PERCEPTUAL ASSOCIATIVE MEMORY FOR A NEURO-LINGUISTIC BEHAVIOR RECOGNITION SYSTEM

• US 20160170961A1
• Filed: 12/12/2014
• Published: 06/16/2016
• Est. Priority Date: 12/12/2014
• Status: Active Grant

• Current Assignee: Intellective Ai, Inc.
• Original Assignee: Behavioral Recognition Systems Incorporated
• Inventors: SEOW, Ming-Jung, XU, Gang, YANG, Tao, COBB, Wesley Kenneth
• Granted Patent: US 10,409,910 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 40/242 Dictionaries
• CPC Class Codes: G06F 40/284 Lexical analysis, e.g. tokenisation or collocates
• CPC Class Codes: G06F 40/40 Processing or translation of natural language natural language analysis G06F40/20; semantic analysis G06F40/30
• CPC Class Codes: G06N 3/0409 Adaptive resonance theory [ART] networks
• CPC Class Codes: G06N 3/042 Knowledge-based neural networks; Logical representations of neural networks
• CPC Class Codes: G06N 3/047 Probabilistic or stochastic networks
• CPC Class Codes: G06N 3/088 Non-supervised learning, e.g. competitive learning
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition

Abstract

Techniques are disclosed for generating a syntax for a neuro-linguistic model of input data obtained from one or more sources. A stream of words of a dictionary built from a sequence of symbols are received. The symbols are generated from an ordered stream of normalized vectors generated from input data. Statistics for combinations of words co-occurring in the stream are evaluated. The statistics includes a frequency upon which the combinations of words co-occur. A model of combinations of words based on the evaluated statistics is updated. The model identifies statistically relevant words. A connected graph is generated. Each node in the connected graph represents one of the words in the stream. Edges connecting the nodes represent a probabilistic relationship between words in the stream. Phrases are identified based on the connected graph.

Claims

1. A method for generating a syntax for a neuro-linguistic model of input data obtained from one or more sources, comprising:

receiving a stream of words of a dictionary built from a sequence of symbols, wherein the symbols are generated from an ordered stream of normalized vectors generated from input data;

evaluating statistics for combinations of words co-occurring in the stream, wherein the statistics includes a frequency upon which the combinations of words co-occur;

updating a model of combinations of words based on the evaluated statistics, wherein the model identifies statistically relevant words;

generating a connected graph, wherein each node in the connected graph represents one of the words in the stream, and wherein edges connecting the nodes represent a probabilistic relationship between words in the stream; and

identifying phrases based on the connected graph.

2. The method of claim 1, wherein the statistics include a statistical significance score indicating a frequency that two words co-occur with one another.

3. The method of claim 2, further comprising:

identifying one or more phrases including co-occurring words having a statistical significance score that exceeds a specified threshold; and

outputting the identified phrases.

4. The method of claim 1, wherein the edges are weighted based on a statistical significance score between each node.

5. The method of claim 1, further comprising:

receiving a second stream of words; and

updating the statistics of the co-occurring words based on the second stream of words.

6. The method of claim 5, further comprising, decreasing a statistical significance score of co-occurring words that are less frequently observed over time.

7. The method of claim 5, further comprising, increasing a statistical significance score of co-occurring words that are more frequently observed over time.

8. A computer-readable storage medium storing instructions, which, when executed on a processor, performs an operation for generating a syntax for a neuro-linguistic model of input data obtained from one or more sources, the operation comprising:

receiving a stream of words of a dictionary built from a sequence of symbols, wherein the symbols are generated from an ordered stream of normalized vectors generated from input data;

evaluating statistics for combinations of words co-occurring in the stream, wherein the statistics includes a frequency upon which the combinations of words co-occur;

updating a model of combinations of words based on the evaluated statistics, wherein the model identifies statistically relevant words;

generating a connected graph, wherein each node in the connected graph represents one of the words in the stream, and wherein edges connecting the nodes represent a probabilistic relationship between words in the stream; and

identifying phrases based on the connected graph.

9. The computer-readable storage medium of claim 8, wherein the statistics include a statistical significance score indicating a frequency that two words co-occur with one another.

10. The computer-readable storage medium of claim 9, wherein the operation further comprises:

identifying one or more phrases including co-occurring words having a statistical significance score that exceeds a specified threshold; and

outputting the identified phrases.

11. The computer-readable storage medium of claim 8, wherein the edges are weighted based on a statistical significance score between each node.

12. The computer-readable storage medium of claim 8, wherein the operation further comprises:

receiving a second stream of words; and

updating the statistics of the co-occurring words based on the second stream of words.

13. The computer-readable storage medium of claim 12, wherein the operation further comprises, decreasing a statistical significance score of co-occurring words that are less frequently observed over time.

14. The computer-readable storage medium of claim 12, wherein the operation further comprises, increasing a statistical significance score of co-occurring words that are more frequently observed over time.

15. A system, comprising:

a processor; and

a memory storing one or more application programs configured to perform an operation for generating a syntax for a neuro-linguistic model of input data obtained from one or more sources, the operation comprising;

receiving a stream of words of a dictionary built from a sequence of symbols, wherein the symbols are generated from an ordered stream of normalized vectors generated from input data;

evaluating statistics for combinations of words co-occurring in the stream, wherein the statistics includes a frequency upon which the combinations of words co-occur;

updating a model of combinations of words based on the evaluated statistics, wherein the model identifies statistically relevant words;

generating a connected graph, wherein each node in the connected graph represents one of the words in the stream, and wherein edges connecting the nodes represent a probabilistic relationship between words in the stream; and

identifying phrases based on the connected graph.

16. The system of claim 15, wherein the statistics include a statistical significance score indicating a frequency that two words co-occur with one another.

17. The system of claim 16, wherein the operation further comprises:

identifying one or more phrases including co-occurring words having a statistical significance score that exceeds a specified threshold; and

outputting the identified phrases.

18. The system of claim 15, wherein the operation further comprises:

receiving a second stream of words; and

updating the statistics of the co-occurring words based on the second stream of words.

19. The system of claim 18, wherein the operation further comprises, decreasing a statistical significance score of co-occurring words that are less frequently observed over time.

20. The system of claim 18, wherein the operation further comprises, increasing a statistical significance score of co-occurring words that are more frequently observed over time.