TRUSTED DECISION SUPPORT SYSTEM AND METHOD

• US 20140058990A1
• Filed: 08/19/2013
• Published: 02/27/2014
• Est. Priority Date: 05/03/2005
• Status: Abandoned Application

• Current Assignee: Gregory P. Benson
• Original Assignee: Palomar Technologies Incorporated
• Inventors: Benson, Greg, Fistonich, Matthew Anthony
• Application Number:
  US13/970,514
• Publication Number:
  US 20140058990A1
• Time in Patent Office: Days
• US Class Current: 706/14
• 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. 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. 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. (canceled)

2. A method comprising:

selecting, by an electronic device comprising at least one processor, a data set for an application area;

creating, by the electronic device comprising at least one processor, a weighted data set based on weighted scores assigned to data in the data set;

generating, by the electronic device comprising at least one processor, a correlation between the weighted data set and one or more previously correlated weighted data sets; and

determining, by the electronic device comprising at least one processor, a recommended action as a response to an event related to the data set, and outcome information for the recommended action, wherein said determining is based at least in part upon the correlation between the weighted data set and one or more previously correlated weighted data sets.

3. The method of claim 2, further comprising performing, by the electronic device comprising at least one processor, statistical analysis on the data set.

4. The method of claim 2 wherein the weighted data set is created according to weighting guidelines that evolve and develop over time.

5. The method of claim 2 further comprising performing statistical analysis on the data set, wherein statistical analysis performed on the data set is performed by a statistical analyses engine programmed with one or more models, wherein each model comprises mathematical instructions for processing the data set.

6. The method of claim 5, wherein the mathematical instructions comprise weights to be assigned to the data set according to at least one of a source from which the data set was received and an age of the data set.

7. The method of claim 5, 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, or residual analysis instructions for multiple regression.

8. The method of claim 2 wherein the data set for an application area comprises data from a sensor network.

9. The method of claim 8, further comprising performing, by the electronic device comprising at least one processor, statistical analysis by a statistical analyses engine on the data set.

10. The method of claim 8 further comprising performing statistical analysis on the data set, wherein the statistical analyses engine is configured to be programmed with one or more models, wherein each model comprises mathematical instructions for processing the data set.

11. The method of claim 10, wherein the mathematical instructions comprise weights to be assigned to the data set according to at least one of a source from which the data set was received or an age of the data set.

12. The method of claim 10, 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, or residual analysis instructions for multiple regression.

13. A method comprising:

selecting, by an electronic device comprising at least one processor, a data set for an application area;

analyzing, by the electronic device comprising at least one processor, the data set based on fuzzy logic instructions;

generating, by the electronic device comprising at least one processor, a recommended action and outcome information for the recommended action; and

performing, by the electronic device comprising at least one processor, statistical analysis by a statistical analysis engine configured to be programmed with one or more models, wherein each model comprises mathematical instructions for processing the data set, wherein the mathematical instructions comprise weights to be assigned to the data set according to at least one of a source from which the data set was received or an age of the data set.

14. The method of claim 13, 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, or residual analysis instructions for multiple regression.

15. The method of claim 13, further comprising:

receiving, by the electronic device comprising at least one processor, current data from at least one of a plurality of sources;

comparing, by the electronic device comprising at least one processor, the current data and the previously received data; and

providing, by the electronic device comprising at least one processor, a recommended action and outcome information for the recommended action based at least in part on the comparison.

16. The method of claim 15, wherein the plurality of sources includes disparate sources.

17. The method of claim 15, further comprising a database of previously recommended actions associated with the previously received data, wherein the electronic device is further configured to provide a recommended action and outcome information based at least in part on the current data, the previously received data, or the associated previously recommended actions.

18. A method for performing an application specific decision support model comprising:

identifying, by an electronic device comprising at least one processor, data sources for an application area;

selecting, by the electronic device comprising at least one processor, variables to be searched for in each identified data source;

assigning, by the electronic device comprising at least one processor, weights to each variable searched, wherein weights correspond to relevance of information in each data source;

identifying, by the electronic device comprising at least one processor, instructions to apply to a search of a selected variable in an identified data source;

conducting, by the electronic device comprising at least one processor, a correlation process, wherein a current scenario is correlated with previous scenarios;

conducting, by the electronic device comprising at least one processor, multiple analysis on the current scenario;

determining, by the electronic device comprising at least one processor, at least one next likely outcome or event for at least one time point;

identifying, by the electronic device comprising at least one processor, at least one recommendation for each time point, wherein the at least one recommendation is based on the at least one next likely outcome or event;

determining, by the electronic device comprising at least one processor, at least one potential consequence for each recommendation;

performing, by the electronic device comprising at least one processor, an action based upon the at least one recommendation and at least one potential consequence; and

storing, by the electronic device comprising at least one processor, results of the action.

19. The method of claim 18 wherein the making of at least one prediction, the identifying of at least one recommendation, or the determining of at least one potential consequence are executed simultaneously.

20. The method of claim 18 wherein the 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, or residual analysis instructions for multiple regression.

21. The method of claim 18 wherein the action performed comprises presenting the at least one recommendation and at least one consequence to a user for a user input.