LEXICAL ANALYZER FOR A NEURO-LINGUISTIC BEHAVIOR RECOGNITION SYSTEM

• US 20160170964A1
• 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: XU, Gang, SEOW, Ming-Jung, YANG, Tao, COBB, Wesley Kenneth
• Granted Patent: US 10,409,909 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 40/242 Dictionaries
• CPC Class Codes: G06F 40/289 Phrasal analysis, e.g. finite state techniques or chunking
• CPC Class Codes: G06N 20/00 Machine learning
• 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 3/045 Combinations of 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
• CPC Class Codes: G06N 5/045 Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence

Abstract

Techniques are disclosed for building a dictionary of words from combinations of symbols generated based on input data. A neuro-linguistic behavior recognition system includes a neuro-linguistic module that generates a linguistic model that describes data input from a source (e.g., video data, SCADA data, etc.). To generate words for the linguistic model, a lexical analyzer component in the neuro-linguistic module receives a stream of symbols, each symbol generated based on an ordered stream of normalized vectors generated from input data. The lexical analyzer component determines words from combinations of the symbols based on a hierarchical learning model having one or more levels. Each level indicates a length of the words to be identified at that level. Statistics are evaluated for the words identified at each level. The lexical analyzer component identifies one or more of the words having statistical significance.

Claims

1. A method for building a dictionary of words from combinations of symbols generated based on input data, comprising:

receiving a stream of symbols, each symbol associated with a cluster of vectors generated from input data;

determining words from combinations of the symbols in the stream based on a hierarchical learning model having one or more levels, wherein each level indicates a length of the words to be identified at that level and wherein statistics are evaluated for the words identified at each level;

identifying one or more of the words having statistical significance based on the evaluated statistics.

2. The method of claim 1, further comprising, outputting the identified one or more of the words having statistical significance and the statistics associated with each of the identified one or more of the words.

3. The method of claim 1, wherein the statistical significance is determined based on a score indicating how often a word is observed in the stream of symbols.

4. The method of claim 1, further comprising:

receiving a second stream of symbols;

determining words from combinations of the symbols in the second stream based on the hierarchical learning model; and

updating, via the hierarchical learning model, the statistics of previously identified words.

5. The method of claim 1, further comprising, generating a feature model from the identified one or more of the words having statistical significance, wherein the feature model includes generalizations of the identified one or more of the words having statistical significance.

6. The method of claim 5, wherein generating the feature model comprises:

identifying features associated with the cluster of vectors associated with each symbol;

generalizing the symbols based on the features.

7. The method of claim 5, further comprising, evaluating statistics of the generalizations.

8. A computer-readable storage medium storing instructions, which, when executed on a processor, performs an operation for building a dictionary of words from combinations of symbols generated based on input data, comprising:

receiving a stream of symbols, each symbol associated with a cluster of vectors generated from input data;

determining words from combinations of the symbols in the stream based on a hierarchical learning model having one or more levels, wherein each level indicates a length of the words to be identified at that level and wherein statistics are evaluated for the words identified at each level;

identifying one or more of the words having statistical significance based on the evaluated statistics.

9. The computer-readable storage medium of claim 8, wherein the operation further comprises, outputting the identified one or more of the words having statistical significance and the statistics associated with each of the identified one or more of the words.

10. The computer-readable storage medium of claim 8, wherein the statistical significance is determined based on a score indicating how often a word is observed in the stream of symbols.

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

receiving a second stream of symbols;

determining words from combinations of the symbols in the second stream based on the hierarchical learning model; and

updating, via the hierarchical learning model, the statistics of previously identified words.

12. The computer-readable storage medium of claim 8, wherein the operation further comprises, generating a feature model from the identified one or more of the words having statistical significance, wherein the feature model includes generalizations of the identified one or more of the words having statistical significance.

13. The computer-readable storage medium of claim 12, wherein generating the feature model comprises:

identifying features associated with the cluster of vectors associated with each symbol;

generalizing the symbols based on the features.

14. The computer-readable storage medium of claim 12, wherein the operation further comprises, evaluating statistics of the generalizations.

15. A system, comprising:

a processor; and

a memory storing one or more application programs configured to perform an operation for building a dictionary of words from combinations of symbols generated based on input data, comprising;

receiving a stream of symbols, each symbol associated with a cluster of vectors generated from input data;

determining words from combinations of the symbols in the stream based on a hierarchical learning model having one or more levels, wherein each level indicates a length of the words to be identified at that level and wherein statistics are evaluated for the words identified at each level;

identifying one or more of the words having statistical significance based on the evaluated statistics.

16. The system of claim 15, wherein the operation further comprises, outputting the identified one or more of the words having statistical significance and the statistics associated with each of the identified one or more of the words.

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

receiving a second stream of symbols;

determining words from combinations of the symbols in the second stream based on the hierarchical learning model; and

updating, via the hierarchical learning model, the statistics of previously identified words.

18. The system of claim 15, wherein the operation further comprises, generating a feature model from the identified one or more of the words having statistical significance, wherein the feature model includes generalizations of the identified one or more of the words having statistical significance.

19. The system of claim 18, wherein generating the feature model comprises:

identifying features associated with the cluster of vectors associated with each symbol;

generalizing the symbols based on the features.

20. The system of claim 18, wherein the operation further comprises, evaluating statistics of the generalizations.