Trusted decision support system and method

• US 20070022057A1
• Filed: 05/03/2006
• Published: 01/25/2007
• Est. Priority Date: 05/03/2005
• Status: Active Grant

• Current Assignee: Gregory P. Benson
• Original Assignee: Palomar Technologies Incorporated
• Inventors: Benson, Greg, Fistonich, Matthew
• Granted Patent: US 7,526,455 B2
• Time in Patent Office: Days
• US Class Current: 705/59
• CPC Class Codes: G05B 13/0275 using fuzzy logic only
• CPC Class Codes: G06F 11/202 where processing functionality is redundant redundant communication control functionality G06F11/2005, redundant storage control functionality G06F11/2089
• CPC Class Codes: G06F 21/52 during program execution, e.g. stack integrity ; Preventing unwanted data erasure; Buffer overflow
• CPC Class Codes: G06F 2221/034 Test or assess a computer or a system
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/048 Fuzzy inferencing
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 10/08 Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
• CPC Class Codes: G06Q 10/0833 Tracking
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 50/26 Government or public services business processes related to the transportation industry G06Q50/40
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083
• CPC Class Codes: G07C 2009/0092 for cargo, freight or shipping containers and applications therefore in general
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/085 using electronic data carriers
• CPC Class Codes: G07C 5/0891 Video recorder in combination with video camera
• CPC Class Codes: G07C 9/257 electronically G07C9/26 takes precedence
• CPC Class Codes: G07C 9/37 using biometric data, e.g. fingerprints, iris scans or voice recognition
• CPC Class Codes: G07F 7/0636 in which the trolleys or carts are kept in a restricted zone such as a coral-like enclosure, or are passing a gate before use is possible
• CPC Class Codes: G07G 1/0036 Checkout procedures
• CPC Class Codes: G07G 3/00 : Alarm indicators, e.g. bells
• CPC Class Codes: G08B 13/196 : using television cameras
• CPC Class Codes: G08B 13/22 : Electrical actuation
• CPC Class Codes: G08B 13/2454 : Checking of authorisation of a person accessing tagged items in an EAS system
• CPC Class Codes: G08B 21/02 : Alarms for ensuring the safety of persons
• CPC Class Codes: G08B 21/12 : responsive to undesired emission of substances, e.g. pollution alarms
• CPC Class Codes: G08B 25/14 : Central alarm receiver or annunciator arrangements
• CPC Class Codes: G08B 29/04 : Monitoring of the detection circuits
• CPC Class Codes: G08B 29/16 : Security signalling or alarm systems, e.g. redundant systems
• CPC Class Codes: H04K 3/22 : including jamming detection and monitoring
• CPC Class Codes: H04L 2209/805 : Lightweight hardware, e.g. radio-frequency identification [RFID] or sensor
• CPC Class Codes: H04L 63/0428 : wherein the data content is protected, e.g. by encrypting or encapsulating the payload
• CPC Class Codes: H04L 63/10 : for controlling access to devices or network resources
• CPC Class Codes: H04L 63/101 : Access control lists [ACL]
• CPC Class Codes: H04L 67/025 : for remote control or remote monitoring of applications
• CPC Class Codes: H04L 67/12 : specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04L 67/52 : specially adapted for the location of the user terminal
• CPC Class Codes: H04L 67/535 : Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04L 9/3236 : using cryptographic hash functions
• CPC Class Codes: H04L 9/3247 : involving digital signatures
• CPC Class Codes: H04N 7/181 : for receiving images from a plurality of remote sources

Abstract

Methods and apparatus for providing a comprehensive decision support system to include predictions, recommendations with consequences and optimal follow-up actions in specific situations are described. Data is obtained from multiple disparate data sources, depending on the information deemed necessary for the situation being modeled. Some embodiments perform complex systems modeling including performing massive correlative analyses of the data obtained from the multiple disparate data sources with current situational data obtained regarding the situation for which the decision support process is being utilized. The decision support system provides a prediction or predictions and a recommendation or a choice of recommendations based on the correlative analysis and/or other analyses. In some embodiments the decision support system provides possible consequences that could result from a recommendation. In other embodiments the decision support system provides a list of tasks for acting upon a recommendation. Also described are methods and apparatus for developing application specific decision support models. The decision support model development process may include identifying multiple disparate data sources for retrieval of related information, selection of classification variables to be retrieved from the data sources, assignment of weights to each classification variable, selecting and/or defining rules, and selecting and/or defining analysis functions.

Claims

1. A decision support system, comprising:

a database containing data from a plurality of disparate data sources; and

an electronic device configured to classify the database data and assign weighted values to the data based at least in part on the classification, the electronic device further configured to analyze the database data so as to provide at least one recommended action and outcome information for the at least one recommended action.

2. The system of claim 1, wherein the database data comprises at least one of location data, real-time sensor data, legacy computer data, relational database records, flat-file database records, non alpha-numeric data, and unstructured data.

3. The system of claim 1, wherein the disparate data sources comprise at least one of an internet source, a real-time sensor, a computer database, a relational database, and a flat-file database.

4. The system of claim 1, wherein the system is further configured to store actual outcomes of previously stored and performed recommended actions, and the at least one recommended action is based at least in part on the stored actual outcomes.

5. The system of claim 1, wherein the system is configured to initiate the at least one recommended action according to at least one of an input from a user and preprogrammed instructions without user input at the time of initiating the at least one recommended action.

6. The system of claim 1, further comprising a statistical analyses engine configured to be programmed with one or more models, wherein each model comprises mathematical instructions for processing the database data.

7. The system of claim 6, wherein the mathematical instructions comprise weights assigned to database data according to at least one of the data source from which the database data was received and the age of the database data.

8. The system of claim 6, wherein the mathematical instructions comprise at least one of fuzzy logic instructions, Bayesian analyses instructions, neural network analyses instructions, probability calculation instructions, mean calculation instructions, confidence interval calculation instructions, Z-test instructions, T-test instructions, autoregressive modeling instructions, and residual analysis instructions for multiple regression.

9. A decision support system, comprising:

a sensor network;

a database configured to store data from the sensor network; and

an electronic device configured to classify the database data and assign weighted values to the data based at least in part on the classification, the electronic device further configured to analyze the database data so as to provide at least one recommended action and outcome information for the at least one recommended action.

10. The system of claim 9, wherein the sensors are configured to sense disparate inputs.

11. The system of claim 9, wherein the database data comprises at least one of location data, real-time sensor data, legacy computer data, relational database records, flat-file database records, non alpha-numeric data, and unstructured data.

12. The system of claim 9, further comprising an actual outcome database, wherein the system is configured to store actual outcomes of performed previously recommended actions, and the recommended action is based at least in part on the stored actual outcomes.

13. The system of claim 9, wherein the system is configured to initiate the recommended action according to at least one of an input from a user and preprogrammed instructions without user input at the time of initiating the recommended action.

14. The system of claim 9, further comprising a statistical analyses engine configured to be programmed with one or more models, wherein each model comprises mathematical instructions for processing the database data.

15. The system of claim 14, wherein the mathematical instructions comprise weights assigned to database data according to at least one of the data source from which the database data was received and the age of the database data.

16. The system of claim 14, wherein the mathematical instructions comprise at least one of fuzzy logic instructions, Bayesian analyses instructions, neural network analyses instructions, probability calculation instructions, mean calculation instructions, confidence interval calculation instructions, Z-test instructions, T-test instructions, autoregressive modeling instructions, and residual analysis instructions for multiple regression.

17. A goal seeking system comprising:

an action database comprising database actions;

an outcome database comprising outcomes associated with the actions; and

an electronic device configured to receive target goal information, the electronic device configured to provide at least one recommended action to achieve the target goal.

18. The system of claim 17, further comprising a statistical analyses engine configured to be programmed with one or more models, wherein each model comprises mathematical instructions for processing the target goal information, the database actions, and the outcomes.

19. The system of claim 18, wherein the mathematical instructions comprise weights assigned to database actions according to the age of the database actions.

20. The system of claim 18, wherein the mathematical instructions comprise at least one of fuzzy logic instructions, Bayesian analyses instructions, neural network analyses instructions, probability calculation instructions, mean calculation instructions, confidence interval calculation instructions, Z-test instructions, T-test instructions, autoregressive modeling instructions, and residual analysis instructions for multiple regression.

21. A decision support system, comprising:

a database containing data from a plurality of disparate data sources; and

an electronic device configured to analyze the database data so as to provide at least one recommended action and a plurality of recommended tasks to perform the recommended action.

22. The system of claim 21, wherein the electronic device is further configured to correlate related database data and to predict future outcomes based on the correlation.

23. The system of claim 22, further configured to perform at least one of the recommended tasks.

24. The system of claim 21, wherein the disparate data sources comprise at least one of an internet source, a real-time sensor, a computer database, a relational database, and a flat-file database.

25. The system of claim 21, wherein the system is configured to initiate the at least one recommended action according to at least one of an input from a user and preprogrammed instructions without user input at the time of initiating the at least one recommended action.

26. The system of claim 21, further comprising a statistical analyses engine configured to be programmed with one or more models, wherein each model comprises mathematical instructions for processing the database data.

27. The system of claim 26, wherein the mathematical instructions comprise weights assigned to database data according to at least one of the data source from which the database data was received and the age of the database data.

28. The system of claim 26, wherein the mathematical instructions comprise at least one of fuzzy logic instructions, Bayesian analyses instructions, neural network analyses instructions, probability calculation instructions, mean calculation instructions, confidence interval calculation instructions, Z-test instructions, T-test instructions, autoregressive modeling instructions, and residual analysis instructions for multiple regression.