PERCEPTUAL ASSOCIATIVE MEMORY FOR A NEURO-LINGUISTIC BEHAVIOR RECOGNITION SYSTEM
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 . (canceled)
2 . A 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 plurality of video frames to obtain normalized data for the plurality of video frames;
generating, via the at least one processor, a syntax for a neuro-linguistic model, the syntax including a stable model of phrases;
identifying, via the at least one processor, instances of at least one machine-readable phrase from the stable model of phrases and based on a connected graph;
calculating, via the at least one processor, an unusualness score for an observation of a first machine-readable phrase from the at least one machine-readable phrase identified in the connected graph; and
publishing, via the at least one processor, an alert associated with the observation of the first machine-readable phrase, the alert indicating an anomaly in the behavior of the object.
3 . The computer-implemented method of claim 2 , wherein generating the syntax includes:
generating, via the at least one processor, a plurality of machine-readable symbols from the normalized data, each machine-readable symbol from the plurality of machine-readable symbols 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 plurality of machine-readable symbols.
4 . The computer-implemented method of claim 2 , wherein generating the syntax includes:
evaluating, via the at least one processor, statistics for a plurality of combinations of machine-readable words co-occurring in a stream of machine-readable words, the statistics including a frequency at which combinations of machine-readable words from the plurality of combinations of machine-readable words co-occur; and
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 co-occurring machine-readable words.
5 . The computer-implemented method of claim 2 , wherein generating the syntax includes generating, via the at least one processor, a connected graph having a plurality of nodes and a plurality of edges, each node from the plurality of nodes representing one machine-readable word in a stream of machine-readable words, and each edge from the plurality of edges connecting the nodes representing a probabilistic measure of co-occurrence of pairs of statistically relevant words in the stream of machine-readable words.
6 . The computer-implemented method of claim 2 , wherein generating the syntax includes generating a connected graph, via the at least one processor, the connected graph including a plurality of nodes and a plurality of edges, each edge from the plurality of edges weighted based on a statistical significance score between each pair of nodes from the plurality of nodes.
7 . The computer-implemented method of claim 2 , further comprising:
receiving a stream of machine-readable words; and
updating statistics of the co-occurring words based on the stream of machine-readable words.
8 . The computer-implemented method of claim 2 , wherein generating the syntax includes generating a dictionary of machine-readable words based on an observed-sequence of the machine-readable symbols, the method further comprising at least one of:
decreasing a statistical significance score of co-occurring machine-readable words, from the dictionary of machine-readable words, that are less frequently observed over time; or
increasing a statistical significance score of co-occurring machine-readable words, from the dictionary of machine-readable words, that are more frequently observed over time.
9 . A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to:
receive a plurality of video frames from a video source, the plurality of video frames including a representation of an object;
normalize, 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;
generate, via the at least one processor, a syntax for a neuro-linguistic model, the syntax including a stable model of phrases;
identify, via the at least one processor, instances of at least one machine-readable phrase from the stable model of phrases and based on a connected graph;
calculate, via the at least one processor, an unusualness score for an observation of a first machine-readable phrase from the at least one machine-readable phrase identified in the connected graph; and
publish, via the at least one processor, an alert associated with the observation of the first machine-readable phrase, the alert indicating an anomaly in the behavior of the object.
10 . The non-transitory computer-readable storage medium of claim 9 , wherein the instructions to generate the syntax include instructions to:
generate a plurality of machine-readable symbols from the normalized data, each machine-readable symbol from the plurality of machine-readable symbols 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 plurality of machine-readable symbols.
11 . The non-transitory computer-readable storage medium of claim 9 , wherein the instructions to generate the syntax include instructions to:
evaluate statistics for a plurality of combinations of machine-readable words co-occurring in a stream of machine-readable words, the statistics including a frequency at which combinations of machine-readable words from the plurality of combinations of machine-readable words co-occur; and
update a model of combinations of machine-readable words based on the evaluated statistics, the model identifying statistically relevant observations of co-occurring machine-readable words.
12 . The non-transitory computer-readable storage medium of claim 9 , wherein the instructions to generate the syntax include instructions to generate a connected graph having a plurality of nodes and a plurality of edges, each node from the plurality of nodes representing one machine-readable word in a stream of machine-readable words, and each edge from the plurality of edges connecting the nodes representing a probabilistic measure of co-occurrence of pairs of statistically relevant words in the stream of machine-readable words.
13 . The non-transitory computer-readable storage medium of claim 9 , wherein the instructions to generate the syntax include instructions to generate a connected graph, the connected graph including a plurality of nodes and a plurality of edges, each edge from the plurality of edges weighted based on a statistical significance score between each pair of nodes from the plurality of nodes.
14 . The non-transitory computer-readable storage medium of claim 9 , further storing instructions that, when executed by the processor, cause the processor to:
receive a stream of machine-readable words; and
update statistics of the co-occurring words based on the stream of machine-readable words.
15 . The non-transitory computer-readable storage medium of claim 9 , wherein the instructions to generate the syntax include instructions to generate a dictionary of machine-readable words based on an observed-sequence of the machine-readable symbols, the non-transitory computer-readable storage medium further storing instructions that, when executed by the processor, cause the processor to:
decrease a statistical significance score of co-occurring machine-readable words, from the dictionary of machine-readable words, that are less frequently observed over time; or
increase a statistical significance score of co-occurring machine-readable words, from the dictionary of machine-readable words, that are more frequently observed over time.
16 . A system, comprising:
a processor; and
a memory storing processor-executable instructions that, when executed by the processor, cause the processor to:
receive a plurality of video frames from a video source, the plurality of video frames including a representation of an object;
normalize, 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;
generate, via the at least one processor, a syntax for a neuro-linguistic model, the syntax including a stable model of phrases;
identify, via the at least one processor, instances of at least one machine-readable phrase from the stable model of phrases and based on a connected graph;
calculate, via the at least one processor, an unusualness score for an observation of a first machine-readable phrase from the at least one machine-readable phrase identified in the connected graph; and
publish, via the at least one processor, an alert associated with the observation of the first machine-readable phrase, the alert indicating an anomaly in the behavior of the object.
17 . The system of claim 16 , wherein the instructions to generate the syntax include instructions to:
generate a plurality of machine-readable symbols from the normalized data, each machine-readable symbol from the plurality of machine-readable symbols 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 plurality of machine-readable symbols.
18 . The system of claim 16 , wherein the instructions to generate the syntax include instructions to:
evaluate statistics for a plurality of combinations of machine-readable words co-occurring in a stream of machine-readable words, the statistics including a frequency at which combinations of machine-readable words from the plurality of combinations of machine-readable words co-occur; and
update a model of combinations of machine-readable words based on the evaluated statistics, the model identifying statistically relevant observations of co-occurring machine-readable words.
19 . The system of claim 16 , wherein the instructions to generate the syntax include instructions to generate a connected graph having a plurality of nodes and a plurality of edges, each node from the plurality of nodes representing one machine-readable word in a stream of machine-readable words, and each edge from the plurality of edges connecting the nodes representing a probabilistic measure of co-occurrence of pairs of statistically relevant words in the stream of machine-readable words.
20 . The system of claim 16 , wherein the instructions to generate the syntax include instructions to generate a connected graph, the connected graph including a plurality of nodes and a plurality of edges, each edge from the plurality of edges weighted based on a statistical significance score between each pair of nodes from the plurality of nodes.
21 . The system of claim 16 , wherein the memory further stores instructions that, when executed by the processor, cause the processor to:
receive a stream of machine-readable words; and
update statistics of the co-occurring words based on the stream of machine-readable words.