MAPPER COMPONENT FOR A NEURO-LINGUISTIC BEHAVIOR RECOGNITION SYSTEM

• US 20160171096A1
• 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 10,373,062 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 18/2137 based on criteria of topology preservation, e.g. multidimensional scaling or self-organising maps
• CPC Class Codes: G06F 18/23 Clustering techniques
• CPC Class Codes: G06F 18/28 Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries
• 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
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06V 10/32 Normalisation of the pattern dimensions
• CPC Class Codes: G06V 10/762 using clustering, e.g. of similar faces in social networks
• CPC Class Codes: G06V 30/19127 Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
• CPC Class Codes: G06V 30/1914 Determining representative reference patterns, e.g. averaging or distorting patterns; Generating dictionaries, e.g. user dictionaries
• CPC Class Codes: G06V 30/268 Lexical context
• CPC Class Codes: H01B 1/02 mainly consisting of metals or alloys

Abstract

Techniques are disclosed for generating a sequence of symbols based on input data for a neuro-linguistic model. The model may be used by a behavior recognition system to analyze the input data. A mapper component of a neuro-linguistic module in the behavior recognition system receives one or more normalized vectors generated from the input data. The mapper component generates one or more clusters based on a statistical distribution of the normalized vectors. The mapper component evaluates statistics and identifies statistically relevant clusters. The mapper component assigns a distinct symbol to each of the identified clusters.

Claims

1. A method for generating a sequence of symbols based on a stream of normalized vectors generated from input data, the method comprising:

receiving a normalized vector of feature values generated from input data, each feature value associated with one of a plurality of features;

for each feature value in the normalized vector;

evaluating a distribution of one or more clusters in a cluster space corresponding to one of the plurality of features associated with the feature value, and

mapping the feature value to one of the clusters based on the distribution.

2. The method of claim 1, further comprising:

receiving a second normalized vector of feature values generated from second input data; and

for each feature value in the second normalized vector;

mapping the second normalized vector to a corresponding cluster,

updating the distribution of the corresponding cluster based on the mapping, and

upon determining that the corresponding cluster has statistical significance based on the updated distribution, outputting one of a plurality of symbols associated with the cluster.

3. The method of claim 1, further comprising, upon determining that two or more of the clusters overlap in the cluster space based on the evaluated distribution of each of the clusters, merging the two or more clusters.

4. The method of claim 1, wherein the statistical significance of each cluster is determined based on a statistical significance score that indicates a specified amount of the feature values mapping to the cluster over time.

5. The method of claim 4, further comprising, upon determining that feature values mapping to the cluster have not been received after a period based on a function of time, decaying the statistical significance of the cluster.

6. The method of claim 1, wherein the statistics of each cluster include a mean, variance, and standard deviation.

7. The method of claim 1, wherein each value in normalized vectors are within a range of 0 and 1, inclusive.

8. A computer-readable storage medium storing instructions, which, when executed on a processor, performs an operation for generating a sequence of symbols based on a stream of normalized vectors generated from input data, the operation comprising:

receiving a normalized vector of feature values generated from input data, each feature value associated with one of a plurality of features;

for each feature value in the normalized vector;

evaluating a distribution of one or more clusters in a cluster space corresponding to one of the plurality of features associated with the feature value, and

mapping the feature value to one of the clusters based on the distribution.

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

receiving a second normalized vector of feature values generated from second input data; and

for each feature value in the second normalized vector;

mapping the second normalized vector to a corresponding cluster,

updating the distribution of the corresponding cluster based on the mapping, and

upon determining that the corresponding cluster has statistical significance based on the updated distribution, outputting one of a plurality of symbols associated with the cluster.

10. The computer-readable storage medium of claim 8, wherein the operation further comprises, upon determining that two or more of the clusters overlap in the cluster space based on the evaluated distribution of each of the clusters, merging the two or more clusters.

11. The computer-readable storage medium of claim 8, wherein the statistical significance of each cluster is determined based on a statistical significance score that indicates a specified amount of the feature values mapping to the cluster over time.

12. The computer-readable storage medium of claim 11, wherein the operation further comprises, upon determining that feature values mapping to the cluster have not been received after a period based on a function of time, decaying the statistical significance of the cluster.

13. The computer-readable storage medium of claim 8, wherein the statistics of each cluster include a mean, variance, and standard deviation.

14. The computer-readable storage medium of claim 8, wherein each value in normalized vectors are within a range of 0 and 1, inclusive.

15. A system, comprising:

a processor; and

a memory storing one or more application programs configured to perform an operation for generating a sequence of symbols based on a stream of normalized vectors generated from input data, the operation comprising;

receiving a normalized vector of feature values generated from input data, each feature value associated with one of a plurality of features,

for each feature value in the normalized vector;

evaluating a distribution of one or more clusters in a cluster space corresponding to one of the plurality of features associated with the feature value, and

mapping the feature value to one of the clusters based on the distribution.

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

receiving a second normalized vector of feature values generated from second input data; and

for each feature value in the second normalized vector;

mapping the second normalized vector to a corresponding cluster,

updating the distribution of the corresponding cluster based on the mapping, and

upon determining that the corresponding cluster has statistical significance based on the updated distribution, outputting one of a plurality of symbols associated with the cluster.

17. The system of claim 15, wherein the operation further comprises, upon determining that two or more of the clusters overlap in the cluster space based on the evaluated distribution of each of the clusters, merging the two or more clusters.

18. The system of claim 15, wherein the statistical significance of each cluster is determined based on a statistical significance score that indicates a specified amount of the feature values mapping to the cluster over time.

19. The system of claim 18, wherein the operation further comprises, upon determining that feature values mapping to the cluster have not been received after a period based on a function of time, decaying the statistical significance of the cluster.

20. The system of claim 15, wherein each value in normalized vectors are within a range of 0 and 1, inclusive.