Method and apparatus for creating a predictive model

1. A method for creating a predictive model, the method comprising the steps of:

determining trends and patterns in electronic data, using at least a first machine language algorithm;

refining the determination of the algorithm;

searching for social models that describe the identified trends and patterns using at least a second machine language algorithm;

verifying causal links;

constructing at least one model about human node behavior and interactions;

utilizing the social models to do at least one of the following: validate hypotheses, predict future behavior, and examine hypothetical scenarios;

automatically updating predictions when new data is introduced;

using probabilistic techniques to learn hierarchical structure in unstructured text;

continuously updating a set of themes;

examining grammatical rules of each component of text;

matching grammatical constituents to semantic roles; and,

reorganizing data into clusters of entities with common attributes.

2. The method of claim 1 , wherein the step of determining trends and patterns in electronic data, using at least a first machine language algorithm comprises the step of:

using categorization and relational modeling algorithms to identify trends and patterns.

3. The method of claim 2 , wherein the method further comprises the steps of:

converting structured and unstructured textual and numerical data into behavioral predictive models; and,

utilizing a probabilistic modeling algorithm to generate entity-level probability models.

4. The method of claim 3 , wherein the method further comprises the steps of:

converting extracted entities and attributes into sets of entity-attribute-value and entity-entity-relationship;

automatically determining the number of kinds of objects contained in the entity-attribute-value set;

assigning attributes to the kind of object described; and,

automatically determining rules which govern entities in the entity-entity-relationship set.

5. The method of claim 4 , wherein the method further comprises the step of:

converting segments into object-oriented Bayesian Networks.

6. The method of claim 5 , wherein the method further comprises the steps of:

linking algorithms with a user interface; and,

parsing each sentence based on parts of speech and relative positions, using a semantic role labeling algorithm, to extract entities and discover relationships between entities.

7. The method of claim 6 , wherein the method further comprises the steps of:

utilizing a Bayesian network learning algorithm to analyze causes and effects of observed evidence using Bayesian Networks; and,

creating real-time mathematical models to predict actions.

8. The method of claim 7 , wherein the method further comprises the steps of:

creating Bayesian Network frames;

combining frames to explain relationships between entities and events;

determining, using an MCMCDA algorithm, likely social network structures;

approximating joint probabilistic data associations; and,

providing a fully polynomial randomized approximation scheme.

9. A non-transitory computer readable medium containing instructions for a method for creating a predictive model, the computer readable medium comprising the steps of:

determining trends and patterns in electronic data, using at least a first machine language algorithm;

refining the determination of the algorithm;

searching for social models that describe the identified trends and patterns using at least a second machine language algorithm;

verifying causal links;

constructing at least one model about human node behavior and interactions;

utilizing the social models to do at least one of the following: validate hypotheses, predict future behavior, and examine hypothetical scenarios;

automatically updating predictions when new data is introduced;

using probabilistic techniques to learn hierarchical structure in unstructured text;

continuously updating a set of themes;

examining grammatical rules of each component of text;

matching grammatical constituents to semantic roles; and,

reorganizing data into clusters of entities with common attributes.

10. The computer readable medium of claim 9 , wherein the step of determining trends and patterns in electronic data, using at least a first machine language algorithm comprises the step of:

using categorization and relational modeling algorithms to identify trends and patterns.

11. The computer readable medium of claim 10 , wherein the computer readable medium further comprises the steps of:

converting structured and unstructured text into behavioral predictive models; and,

utilizing a probabilistic modeling algorithm to generate entity-level probability models.

12. The computer readable medium of claim 11 , wherein the computer readable medium further comprises the steps of:

converting extracted entities and attributes into sets of entity-attribute-value and entity-entity-relationship;

automatically determining the number of kinds of objects contained in the entity-attribute-value set;

assigning attributes to the kind of object described; and,

automatically determining rules which govern entities in the entity-entity-relationship set.

13. The computer readable medium of claim 12 , wherein the computer readable medium further comprises the step of:

converting segments into object-oriented Bayesian Networks.

14. The computer readable medium of claim 13 , wherein the computer readable medium further comprises the steps of:

linking algorithms with a user interface; and,

parsing each sentence based on parts of speech and relative positions, using a semantic role labeling algorithm, to extract entities and discover relationships between entities.

15. The computer readable medium of claim 14 , wherein the computer readable medium further comprises the steps of:

utilizing a Bayesian network learning algorithm to analyze causes and effects of observed evidence using Bayesian Networks; and,

creating real-time mathematical models to predict actions.

16. The computer readable medium of claim 15 , wherein the computer readable medium further comprises the steps of:

creating Bayesian Network frames;

combining frames to explain relationships between entities and events;

determining, using an MCMCDA algorithm, likely social network structures;

approximating joint probabilistic data associations; and,

providing a fully polynomial randomized approximation scheme.