Method and apparatus for non-disruptive embedding of specialized elements

• US 20090288064A1
• Filed: 07/07/2009
• Published: 11/19/2009
• Est. Priority Date: 08/03/2007
• Status: Active Grant

• Current Assignee: Wei Yen
• Original Assignee: AiLive, Inc.
• Inventors: Reynolds, Stuart, Tu, Xiaoyuan, Funge, John, Wright, Ian, Yen, Wei, Dobson, Daniel, Wilkinson, Dana, Musick, JR., Charles, Powers, III, William Robert
• Granted Patent: US 7,937,243 B2
• Time in Patent Office: Days
• US Class Current: 717/106
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: Y10S 706/906 Process plant
• CPC Class Codes: Y10S 707/99943 Generating database or data structure, e.g. via user interface

Abstract

Techniques for non-disruptive embedding of specialized elements are disclosed. In one aspect of the techniques, ontology is defined to specify an application domain. A program interface (API) is also provided for creating raw features by a developer. Thus a module is provided for at least one form of statistical analysis within the ontology. The module is configured automatically in a computing device with the API in response to a system consistent with the ontology, wherein the system has no substantial requirement for specialized knowledge of that form of statistical analysis, and the module has no substantial requirement for specialized knowledge of particular functions provided by the system.

Claims

1. A method for non-disruptive embedding specialized elements, the method comprising:

defining an ontology specifying an application domain;

providing a program interface (API) for creating raw features by a developer;

providing a module for at least one form of statistical analysis within the ontology;

configuring automatically the module in a computing device with the API in response to a system consistent with that ontology, wherein the system has no substantial requirement for specialized knowledge of that form of statistical analysis, and the module has no substantial requirement for specialized knowledge of particular functions provided by the system.

2. The method as in recited claim 1, wherein the raw features are created by the developer using the API and types being declared, the raw features provide a convenient way to input information into the specialized elements at run-time.

3. The method as in recited claim 2, wherein values to the raw features are set at run-time.

4. The method as in recited claim 2, wherein the ontology includes a model of a game world.

5. The method as in recited claim 2, wherein the form of statistical analysis includes a form of machine learning.

7. The method as in recited claim 1, wherein the API is supplied in a commonly used computer language so that the developer defines the raw features that are constructed using the API.

6. The method as in recited claim 6, wherein that form of machine learning includes a portion capable of generating a behavior defined within the ontology.

8. An apparatus including a computer readable medium for storing program instructions, the instructions associated with

defining an ontology specifying an application domain;

providing a program interface (API) for creating raw features by a developer;

providing a module for at least one form of statistical analysis within the ontology; and

configuring automatically the module in a computing device with the API in response to a system consistent with that ontology, wherein the system has no substantial requirement for specialized knowledge of that form of statistical analysis, and the module has no substantial requirement for specialized knowledge of particular functions provided by the system.

9. The apparatus as recited in claim 8, wherein the raw features are created by the developer using the API and types being declared, the raw features provide a convenient way to input information into the specialized elements at run-time.

10. The apparatus as recited in claim 9, wherein values to the raw features are set at run-time.

11. The apparatus as recited in claim 9, wherein the ontology includes a model of a game world.

12. The apparatus as recited in claim 9, wherein the form of statistical analysis includes a form of machine learning.

13. The apparatus as recited in claim 12, wherein that form of machine learning includes a portion capable of generating a behavior defined within the ontology.

14. The apparatus as recited in claim 8, wherein the API is supplied in a commonly used computer language so that the developer defines the raw features that are constructed using the API.

15. A system for non-disruptive embedding specialized elements, the system comprising including:

specialized elements that are restricted to a particular domain of data analysis or processing, wherein the particular domain is defined by an ontology;

a set of program interfaces for creating raw features for a particular application;

configurable data, provided by a module for at least one form of statistical analysis within the ontology that permits the specialized elements to be tailored to the particular application, thereby expanding applicability of the specialized elements to a plurality of applications within the particular domain.

16. The system as recited in claim 15, wherein

the specialized elements are provided by a supplier to a developer without the supplier having detailed knowledge of structures and internal operations used by the particular application;

and

the particular application is generated by the developer without the developer having detailed knowledge of internal operations used by the specialized elements.

17. The system as recited in claim 16, wherein the particular domain is a particular form of game, simulation, or analysis program.

18. The system as recited in claim 16, wherein the specialized elements enable statistical analysis, data mining, machine learning, online learning, or online analytical processing in the particular domain.

19. The system as recited in claim 16, wherein the configurable data includes templates of configurable data definitions that, once configured, provide a link between the particular application and the specialized elements.

20. The system as recited in claim 19, wherein the configurable data includes one or more parameters that specify predicates and targets for the specialized elements, the specialized elements respond to queries from the particular application.

21. The system as recited in claim 20, wherein the configurable data includes:

one or more header files that define structures for features used by the application; and

one or more parameters that specify predicates and targets for the specialized elements.