Perceptual associative memory for a neuro-linguistic behavior recognition system
View Patent ↗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.
1. A computer-implemented method for analyzing behavior of an object, the method comprising:
receiving a plurality of video frames from a video source, the plurality of video frames including a representation of an object;
normalizing, via at least one processor, data in each video frame from the received plurality of video frames to obtain normalized data for the plurality of video frames;
performing, via the at least one processor, neuro-linguistic analysis on the normalized data;
generating, via the at least one processor, a syntax for a neuro-linguistic model based on the neuro-linguistic analysis, the syntax including a stable model of phrases, wherein generating the syntax includes:
generating, via the at least one processor, machine-readable symbols from the normalized data, each machine-readable symbol being associated with a distinct cluster of the normalized data, and building a dictionary of machine-readable words based on an observed-sequence of the machine-readable symbols;
evaluating, via the at least one processor, statistics for combinations of machine-readable words co-occurring in a stream of machine-readable words, the statistics including a frequency at which the combinations of words co-occur;
updating, via the at least one processor, a model of combinations of machine-readable words based on the evaluated statistics, the model identifying statistically relevant observations of machine-readable words co-occurring in the input stream of machine-readable symbols; and
generating, via the at least one processor, a connected graph, each node in the connected graph representing one of the words in the stream, and edges connecting the nodes representing a probabilistic measure of co-occurrence of pairs of statistically relevant words in the stream connected by an edge of the graph;
identifying, via the at least one processor, instances of at least one machine-readable phrase based on the connected graph;
determining, via the at least one processor, an unusualness score for an observation of at least a first one of the machine-readable phrases identified in the connected graph; and
publishing, via the at least one processor, an alert regarding the observation of the first machine-readable phrase, the alert indicating an anomaly in the behavior of the object.
2. The computer-implemented 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 computer-implemented method of claim 2 , further comprising:
identifying one or more phrases, from the stable model of phrases, that include co-occurring words having a statistical significance score that exceeds a specified threshold; and
outputting the identified phrases.
4. The computer-implemented method of claim 1 , wherein the edges are weighted based on a statistical significance score between each node.
5. The computer-implemented method of claim 1 , further comprising:
receiving a second stream of machine-readable words; and
updating the statistics of the co-occurring words based on the second stream of machine-readable words.
6. The computer-implemented method of claim 5 , further comprising:
decreasing a statistical significance score of co-occurring words that are less frequently observed over time.
7. The computer-implemented method of claim 5 , further comprising:
increasing a statistical significance score of co-occurring words that are more frequently observed over time.
8. A non-transitory computer-readable storage medium storing instructions, which, when executed on a processor, performs an operation for analyzing behavior of an object, the instructions comprising instructions to:
receive a plurality of video frames including a representation of an object;
normalize data in each video frame from the received plurality of video frames to obtain normalized data for the plurality of video frames;
perform neuro-linguistic analysis on the normalized data;
generate a syntax for a neuro-linguistic model based on the neuro-linguistic analysis, the syntax including a stable model of phrases, wherein the instructions to generate the syntax includes instructions to:
generate machine-readable symbols from the normalized data, each machine-readable symbol being associated with a distinct cluster of the normalized data, and build a dictionary of machine-readable words based on an observed-sequence of the machine-readable symbols;
evaluate statistics for combinations of machine-readable words co-occurring in a stream of machine-readable words, the statistics including a frequency at which the combinations of words co-occur;
update a model of combinations of machine-readable words based on the evaluated statistics, the model identifying statistically relevant observations of machine-readable words co-occurring in the input stream of machine-readable symbols; and
generate a connected graph, each node in the connected graph representing one of the words in the stream, and edges connecting the nodes representing a probabilistic measure of co-occurrence of pairs of statistically relevant words in the stream connected by an edge of the graph;
identify instances of at least one machine-readable phrase, from the stable model of phrases, based on the connected graph;
determine an unusualness score for an observation of at least a first one of the machine-readable phrases identified in the connected graph; and
publish an alert regarding the observation of the first machine-readable phrase, the alert indicating an anomaly in the behavior of the object.
9. The non-transitory 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 non-transitory computer-readable storage medium of claim 9 , wherein the instructions further comprises instructions to:
identify one or more phrases from the stable model of phrases that includes co occurring words having a statistical significance score that exceeds a specified threshold; and
output the identified phrases.
11. The non-transitory computer-readable storage medium of claim 8 , wherein the edges are weighted based on a statistical significance score between each node.
12. The non-transitory computer-readable storage medium of claim 8 , wherein the instructions further comprises instructions to:
receive a second stream of words; and
update the statistics of the co-occurring words based on the second stream of words.
13. The non-transitory computer-readable storage medium of claim 12 , wherein the instructions further comprises instructions to: decrease a statistical significance score of co-occurring words that are less frequently observed over time.
14. The non-transitory computer-readable storage medium of claim 12 , wherein the instructions further comprises instructions to:
increase 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 analyzing behavior of an object, the operation comprising:
receiving a plurality of video frames including a representation of an object;
normalizing data in each video frame from the received plurality of video frames to obtain normalized data for the plurality of video frames;
performing neuro-linguistic analysis on the normalized data;
generating a syntax for a neuro-linguistic model based on the neuro-linguistic analysis, the syntax including a stable model of phrases, wherein generating the syntax includes:
generating machine-readable symbols from the normalized data, each machine-readable symbol being associated with a distinct cluster of the normalized data, and building a dictionary of machine-readable words based on an observed-sequence of the machine-readable symbols;
evaluating statistics for combinations of machine-readable words co-occurring in a stream of machine-readable words, the statistics including a frequency at which the combinations of words co-occur,
updating a model of combinations of machine-readable words based on the evaluated statistics, the model identifying statistically relevant observations of machine-readable words co-occurring in the input stream of machine-readable symbols, and
generating a connected graph, each node in the connected graph representing one of the words in the stream, and edges connecting the nodes representing a probabilistic measure of co-occurrence of pairs of statistically relevant words in the stream connected by an edge of the graph,
identifying instances of at least one machine-readable phrase, from the stable model of phrases, based on the connected graph,
determining an unusualness score for an observation of at least a first one of the machine-readable phrases identified in the connected graph, and
issuing an alert regarding the observation of the first machine-readable phrase, the alert indicating an anomaly in the behavior of the object.
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, from the stable model of phrases, that include 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 machine-readable words; and
updating the statistics of the co-occurring words based on the second stream of machine-readable 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.