Adaptive dynamic personal modeling system and method

• US 20040015906A1
• Filed: 04/30/2002
• Published: 01/22/2004
• Est. Priority Date: 04/30/2001
• Status: Abandoned Application

• Current Assignee: Tanvir Y. Goraya
• Original Assignee: Tanvir Y. Goraya
• Inventors: Goraya, Tanvir Y.
• Application Number:
  US10/135,159
• Publication Number:
  US 20040015906A1
• Time in Patent Office: Days
• US Class Current: 717/141
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition

Abstract

The present invention provides a system and method for building and analyzing a quantitative model from qualitative tacit knowledge. In one aspect of the present invention, there is provided a system for creating and analyzing a model of a user'"'"'s semantic knowledge. The semantic knowledge model is based on cause and effect relationships as defined by the user. In another aspect of the present invention, there is provided a system for creating and analyzing a model of a user'"'"'s episodic knowledge. The episodic knowledge model is based on the user'"'"'s past experiences, including recalled stimuli and responses. The semantic and episodic models are used to describe the users internal mental model.

Claims

1. A method for building and analyzing a quantitative model from qualitative tacit knowledge comprising the steps of:

receiving a plurality of cause factors and a plurality of effect factors;

receiving a plurality of defined cause and effect relationships, each defined cause and effect relationship comprising a relationship value corresponding to a degree of influence that a selected cause factor has on a selected effect factor; and

providing a means for holistically analyzing a plurality of cause and effect relationships.

2. The method of claim 1, further comprising the step of applying a linear function to the relationship value.

3. The method of claim 2, wherein the linear function is applied based upon a specified relative importance.

4. A method for building and analyzing a quantitative model from qualitative tacit knowledge comprising the steps of:

receiving a plurality of episodes, each episode comprising a plurality of cause factors and a plurality of effect factors;

receiving a plurality of defined cause and effect relationships, each defined cause and effect relationship comprising a value corresponding to a degree of influence that a selected cause factor in an episode has on a selected effect factor within the same episode; and

matching defined relationships in a first episode with defined relationships in a second episode such that the matched relationships have the same cause factor and the same effect factor;

deriving a function from the matched defined relationships such that the derived function describes a relationship between causes and effects across episodes; and

providing a means for holistically analyzing a plurality of cause and effect relationships.

5. The method of claim 4 wherein the derived function is a nonlinear function.

6. A system for building and analyzing a quantitative model from qualitative tacit knowledge comprising:

computer readable code on a computer readable medium for receiving a plurality of cause factors and a plurality of effect factors;

computer readable code on a computer readable medium for receiving a plurality of defined cause and effect relationships, each defined cause and effect relationship comprising a relationship value corresponding to a degree of influence that a selected cause factor has on a selected effect factor; and

computer readable code on a computer readable medium for providing a means for holistically analyzing a plurality of cause and effect relationships.