component (aRSGc)
473
[0355] The Aggregate Real-time Significance Generator (aRSGc) 473
is primarily responsible for the application of algorithms and
procedures, such as applications and procedures that perform
normalized comparison, generate correlation and significance
detection information including but not limited to Intelligent
Object properties, data content values, and meta-data index
content; based on query parameters. It may also be responsible for
organizing or enabling organization of Intelligent Object and
iPools.
[0356] In an exemplary embodiment an Aggregate Real-time
Significance Generator component (aRSGc) 473 contains a set of
software instructions comprising Pool Content Access methods and
processes including bi-directional information interchange with
components and access interfaces including but not limited to the
following Unified Presentation Layer, Aggregate Real-time
Significance Generator interface (aRSGi) 474 and Pool Content
Access components. Additionally a set of Pool Content Access
definitions are included, which provide automated and/or
interactive methods and processes including application of
algorithms for normalized comparison; correlation and significance
detection information including but not limited to Intelligent
Object properties; data content values and meta-data index content;
based on parameters fielded by all valid queries. The Aggregate
Real-time Significance Generator enables organization of
Intelligent Object(s) and iPools, utilizing methods and processes
including assembly; ranking; classification; and/or tabulation of
Intelligent Objects; according to comparison of normalized
parameters such as data content values; and meta-data index
properties provided by direct Intelligent Object-to-Intelligent
Object; and iPool-to-iPool information interchange; via addressing
including but not limited to vector subset pointing; meta-data
index addressing; and activated by such as query submissions, user
preference rules and/or viewing instructions.
[0357] The Aggregate Real-time Significance Generator component
(aRSGc) 473 enables organization of data utilizing methods and
processes including assembly; ranking; classification; and/or
tabulation of Intelligent Objects; based on the significance of
results generated by external applications; components; access
interfaces; and activated by query submissions, user preference
rules and/or viewing instructions.
[0358] In an exemplary embodiment, the Aggregate Real-time
Significance Generator component (aRSGc) 473 comprises a module
within an information technology platform for Intelligent Objects.
In another embodiment, the Aggregate Real-time Significance
Generator component optionally comprises a module within an
information technology platform using object-oriented data
structures.
[0359] Aggregate Real-time Significance Generator Interface (aRSGi)
474
[0360] The Aggregate Real-time Significance Generator interface
(aRSGi) 474 is primarily responsible for detecting and extracting
direct information required for significance generation provided by
the Intelligent Objects and their data content, Intelligent Object
Pool 204, iPools 155, components and access interfaces. It may also
be responsible for information derived from the Aggregate Real-time
Significance Generator component (aRSGc) 473 directly to the
Intelligent Object Pool 204, iPools 155, components, and access
interfaces; and for access control for information interchange to
and from the Intelligent Objects, Intelligent Object Pool 204,
iPools 155, components and interfaces. It may further be
responsible for aggregate real-time or near-real-time significance
generation activity information to access interfaces and components
as required for utility.
[0361] In an exemplary embodiment, an Aggregate Real-time
Significance Generator interface (aRSGi) 474 contains a set of
software instructions comprising Pool Content Access methods and
processes including bi-directional information interchange with
components and access interfaces of and/or including but not
limited to the following Aggregate Real-time Significance Generator
component (aRSGc) 473; Intelligent Object Pool access interfaces;
Intelligent Object Handler 202; and Intelligent Molecular Objects
200. Methods and processes are provided, which detect and extract
direct information required for significance generation provided by
the Intelligent Objects and their data content; Intelligent Object
Pool 204; iPools 155; components; and access interfaces; the
information including but not limited to Intelligent Object data
content accessed via vector pointers; meta-data indices; including
meta-data for content information; attribute information
relationship information; protocols; ontologies; annotations; and
other data-enabling information; provided directly to the Aggregate
Real-time Significance Generator component.
[0362] Additionally, methods and processes are provided, which
route information derived from the Aggregate Real-time Significance
Generator component (aRSGc) 473 directly to the Intelligent Object
Pool 204; iPools 155; components; and access interfaces. Automated
and/or interactive methods and processes are provided, including
but not limited to access control for information interchange to
and from the Intelligent Objects, Intelligent Object Pool 204,
iPools 155, components and interfaces. Comprised methods and
processes also include provision of Aggregate Real-time
Significance Generator component (aRSGc) 473 activity information
to access interfaces and components as required for utility
including but not limited to synchronization of the component
information interchange and activity recordation of the component
information interchange and activity; auditing of the component
information interchange and activity; validation of the component
information interchange and activity.
[0363] In an exemplary aspect, the Aggregate Real-time Significance
Generator interface (aRSGi) 474 comprised as a module within an
information technology platform for Intelligent Objects. In an
optional aspect, the Aggregate Real-time Significance Generator
interface (aRSGi) 474 comprised as a module within an information
technology platform using object-oriented data structures.
[0364] Result Aggregation Engine Component (RAEc) 224, 475
[0365] The Result Aggregation Engine Component (RAEc) 224, 475 is
primarily responsible for aggregation of results provided by the
aggregate significance generator module based on input or generated
parameters and/or inquiries, such as for example queries, commands,
processing requests, alerts, updates, and reports; such as may be
presented by an object handler such as by Intelligent Object
Handler 202. It may also be responsible for generating customized
meta-data profiles, and/or generating aggregated meta-data outputs
and updates such as iPool definition updates, iPool relationship
definitions, Intelligent Object definition updates, and Intelligent
Object relationship definitions pertaining to Intelligent Object
and iPool properties and values provided by the Aggregate
Significance Generator module. It may also be responsible for
results assembly, validation, ranking, classification, and
tabulation; for example, according to significance and relationship
information provided by the Aggregate Significance Generator
module.
[0366] In an exemplary embodiment, a Result Aggregation Engine
component (RAEc) 224, 475, which contains a set of software
instructions comprising Pool Content Access methods and processes
including bi-directional information interchange with components
and access interfaces including but not limited to the following
Unified Presentation Layer; Result Aggregation Engine interface
(RAEi) 225, 476, and Pool Content Access components. The Result
Aggregation Engine Component is responsible for aggregation of
results provided by the Aggregate Significance Generator module,
according to parameters submitted via automated; and/or
user-directed queries; commands; processing requests; alerts;
updates; and reports; and presented via an external Intelligent
Object Handler 202; and to generate customized meta-data profiles;
and/or generate aggregated meta-data outputs and updates such as
iPool definition updates; iPool relationship definitions;
Intelligent Object definition updates; and Intelligent Object
relationship definitions pertaining to Intelligent Object and iPool
properties and values provided by the Aggregate Significance
Generator module. A set of methods and processes, which provide
results assembly, validation, ranking, classification, and
tabulation; according to significance and relationship information
provided by the Aggregate Significance Generator module.
[0367] In an exemplary embodiment, the Result Aggregation Engine
component (RAEc) 475 comprised as a module within an information
technology platform for Intelligent Objects. In another embodiment,
the Result Aggregation Engine component (RAEc) 475 comprises a
module within an information technology platform using
object-oriented data structures.
[0368] Result Aggregation Engine Interface (RAEi) 225, 476
[0369] The Result Aggregation Engine Interface (RAEi) 225, 476 is
primarily responsible for detecting and extracting direct
information required for result aggregation, provided by the
Intelligent Objects and their data content, Intelligent Object Pool
204, iPools 155, components, and access interfaces. It may also be
responsible for routing information derived from the Result
Aggregation Engine component (RAEc) 475 to the Intelligent Object,
Intelligent Object Handler, Pool and iPools, and components and
access interfaces. It may further be responsible for access control
for information interchange to and from the Intelligent Objects,
Intelligent Object Pool 204, iPools 155, components and
interfaces.
[0370] In an exemplary embodiment, a Result Aggregation Engine
interface (RAEi) 476 contains a set of software instructions
comprising Pool Content Access methods and processes including
bi-directional information interchange with components and access
interfaces of and/or including but not limited to the following
Result Aggregation Engine component (RAEc) 475; Intelligent Object
Pool access interfaces; Intelligent Object Handler 202; and
Intelligent Molecular Objects 200. Methods and processes are
provided, which detect and extract direct information required for
result aggregation, provided by the Intelligent Objects and their
data content; Intelligent Object Pool 204; iPools 155; components;
and access interfaces; the information including but not limited to
Intelligent Object data content accessed via vector pointers;
meta-data indices; including meta-data for content information;
attribute information relationship information; protocols;
ontologies; annotations; and other data-enabling information;
provided directly to the Result Aggregation Engine component (RAEc)
475. Additionally, methods and processes are provided, which route
information derived from the Result Aggregation Engine component
(RAEc) 475 directly to the Intelligent Object, Intelligent Object
Handler 202, Pool and iPools 155; components and access interfaces.
Automated and/or interactive methods and processes are provided,
including but not limited to access control for information
interchange to and from the Intelligent Objects, Intelligent Object
Pool 204, iPools 155, components and interfaces. Comprised methods
and processes also include provision of Result Aggregation Engine
component (RAEc) 224,475 activity information to access interfaces
and components as required for utility including but not limited to
synchronization of the component information interchange and
activity recordation of the component information interchange and
activity; auditing of the component information interchange and
activity; validation of the component information interchange and
activity.
[0371] In an exemplary embodiment, the Result Aggregation Engine
interface (RAEi) 225, 476 comprised as a module within an
information technology platform for Intelligent Objects. In an
optional aspect, the Result Aggregation Engine interface (RAEi) 476
optionally comprises a module within an information technology
platform using object-oriented data structures.
[0372] IMO Zoom Component (IMO-Zc) 477
[0373] The IMO Zoom component (IMO-Zc) 477 is primarily responsible
for access, extraction, processing and addressing of information
including of graphical viewing information; including but not
limited to data relationship information and overviews,
self-organizing maps, similarity clustering, dendrograms, charts,
tables, and/or additional graphical representations, and
combinations thereof. It may also be responsible for enabling
thumbnail viewing of Intelligent Object data content, iPool data
content, according to correspondence of data content attributes to
automated and/or user-directed queries and/or commands. It may
further be responsible for enabling graphical pre-sorting and
sorting of Intelligent Object data content, and iPool data content,
according to correspondence of data content attributes to automated
and/or user-directed queries and/or commands.
[0374] An IMO Zoom component (IMO-Zc) 477 contains a set of
software instructions comprising Pool Content Access methods and
processes including but not limited to bi-directional information
interchange with components and access interfaces including but not
limited to the following Unified Presentation Layer; IMO Zoom
interface (IMO-Zi) 478; and Pool Content Access components.
Comprised methods and processes also include access; extraction;
processing and addressing of graphical viewing information;
including but not limited to data relationship information and
overviews; self-organizing maps, similarity clustering;
dendrograms; charts; tables; and/or additional graphical
representations. Additionally, a set of instructions enable
thumbnail viewing of such as Intelligent Object data content; iPool
data content; according to correspondence of data content
attributes to automated and/or user-directed queries and/or
commands. Additionally, a set of instructions enable user-directed
and/or automated graphical pre-sorting and sorting of such as the
Intelligent Object data content; iPool data content; according to
correspondence of data content attributes to automated and/or
user-directed queries and/or commands. The information being
accessed from and routed to the Intelligent Object data;
Intelligent Object Pool 204 data; iPool data; modules, components,
and access interfaces.
[0375] Example of Code for Clustering of Intelligent Object Data
within an IOP
[0376] The following example of computer program software code for
clustering of Intelligent Object data within the Intelligent Object
Pool 204 for viewing and analysis utilizes content attributes and
attribute flags of the corresponding objects. The Cluster Dialog
box, which is evoked below, allows for selection of the adequate
calculation method for the similarity coefficient (e.g. Jacquard,
Dice, Jeffrey, Pearson, and/or other algorithms or procedures as
are known in the art), the parameter the clustering is performed
upon (e.g. spot intensity, spot volume, spot color, spot position,
band position, band concentration, band molecular weight, band
iso-electric point, and the like), and the method for clustering
applied to display numerically and/or graphically the data
relationships (such as Minimum Variance [MVAR], Minimum Variance
Hierarchy [MNVR], Single Link [SLNK], Complete Link [CLNK], Average
Link [ALNK], Weighted Average Link [WLNK], Median Method, Centroid
Method, Minimum Spanning Tree [MST], Neighbor-Joining Method, FMLS,
FMLS with Evolutionary Clock and the like). User preferred
clustering parameters can be saved and reloaded to be applied to
similar data objects consequently.
[0377] Table I. Exemplary computer program code for performing the
procedure
2TABLE I Exemplary computer program code for performing an
embodiment of the procedure. // CClusterDlg dialog class
CClusterDlg : public Cdialog { // Construction public:
CClusterDlg(CWnd* pParent = NULL); // standard constructor //
Dialog Data //{{AFX_DATA(CClusterDlg) enum {IDD = IDD_CLUSTER}; int
m_nCalibMethod; //}}AFX_DATA // Overrides // Class Wizard generated
virtual function overrides //{{AFX_VIRTUAL(CClusterDlg) protected:
virtual void DoDataExchange(CDataExchange* pDX); // DDX/DDV support
//}}AFX_VIRTUAL // Implementation protected: // Generated message
map functions //{{(AFX_MSG(CClusterDlg) //}}AFX_MSG
DECLARE_MESSAGE_MAP( ) }; // CClusterDlg dialog
CClusterDlg::CclusterDlg(CWnd* pParent/*=NULL*/) :
CDialog(CClusterDlg::IDD, pParent) { //{{AFX_DATA_INIT(CclusterDlg)
m_nCalibMethod = 0; //}}AFX_DATA_INIT } void
CClusterDlg::DoDataExchange(CDataE- xchange* pDX) {
//}}AFX_DATA_MAP(CclusterDlg) DDX_CBIndex(pDX, IDC_CALIBMETHOD,
m_nCalibMethod); //}}AFX_DATA_MAP } BEGIN_MESSAGE_MAP(CClusterDlg,
Cdialog) //{{AFX_MSG_MAP(CClusterDlg) //}}AFX_MSG_MAP
END_MESSAGE_MAP( ) . . . BEGIN_MESSAGE_MAP(CcontentAttribDl- g,
CDialog) //{{AFX_MSG_MAP(CcontentAttribDlg) //}}AFX_MSG_MAP
END_MESSAGE_MAP( ) CContentAttribDlg::Ccont- entAttribDlg(CWnd*
pParent/*=NULL*/) : CDialog(CcontentAttribDlg::- IDD, pParent) {
//{{AFX_DATA_INIT(CcontentAttribDlg) // NOTE: the ClassWizard will
add member initialization here //}}AFX_DATA_INIT } void
CcontentAttribDlg::DoDataExchange(- CDataExchange* pDX) {
//{{AFX_DATA_MAP(CcontentAttribDlg) DDX_Control(pDX, IDC_TREE1,
m_treeContentAttrib); //}}AFX_DATA_MAP } BOOL
CcontentAttribDlg::OnInitDialog( ) { CDialog::OnInitDialog( );
FillTree( ); return TRUE; // return TRUE unless you set the focus
to a control // EXCEPTION: OCX Property Pages should return FALSE }
void CcontentAttribDlg::FillTree( ) { // create the tree BYTE flag
= 0; CString str = _T(" "); HTREEITEM parent = TVI_ROOT; for(int i
= 0; i < 1000; i++) { GetLutContentAttrib(i, str, flag);
if(!str.IsEmpty( )) { if(flag & 1) { parent =
m_treeContentAttrib.InsertItem(str, parent); WORD l = flag; WORD h
= (WORD)i; DWORD f = MAKELONG(l, h);
m_treeContentAttrib.SetItemData(parent, f); } if(flag & 2) {
CString fmt = _T(" "); fmt.Format(_T("(%.3d) - %s"), i, str);
HTREEITEM item = m_treeContentAttrib.InsertItem(fmt, parent); WORD
l = flag; WORD h = (WORD)i; DWORD f = MAKELONG(l, h);
m_treeContentAttrib.SetItemData(item, f); } if(flag & 4) parent
= m_treeContentAttrib.GetParentItem(parent); if(flag & 8) {
parent = m_treeContentAttrib.GetParentIt- em(parent); parent =
m_treeContentAttrib.GetParentItem(parent); } if(flag & 16) {
parent = m_treeContentAttrib.GetParentItem(parent); parent =
m_treeContentAttrib.GetParentItem(parent); parent =
m_treeContentAttrib.GetParentItem(parent); } if(flag & 32) {
parent = m_treeContentAttrib.GetParentItem(parent- ); parent =
m_treeContentAttrib.GetParentItem(parent); parent =
m_treeContentAttrib.GetParentItem(parent); parent =
m_treeContentAttrib.GetParentItem(parent); } } } } (End Code
Example)
[0378] In an exemplary embodiment, the IMO Zoom component (IMO-Zc)
477 comprises a module within an information technology platform
for Intelligent Objects. In another embodiment, the IMO Zoom
component (IMO-Zc) 477 optionally comprises a module within an
information technology platform using object-oriented data
structures.
[0379] IMO Zoom Interface (IMO-Zi) 478
[0380] The IMO Zoom interface (IMO-Zi) 478 is primarily responsible
for detection of graphical content provided by the Intelligent
Objects 200 and their data content, Intelligent Object Pool 204,
iPools 155, components and access interfaces; accessing and routing
of the information via such means as vectorized data field
pointers, and meta-data index definitions, and provided by the IMO
Zoom component. It may also be responsible for routing graphical
information derived from the IMO Zoom component (IMO-Zc) 477 to the
Intelligent Object Pool 204, iPools 155, components, and access
interfaces (interfaces or module/component interfaces). It may
further be responsible for providing and/or enabling information
interchange to and from the Intelligent Objects, Intelligent Object
Pool 204, iPools 155, components and interfaces.
[0381] An IMO Zoom interface (IMO-Zi) 478 contains a set of
software instructions comprising Pool Content Access methods and
processes including bi-directional information interchange with
components and access interfaces including but not limited to the
following IMO Zoom component (IMO-Zc) 477; Intelligent Object Pool
access interfaces; Intelligent Object Handler 202; and Intelligent
Molecular Objects 200. Methods and processes are provided
comprising detection of graphical content provided by the
Intelligent Objects 200 and their data content; Intelligent Object
Pool 204; iPools 155; components; and access interfaces; accessing
and routing of the information via such as vectorized data field
pointers; and meta-data index definitions; and provided by the IMO
Zoom component 477.
[0382] Additionally, methods and processes are provided, which
route graphical information derived from the IMO Zoom component
(IMO-Zc) 477 to the Intelligent Object Pool 204; iPools 155;
components; and access interfaces. Automated and/or interactive
methods and processes are provided, including but not limited to
access control for information interchange to and from the
Intelligent Objects, Intelligent Object Pool 204, iPools 155,
components and interfaces. Comprised methods and processes also
include provision of IMO Zoom component (IMO-Zc) 477 activity
information to access interfaces and components as required for
utility including but not limited to synchronization of the
component information interchange and activity recordation of the
component information interchange and activity; auditing of the
component information interchange and activity; validation of the
component information interchange and activity.
[0383] In an exemplary embodiment, the IMO Zoom interface (IMO-Zi)
478 comprises a module within an information technology platform
151 for Intelligent Objects. In another embodiment, the IMO Zoom
interface (IMO-Zi) 478 comprises a module within an information
technology platform using object-oriented data structures.
[0384] Distributed Learning Engine Component (DLEc) 479
[0385] The Distributed Learning Engine Component (DLEc) 479, when
optionally present, is primarily responsible for knowledge
extraction and is optionally provided in the inventive system,
method, and computer program.
[0386] In one embodiment, a Distributed Learning Engine component
(DLEc) 479 contains a set of software instructions comprising Pool
Content Access methods and processes including bi-directional
information interchange with components and access interfaces
including but not limited to the following Unified Presentation
Layer, Distributed Learning Engine interface (DLEi) 480 and Pool
Content Access components. Methods and processes for knowledge
extraction utilizing automated and/or user-directed interactive
application of comprised protocols, functions and algorithms to
information and actions including but not limited to user queries;
automated queries; customized sets of queries; data objects such as
the Intelligent Object; data handlers such as the Intelligent
Object Handler 202; data pools such as the Intelligent Object Pool
204; iPools 155; the global data entity; and/or data subsets
therein. Methods and processes are provided for the automation of
learning processes including but not limited to simulation;
prediction, hypothesis generation, testing, result assessment and
comparison; interactive result optimization and feedback; data and
user workspace definition; assessment; organization; customization;
data and user workspace relationship definition; assessment;
organization; customization; and/or optimization of accessing,
routing and processing protocols for user workspaces; Intelligent
Objects; Intelligent Object Pools and iPools 155; utilizing a
comprised set of Pool Content Access definitions, which may include
but are not limited to Cross-reference clustering; Boolean network
algorithms; Bayesian network algorithms; neural network algorithms;
iterative learning algorithms; time series analysis algorithms;
pattern matching algorithms; structure matching algorithms; and
rule induction algorithms. Methods are provided for the application
of the algorithms to the information to enable such as automated,
dynamic and/or interactive iterative processing; significance
detection; tabulation; validation; ranking; assembly; and/or other
forms of distributed learning.
[0387] In another embodiment, the Distributed Learning Engine
component (DLEc) 479 comprised as a module within an information
technology platform for Intelligent Objects. In another embodiment,
the Distributed Learning Engine component (DLEc) 479 optionally
comprises a module within an information technology platform using
object-oriented data structures.
[0388] Distributed Learning Engine Interface (DLEi) 480
[0389] The Distributed Learning Engine interface (DLEi) 480, when
optionally present, is primarily responsible for detecting and
extracting direct information required for distributed learning,
provided by the Intelligent Objects and their data content,
Intelligent Object Pool 204, iPools 155, components and access
interfaces, and is optionally provided in the inventive system,
method, and computer program.
[0390] In an optional embodiment, a Distributed Learning Engine
interface (DLEi) 480 contains a set of software instructions
comprising Pool Content Access methods and processes including
bi-directional information interchange with components and access
interfaces of and/or including but not limited to the following
Distributed Learning Engine component (DLEc) 479; Intelligent
Object Pool access interfaces; Intelligent Object Handler 202; and
Intelligent Molecular Objects 200. Methods and processes are
provided, which detect and extract direct information required for
distributed learning, provided by the Intelligent Objects and their
data content; Intelligent Object Pool 204; iPools 155; components;
and access interfaces; the information including but not limited to
Intelligent Object data content accessed via vector pointers;
meta-data indices; including meta-data for content information;
attribute information relationship information; protocols;
ontologies; annotations; and other data-enabling information;
provided directly to the Distributed Learning Engine component
(DLEc) 479. Additionally, methods and processes are provided, which
route information derived from the Distributed Learning Engine
component (DLEc) 479 directly to the Intelligent Object,
Intelligent Object Handler 202, Pool and iPools 155; components and
access interfaces. Automated and/or interactive methods and
processes are provided, including but not limited to access control
for information interchange to and from the Intelligent Objects,
Intelligent Object Pool 204, iPools 155, components and interfaces.
Comprised methods and processes also include provision of
Distributed Learning Engine component (DLEc) 479 activity
information to access interfaces and components as required for
utility including but not limited to synchronization of the
component information interchange and activity recordation of the
component information interchange and activity; auditing of the
component information interchange and activity; validation of the
component information interchange and activity.
[0391] In an exemplary embodiment, the Distributed Learning Engine
interface (DLEi) 480 comprised as a module within an information
technology platform for Intelligent Objects. In another exemplary
embodiment, the Distributed Learning Engine interface comprises a
module within an information technology platform optionally using
object-oriented data structures.
[0392] Knowledge Extraction Engine Component (KEEc) 481
[0393] The Knowledge Extraction Engine component (KEEc) 481, when
optionally present, is primarily responsible for knowledge
extraction utilizing automated and/or user-directed application of
protocols, functions and algorithms to information and actions
including but not limited to user queries, automated queries,
customized sets of queries, data objects such as the Intelligent
Object, data handlers such as the Intelligent Object Handler 202,
data pools such as the Intelligent Object Pool 204, iPools 155, the
global data entity, and/or data subsets therein.
[0394] In an optional embodiment, a Knowledge Extraction Engine
component (KEEc) 481, which contains a set of software instructions
comprising Pool Content Access methods and processes including
bi-directional information interchange with components and access
interfaces including but not limited to Unified Presentation Layer;
Knowledge Extraction Engine interface (KEEi) 482; and Pool Content
Access components. Methods and processes for knowledge extraction
utilizing automated and/or user-directed interactive application of
comprised protocols, functions and algorithms to information and
actions including but not limited to user queries; automated
queries; customized sets of queries; data objects such as the
Intelligent Object; data handlers such as the Intelligent Object
Handler 202; data pools such as the Intelligent Object Pool 204;
iPools 155; the global data entity; and/or data subsets therein.
Methods and processes are provided for the automation of knowledge
extraction processes including but not limited to active data
attribute searching, detection and extraction; similarity and
significance assessment and comparison; hypothesis generation;
report generation; interactive result optimization and feedback;
data annotation and user workspace definition, assessment,
organization and customization and/or optimization; data annotation
and user workspace relationship definition, assessment,
organization and customization and/or optimization of accessing,
routing and processing protocols for user workspaces; Intelligent
Objects; Intelligent Object Pools and iPools 155; utilizing a
comprised set of Pool Content Access definitions, which may include
but are not limited to the following Cross-reference clustering;
Boolean network algorithms; Bayesian network algorithms; neural
network algorithms; iterative knowledge extraction algorithms; time
series analysis algorithms; pattern matching algorithms; structure
matching algorithms; and rule induction algorithms. Methods are
provided for the application of the algorithms to the information
to enable such as automated, dynamic and/or interactive iterative
processing; significance detection; tabulation; validation;
ranking; assembly; and/or other forms of knowledge extraction.
[0395] In an exemplary embodiment, the Knowledge Extraction Engine
component (KEEc) 481 comprised as a module within an information
technology platform for Intelligent Objects. In an optional
embodiment, the Knowledge Extraction Engine component (KEEc) 481
comprised as a module within an information technology platform
using object-oriented data structures.
[0396] Knowledge Extraction Engine Interface (KEEi) 482
[0397] The Knowledge Extraction Engine interface (KEEi) 482, when
optionally present, is primarily responsible for detecting and
extracting direct information required for knowledge extraction,
provided by the Intelligent Objects and their data content,
Intelligent Object Pool 204, iPools 155, components, and access
interfaces; . provided directly to the Knowledge Extraction Engine
component (KEEc) 481. The information may include one or more of
but not limited to Intelligent Object data content accessed via
vector pointers, meta-data indices, including meta-data for content
information, attribute information relationship information,
protocols, ontologies, annotations, and other data-enabling
information.
[0398] In an optional embodiment, a Knowledge Extraction Engine
interface (KEEi) 482 contains a set of software instructions
comprising Pool Content Access methods and processes including
bi-directional information interchange with components and access
interfaces of and/or including but not limited to the following
Knowledge Extraction Engine component (KEEc) 481; Intelligent
Object Pool access interfaces; Intelligent Object Handler 202; and
Intelligent Molecular Objects 200. Methods and processes are
provided, which detect and extract direct information required for
knowledge extraction, provided by the Intelligent Objects and their
data content; Intelligent Object Pool 204; iPools 155; components;
and access interfaces; the information including but not limited to
Intelligent Object data content accessed via vector pointers;
meta-data indices; including meta-data for content information;
attribute information relationship information; protocols;
ontologies; annotations; and other data-enabling information;
provided directly to the Knowledge Extraction Engine component
(KEEc) 481.
[0399] Additionally, methods and processes are provided, which
route information derived from the Knowledge Extraction Engine
component (KEEc) 481 directly to the Intelligent Object,
Intelligent Object Handler 202, Pool and iPools 155; components and
access interfaces. Automated and/or interactive methods and
processes are provided, including but not limited to access control
for information interchange to and from the Intelligent Objects,
Intelligent Object Pool 204, iPools 155, components and interfaces.
Comprised methods and processes also include provision of Knowledge
Extraction Engine component (KEEc) 481 activity information to
access interfaces and components as required for utility including
but not limited to synchronization of the component information
interchange and activity recordation of the component information
interchange and activity; auditing of the component information
interchange and activity; validation of the component information
interchange and activity.
[0400] In an exemplary embodiment, the Knowledge Extraction Engine
interface (KEEi) 482 comprises a module within an information
technology platform for Intelligent Objects. In another embodiment,
the Knowledge Extraction Engine interface (KEEi) 482 comprises a
module within an information technology platform optionally using
object-oriented data structures.
[0401] Through provision of the architecture, methods, and modules,
the infrastructure is provided for secure, unified object storage
and object-to-object and iPool-to-iPool query-based interaction, to
allow for a comprehensive real-time analysis of otherwise
inaccessible, inconsistent data sets. It is evident from the above
description that the data pool architecture and methods described
above allow for efficient real-time processing of terabytes of
complex, multidimensional, interdependent data, thereby providing
real-time answers to queries within heterogeneous data environments
with high data density and dynamic application needs.
[0402] Embodiments of the Intelligent Object Pool 204 implemented
as computer software program code may be compiled to run on any one
or more of multiple platforms and/or operating system environments
known in the art as well as those reasonably expected to develop in
the future, including, but not limited to, UNIX and UNIX-based
platforms and/or environments, Linux and Linux-based platforms
and/or environment, Macintosh operating systems (such as OS 9 and
OS 10) and Macintosh-based based platforms and/or environments, or
any Windows platform and/or Windows-based platforms such as
Microsoft Windows 98, ME, 2000, NT, XP and extensions and
modifications thereof, and/or environment such as any Window 32-bit
operating system, platform, or environment.
[0403] The hardware and system descriptions provided herein are
intended define and describe the requirements for a specific
exemplary embodiment, implemented for a 32-bit Microsoft Windows
environment. However, it will be appreciated that a broad class of
general purpose computing systems may be utilized and that the
inventive methods, procedures, and architectures are by-and-large
hardware, operating system, and network or connectivity
agnostic.
[0404] With reference to illustration in FIG. 9, in a global,
heterogeneous environment, data from diversified sources are
functionally integrated via Intelligent Objects and their
Intelligent Object Pool 204, utilizing automated access definition
protocols, multiple alias integration and addressing, data field
mapping and state management to allow for integrated data access,
analysis and exchange utilizing distributed data content presented
within the Client iPool.
[0405] The chart depicts user relationships to the data on database
and iPool levels. In FIG. 9, the upper section of the diagram (the
section that includes the labels "Client iPool", "Restricted",
"Client Data", "Resources", and the like) depicts data within
intranets, LANs and the like, while the lower, larger part of the
diagram contains several different forms of public and/or otherwise
web-accessible data sources. Other examples of data resources are
depicted in the diagram and are merely exemplary and not limiting
in any way.
[0406] With reference to FIG. 10, it represents another exemplary
embodiment of a process model describing interactions of required
or included modules for an exemplary embodiment of the Intelligent
Object Pool 204.
[0407] In this exemplary process model, data from a global data
resource (or any part of a global data resource) are passed through
an access interface, in this example an access interface having a
security layer and a set of access and/or exchange protocols and
integrity assessment procedures, to the Intelligent Objects.
Lastly, the pathways involved in unified direct
Intelligent-Object-to-Intelligent Object and iPool-to-iPool data
interaction generate the real-time answer and pass it back through
security to the Intelligent Object Handler 202.
[0408] By real time answer we generally mean an answer or results
are received within a relatively short period of time so that a
user inputting a query may wait for a response to the query and not
find the wait objectionable to be contrasted with a batch processed
query or other query that has significant waiting time. In many
instances the results will be generated in less than a second so
that there will be no apparent wait; however, for some queries the
delay in receiving a response with results may be a few seconds,
several seconds, and perhaps several minutes. The invention is not
limited to any particular response time.
[0409] With reference to FIG. 11, there is illustrated a
representation of the graphical user interface window and display
screen image within the unified presentation layer displaying
algorithms included for clustering of Intelligent Object data. In
this example, a number of property panes showing dendrograms
results are seen behind the clustering algorithms menu.
[0410] In this example, and in some of the examples and screen
displays that follow, data from a local subset iPool of Intelligent
Objects were queried against specific protein expressions based on
2-dimensional gel electrophoresis (2DE) data. During the real-time
answer-finding process, object-to-object interactions are
represented via a graphical iPool Viewer (IMO Zoom), which also
accesses relevancy of individual result contributions to generate a
unique, exact, relevant real-time answer.
[0411] With reference to FIG. 12, there is illustrated an example
of an interface iPool Zoom viewer and displayed image for viewing
iPool data relationships, utilizing implemented techniques,
including for example techniques that include one or more of
dendrograms and self-organizing maps (SOM). Attributes and
interactions between individual Intelligent Object (IMO) data
within the set of objects are outlined in the margins of this
exemplary display.
[0412] With reference to FIG. 13, there is illustrated an example
of an iPool Zoom interface for viewing iPool data relationships,
utilizing Principal Components Analysis (PCA).
[0413] Operating and Business Model Providing Information
Services
[0414] Among its inventive aspects, the invention provides a
revolutionary new information technology platform that places the
power (in terms of response time to complex queries and analytical
requests utilizing current tools, such as a common query in
bioinformatics for detection of spots in a 2-D Electrophoresis gel)
of an entire floor of clustered servers or `massively parallel
mainframe` computers (e.g. IBM, COMPAQ, or the like) at the hands
of any scientist or consumer, for that matter, with a computer and
a connection to the web. As a result, information processing,
management and storage in every field imaginable (Life Sciences,
Agribusiness, Large Scale Manufacturing, Physics Imaging, and many
more) are dramatically and revolutionarily more efficient and cost
effective.
[0415] The inventive system, method, and business model is
projected toward an initial market in the Life Sciences industries
as a result of internal expertise and a tremendous and growing need
for the kind of time efficiency and cost effectiveness that our IT
Platform will provide.
[0416] To further facilitate the need of Life Science and Life
Scientist, the invention provides developments and advances toward
a number of product modules (typically implemented as computer
program software for execution on computer systems) ranging from
Drug Discovery, Genomics, Proteomics to Metabolism product modules,
that will reside upon the platform and thereby enable the dramatic
shortening of timelines for new drug development and gene therapies
(while also providing for rapidly, validated diagnosis and
treatment) in a Real-Time and cost effective manner. The
abbreviated timelines will, in turn, provide cost savings for each
new drug to the tune of at least $200 million dollars, while
facilitating sales, based on earlier than expected market entry,
upwards of $2 billion, for each new drug.
[0417] These IT platforms will become a standard, particularly as
purveyors of solutions for substantially improved drug discovery,
via its various Drug Discovery, Genomic and Proteomic product
modules.
[0418] The motivation for the platform architecture and its
associated methods and procedures has arisen largely because, as a
result of the human genome project and other related activities
(genomics and proteomics), Biotech and Pharmaceutical companies are
drowning in a flood of information, information which may hold the
key to powerful and valuable new drug discoveries and gene
therapies.
[0419] Currently, Biotechnology and Pharmaceutical companies are
spending upwards of $40 billion each year to sift through this
information in order to uncover new drug candidates and potential
gene therapies. Despite the vast sums of money being spent, the
task of finding new drug candidates and gene therapies remains
daunting, costly and highly inefficient.
[0420] Some reasons for this can be traced to a number of factors,
several of which include: a variety of different types and kinds of
databases; applications and systems that cannot communicate with
one another; the enormous cost to retool a company's existing
information technology platform; the scarcity of bioinformatics
specialist; and, the lack of appropriate analysis tools.
[0421] As a result, Biotechnology and Pharmaceutical companies are
facing three critical issues. The pressure to reduce cost, to speed
up the entire process for new drug development and to recover
R&D cost more quickly via the sales of new drugs. To date, this
remains a wholly unrealized goal.
[0422] As a part of the solution, the inventive IMO IT platform
provides Biotech and pharmaceutical companies with the ability to
quickly and cost effectively sort through the growing mass of
information to discover and produce drugs in vastly reduced time
periods and at greatly reduced cost. It will be possible as a
result of the IMO.TM. IT platform and drug discovery, proteomics
and genomics modules for Biotech and Pharmaceutical companies to
shorten the drug discovery process by 2 to 4 years and save upwards
of $200 million. Additionally, the various Biotech and
Pharmaceutical companies will benefit from earlier than expected
revenues (several billion dollars), as a result of reduced
development time and thus earlier than anticipated market entry for
each new drug.
[0423] The inventive system and methods therefore also provide or
support a number of new and novel business model and operating
model innovations that satisfy the needs of the information
community as well as provide revenue. Forming corporate strategic
relationships are part of this overall concept.
[0424] Business development efforts related to the inventive
technology include marketing the inventive products and services to
Biotech and Pharmaceutical research companies initially, and to
Life Science companies, in general.
[0425] Heretofore, the major participants in the field of
Biotechnology software for data analysis have comprised the
following three categories: Legacy Data Warehouses, data marts, ERP
data mining tool companies, which provide proprietary applications
and databases; Applications Service Providers (ASPs), Portals and
other web-enabled service providers; and Network Integration
Providers, which provide network integration of public databases,
proprietary data and applications as well as support for
local/remote collaboration and decision-making.
[0426] These established companies, have been, in general,
committed to legacy software, narrowly useful web-based technology,
or piecemeal component-based integration solutions, which have
depended and continue to depend on expensive mainframe, server
cluster, and hardware-enabled "parallel processing" computing to
provide their analytical product.
[0427] As of yet, no clear leader has emerged to meet the demand
for innovative software solutions within this rapidly expanding
field, and therefore there remains a need for a more innovative and
satisfactory solution.
[0428] Embodiments of the inventive system, method, and business
model will generate revenue from, for example, at least one or more
of the following areas; (1) the sale and licensing of its IT
Platform, (2) the sale of its various Drug Development, Genomics,
Cheminformatics and Proteomics modules, (3) the sale and licensing
of its data-pool assets, (4) royalties from strategic
collaborations, and (5) internal use of the IT Platform for
production of valued information such as for internal drug
discovery or monitoring of such as public health data. Other
revenue streams are also contemplated.
[0429] Several exemplary application areas are now described by way
of example. While the above referenced related patent applications
have described innovations in information technology, especially
for processing of high numbers of heterogeneous high-density data
in heterogeneous computing environments, and more particularly in
biotechnology, pharmaceutical, chemical, and life science
environments, the invention is not so limited. The systems,
methods, interfaces, engines, procedures, functions, algorithms,
and other aspects of the invention as described here and in the
related applications that are incorporated by reference may
advantageously be applied to and/or used in conjunction with
information systems generally, physics imaging and analysis,
intelligence integration and analysis, large scale manufacturing,
agriculture and agribusiness, geographic information systems (GIS),
the food industry, epidemiology, large scale forensics, economics
and financial systems, health and human services, medical systems,
as well as many other fields in which large amounts of data are
involved.
[0430] In the field of Information Systems, applications of the
inventive structure and method include but are not limited to
Information Technology (IT) Platform(s), B2A infrastructure,
database technology, and platform back-ends, among others. Some of
the value in this area includes but is not limited to Flexible,
Efficient, and Scalable Systems Integration; Data-enabling for Fast
and Secure Data Access and Management; and Scalable and Efficient
Applications Development Environment. Computer, network, and
information systems providers may benefit from aspects of the
invention.
[0431] In the field of Physics Imaging and Analysis, applications
of the inventive structure and method include but are not limited
to Groundwater, Oil, Mineral Exploration, Mining, Mapping, and
Real-time Analysis. Some of the value in this area includes but is
not limited to Added Efficiency and Functionality for Remote,
Magnetic and Sonic Imaging and Analysis, Reduced Exploration Costs,
and Increased Predictive Accuracy for Reduced Extraction Footprint.
Organizations such as NASA, the Department of Energy, an mineral
and resource exploration organizations may benefit from aspects of
the invention.
[0432] In the field of Large Scale Manufacturing, applications of
the inventive structure and method include but are not limited to
Just-in-time (JIT) Inventory Management, Process Management,
Robotics, and CAD/CAM. Some of the value in this area includes but
is not limited to Improved Market, Acquisition and Inventory based
on Global Data Access, Flexible and Scalable Process and
Infrastructure Management, Real-time, and Integrated Process
Optimization. Automobile manufacturers, chemical manufacturers,
semiconductor manufacturers, and other large scale material and
manufacturing organizations may benefit from the inventive
technology.
[0433] In the field of Agribusiness, applications of the inventive
structure and method include but are not limited to GMO's, Crop
Engineering, Seed Banks and Animal Breeding. Some of the value in
this area includes but is not limited to Enhanced Bioengineering
Applications, Automated QA/QC, Integrated GLP/GMP, Inventory and
Process Flow Automation, and Real-time Supply Chain Management.
Chemical, textile, and other food research and production
organizations may benefit from the inventive technology in this
area.
[0434] In the Food Industry, applications of the inventive
structure and method include but are not limited to Modified
Additives, Food Instantization, Food and Foodstuffs processing,
Manufacturing Process Design and Automation, and Inventory and
Product Distribution. Some of the value in this area includes but
is not limited to Enhanced Bioengineering Applications, Automated
QA/QC, Integrated GLP/GMP, Inventory and Process Flow Automation,
and Real-time Supply Chain Management, among others. Consumer food
producers, processors, and packagers will benefit from such
technology.
[0435] In the field of Epidemiology, applications of the inventive
structure and method include but are not limited to Disease
Studies, toxicology studies and analysis, and disease Outbreak
Prevention. Some of the value in this area includes but is not
limited to its Real-time capabilities, and its ability to provide
Predictive Modules for Multidimensional Disease Studies and
Diagnostics. For example, the Center for Disease control (CDC), the
Department of Health and Human Services (DHHS), and various
governmental and environmental laboratories may benefit from such
technology.
[0436] In the field of forensics, particularly Large Scale
Forensics, applications of the inventive structure and method
include but are not limited to Fingerprint, DNA, and Materials
Analysis, and Real-time Data Integration and Access. Some of the
value in this area includes but is not limited to Real-time Access
to Global Data Records, On-site Fingerprint, Photo Searching, and
DNA matching. Law enforcement agencies such as the FBI, Interpol,
and other investigative and law enforcement agencies will benefit
from the technology, and in addition such organizations such as
insurance companies and health maintenance organizations will
benefit.
[0437] Therefore it will be appreciated that the invention is not
limited to any particular field or application; rather, aspects of
the invention may be applied to information technology generally
where large amounts of heterogeneous data or information are
involved.
[0438] Although the foregoing invention has been described in some
detail by way of illustration and example for purposes of clarity
of understanding, it will be readily apparent to those of ordinary
skill in the art in light of the teachings of this invention that
certain changes and modifications may be made thereto without
departing from the spirit or scope of the appended claims.
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