Trusted decision support system and method

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.

1 . A system comprising:
 an electronic device configured to select a data set for an application area, to assign weighted scores to the data in the data set, to correlate the weighted data set with one or more previously correlated weighted data sets, and to determine, based upon the correlation, a recommended action as a response to an event related to the data set.   
   
   
       2 . The system of  claim 1 , further comprising a statistical analysis engine configured to perform statistical analysis on the data set. 
   
   
       3 . 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 data set. 
   
   
       4 . The system of  claim 3 , 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. 
   
   
       5 . The system of  claim 3 , 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. 
   
   
       6 . A system comprising:
 a sensor network;   a database comprising data from the sensor network; and   a electronic device configured to select a data set from the database for an application area, to assign weighted scores to the data in the data set, to correlate the weighted data set with one or more previously correlated weighted data sets, and to determine, based upon the correlation, a recommended action as a response to an event related to the data set.   
   
   
       7 . The system of  claim 6 , further comprising a statistical analysis engine configured to perform statistical analysis on the data set. 
   
   
       8 . The system of  claim 6 , further comprising a statistical analyses engine configured to be programmed with one or more models, wherein each model comprises mathematical instructions for processing the data set. 
   
   
       9 . The system of  claim 8 , 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. 
   
   
       10 . The system of  claim 8 , 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. 
   
   
       11 . A system comprising:
 an electronic device configured to select a data set for an application area, the electronic device further configured to analyze the data set according to fuzzy logic instructions so as to generate a recommended action and outcome information for the recommended action.   
   
   
       12 . The system of  claim 11 , further comprising a statistical analysis engine configured to perform statistical analysis on the data set. 
   
   
       13 . The system of  claim 11 , further comprising a statistical analyses engine configured to be programmed with one or more models, wherein each model comprises mathematical instructions for processing the data set. 
   
   
       14 . The system of  claim 13 , 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. 
   
   
       15 . The system 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, and residual analysis instructions for multiple regression. 
   
   
       16 . A system comprising:
 an electronic device configured to select a data set for an application area, the electronic device further configured to analyze the data set according to statistical analysis instructions so as to generate a recommended action and outcome information for the recommended action.   
   
   
       17 . The system of  claim 16 , further comprising a statistical analyses engine configured to be programmed with one or more models, wherein each model comprises mathematical instructions for processing the data set. 
   
   
       18 . The system of  claim 17 , 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. 
   
   
       19 . The system of  claim 17 , 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. 
   
   
       20 . A data analysis system, the system comprising:
 a database comprising previously received data from a plurality of sources; and   an electronic device configured to receive current data from at least one of the plurality of sources, to compare the current data and the previously received data, and to provide a recommended action and outcome information for the recommended action based at least in part on the comparison.   
   
   
       21 . The system of  claim 20 , further comprising a statistical analysis engine configured to perform statistical analysis on the received current data. 
   
   
       22 . The system of  claim 20 , wherein the system is configured to store the recommended action in a library of recommended actions, and to store the received current data, wherein the stored recommended action and the stored received data are associated, and wherein the recommended action is based at least in part on one or more previously stored recommended actions. 
   
   
       23 . The system of  claim 20 , wherein the system is further 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. 
   
   
       24 . The system of  claim 20 , further comprising a statistical analyses engine configured to be programmed with one or more models, wherein each model comprises mathematical instructions for processing the received current data. 
   
   
       25 . The system of  claim 24 , wherein the mathematical instructions comprise weights assigned to received current data according to at least one of the data source from which the received current data was received and the age of the received current data. 
   
   
       26 . The system of  claim 24 , 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. 
   
   
       27 . A data analysis system, the system comprising:
 a database comprising previously received data from a plurality of sources; and   an electronic device configured to receive current data from at least one of the plurality of sources, to analyze the current data, and to predict an outcome based at least in part on the previous data and the current data.   
   
   
       28 . The system of  claim 27 , further comprising a statistical analysis engine configured to perform statistical analysis on the received current data. 
   
   
       29 . The system of  claim 27 , wherein the system is configured to store the predicted outcome in a library of predicted outcomes, and to store the received current data, wherein the stored predicted outcome and the stored received data are associated, and wherein the predicted outcome is based at least in part on one or more previously stored predicted outcomes. 
   
   
       30 . The system of  claim 27 , wherein the system is further configured to store actual outcomes associated with performed previously received data, and the predicted outcome is based at least in part on the stored actual outcomes. 
   
   
       31 . The system of  claim 27 , further comprising a statistical analyses engine configured to be programmed with one or more models, wherein each model comprises mathematical instructions for processing the received current data. 
   
   
       32 . The system of  claim 31 , wherein the mathematical instructions comprise weights to be assigned to received current data according to at least one of the data source from which the received current data was received and the age of the received current data. 
   
   
       33 . The system of  claim 31 , 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. 
   
   
       34 . A data analysis system, the system comprising:
 a database comprising previously received data from a plurality of sources; and   an electronic device configured to receive current data from at least one of the plurality of sources, to compare the current data and the previously received data, and to predict a future outcome based at least in part on the determination.   
   
   
       35 . The system of  claim 34 , further comprising a statistical analysis engine configured to perform statistical analysis on the received current data. 
   
   
       36 . The system of  claim 34 , wherein the system is configured to store the predicted outcome in a library of predicted outcomes, and to store the received current data, wherein the stored predicted outcome and the stored received data are associated, and wherein the predicted outcome is based at least in part on one or more previously stored predicted outcomes. 
   
   
       37 . The system of  claim 34 , wherein the system is further configured to store actual outcomes associated with previously received data, and the predicted outcome is based at least in part on the stored actual outcomes. 
   
   
       38 . The system of  claim 34 , further comprising a statistical analyses engine configured to be programmed with one or more models, wherein each model comprises mathematical instructions for processing the received current data. 
   
   
       39 . The system of  claim 38 , wherein the mathematical instructions comprise weights to be assigned to received current data according to at least one of the data source from which the received current data was received and the age of the received current data. 
   
   
       40 . The system of  claim 38 , 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. 
   
   
       41 . A recommended action system comprising:
 a database of previously received data;   a database of previously recommended actions associated with the previously received data; and   an electronic device configured to receive current data, correlate the current data with previously received data, and to generate one or more recommended actions based at least in part on the received data, the correlation, and the previously recommended actions.   
   
   
       42 . The system of  claim 41 , wherein the received current data is from disparate sources. 
   
   
       43 . The system of  claim 41 , further comprising a statistical analysis engine configured to perform statistical analysis on the received current data. 
   
   
       44 . The system of  claim 43 , wherein the statistical analyses engine is configured to be programmed with one or more models, wherein each model comprises mathematical instructions for processing the received data. 
   
   
       45 . The system of  claim 44 , wherein the mathematical instructions comprise weights assigned to the received current data according to at least one of the data source from which the received current data was received and the age of the received current data. 
   
   
       46 . The system of  claim 44 , 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. 
   
   
       47 . The system of  claim 41 , wherein the electronic device is further configured to predict one or more outcomes based at least in part on the received data, the correlation, and the previously recommended actions. 
   
   
       48 . A prediction system comprising:
 a database of previously received data;   a database of previously predicted outcomes associated with the previously received data; and   an electronic device configured to receive current data, to correlate the current data with previously received data, and to predict one or more outcomes based at least in part on the received data, the correlation, and the previously predicted outcomes.   
   
   
       49 . The system of  claim 48 , wherein the received current data is from disparate sources. 
   
   
       50 . The system of  claim 48 , further comprising a statistical analysis engine configured to perform statistical analysis on the received current data. 
   
   
       51 . The system of  claim 50 , wherein the statistical analyses engine is configured to be programmed with one or more models, wherein each model comprises mathematical instructions for processing the received data. 
   
   
       52 . The system of  claim 51 , wherein the mathematical instructions comprise weights assigned to the received current data according to at least one of the data source from which the received current data was received and the age of the received current data. 
   
   
       53 . The system of  claim 51 , 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. 
   
   
       54 . The system of  claim 48 , wherein the electronic device is further configured to generate one or more recommended actions based at least in part on the received data, the correlation, and the previously predicted outcomes. 
   
   
       55 . A recommended action system comprising:
 a database of previously received data;   a database of previous actual outcomes associated with the previously received data; and   an electronic device configured to receive current data, correlate the current data with previously received data, and to generate one or more recommended actions based at least in part on the received data, the correlation, and the previous actual outcomes.   
   
   
       56 . The system of  claim 55 , wherein the received current data is from disparate sources. 
   
   
       57 . The system of  claim 55 , further comprising a statistical analysis engine configured to perform statistical analysis on the received current data. 
   
   
       58 . The system of  claim 57 , wherein the statistical analyses engine is configured to be programmed with one or more models, wherein each model comprises mathematical instructions for processing the received current data. 
   
   
       59 . The system of  claim 58 , wherein the mathematical instructions comprise weights assigned to the received current data according to at least one of the data source from which the received current data was received and the age of the received current data. 
   
   
       60 . The system of  claim 58 , 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. 
   
   
       61 . The system of  claim 55 , wherein the electronic device is further configured to predict one or more outcomes based at least in part on the received data, the correlation, and the previous actual outcomes. 
   
   
       62 . An electronic warrant or subpoena system, comprising:
 a database of sensitive data comprising at least one of a plurality of private records and a plurality of security records; and   an electronic device configured to provide information from the sensitive data based at least in part on a received electronic warrant or electronic subpoena.   
   
   
       63 . The system of  claim 62 , further comprising a statistical analysis engine configured to perform statistical analysis on the information. 
   
   
       64 . The system of  claim 63 , wherein the statistical analyses engine is configured to be programmed with one or more models, wherein each model comprises mathematical instructions for processing the information. 
   
   
       65 . The system of  claim 64 , wherein the mathematical instructions comprise weights to be assigned to the information according to the data source from which the information was received. 
   
   
       66 . The system of  claim 64 , wherein the mathematical instructions comprise weights to be assigned to the information according to the age of the information. 
   
   
       67 . The system of  claim 64 , 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. 
   
   
       68 . The system of  claim 62 , wherein the information 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. 
   
   
       69 . The system of  claim 62 , wherein sources of the information comprise at least one of an internet source, a real-time sensor, a computer database, a relational database, and a flat-file database. 
   
   
       70 . The system of  claim 62 , wherein sources of the information are disparate. 
   
   
       71 . The system of  claim 62 , further comprising an integrity services module configured to authenticate the information. 
   
   
       72 . An electronic warrant or subpoena system, comprising:
 a plurality of disparate data sources comprising a plurality of private data; and   an electronic device configured to provide information from the data sources based at least in part on a received electronic warrant or electronic subpoena.   
   
   
       73 . The system of  claim 72 , further comprising a statistical analysis engine configured to perform statistical analysis on the information. 
   
   
       74 . The system of  claim 73 , wherein the statistical analyses engine is configured to be programmed with one or more models, wherein each model comprises mathematical instructions for processing the information. 
   
   
       75 . The system of  claim 74 , wherein the mathematical instructions comprise weights to be assigned to the information according to the data source from which the information was received. 
   
   
       76 . The system of  claim 74 , wherein the mathematical instructions comprise weights to be assigned to the information according to the age of the information. 
   
   
       77 . The system of  claim 74 , 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. 
   
   
       78 . The system of  claim 72 , wherein the information 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. 
   
   
       79 . The system of  claim 72 , wherein sources of the information comprise at least one of an internet source, a real-time sensor, a computer database, a relational database, and a flat-file database. 
   
   
       80 . The system of  claim 72 , wherein sources of the information comprise sensors. 
   
   
       81 . The system of  claim 72 , further comprising an integrity services module configured to authenticate the information.