ANOMALY DETECTION IN SPATIAL AND TEMPORAL MEMORY SYSTEM

• US 20140067734A1
• Filed: 08/29/2013
• Published: 03/06/2014
• Est. Priority Date: 09/05/2012
• Status: Active Grant

• Current Assignee: Numenta, Inc.
• Original Assignee: Numenta, Inc.
• Inventors: Hawkins, Jeffrey C., Agarwal, Rahul
• Granted Patent: US 9,412,067 B2
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks

Abstract

Detecting patterns and sequences associated with an anomaly in predictions made a predictive system. The predictive system makes predictions by learning spatial patterns and temporal sequences in an input data that change over time. As the input data is received, the predictive system generates a series of predictions based on the input data. Each prediction is compared with corresponding actual value or state. If the prediction does not match or deviates significantly from the actual value or state, an anomaly is identified for further analysis. A corresponding state or a series of states of the predictive system before or at the time of prediction are associated with the anomaly and stored. The anomaly can be detected by monitoring whether the predictive system is placed in the state or states that is the same or similar to the stored state or states.

Claims

1. A method of detecting anomaly in a predictive system, comprising:

generating a prediction output by the predictive system at a first time responsive to receiving an input data received, the prediction output representing a predicted value or state at a second time subsequent to the first time;

storing at least a subset of states of the predictive system before the second time, the states of the predictive changing depending on time responsive to receiving the input data;

determining an anomaly by at least comparing the predicted value or state corresponding to the predictive output with an actual value or state at the second time; and

associating the anomaly with one or more stored states of the predictive system before the second time.

2. The method of claim 1, further comprising receiving a request to detect a subsequent occurrence of the anomaly.

3. The method of claim 2, further comprising monitoring the predictive system to detect the one or more stored states of the predictive system associated with the anomaly at a third time subsequent to the second time responsive to receiving the request.

4. The method of claim 3, further comprising detecting an occurrence of an event associated with the anomaly responsive to detecting the occurrence of the one or more stored states of the predictive system associated with the anomaly.

5. The method of claim 2, wherein determining the anomaly comprises comparing tallying accuracy of predicted values or states corresponding to predictive outputs over a first time period including the first time relative to actual values or states over a second time period including the second time.

6. The method of claim 1, wherein the predictive system is configured to learn and detect temporal sequences of spatial patterns in the input data.

7. The method of claim 1, generating the prediction output comprises:

generating a series of spatial pooler outputs in sparse distributed representation by processing the input data over time by a spatial pooler, each of the spatial pooler outputs indicating a spatial pattern detected in the input data at a time; and

generating the prediction output based on stored temporal relationships between the series of spatial pooler outputs by a sequence processor.

8. The method of claim 7, wherein the subset of the states of the predictive system comprises states of the sequence processor.

9. The method of claim 7, wherein determining the anomaly comprises comparing the prediction output at the first time with the spatial pooler output at the second time.

10. The method of claim 1, wherein detecting the anomaly comprises determining whether the predicted value or state deviates from the actual value or state above at the second time beyond a threshold difference.

11. A non-transitory computer-readable storage medium storing instructions thereon, the instructions when executed by a processor causing the processor to:

generate a prediction output by a predictive system at a first time responsive to receiving an input data received, the prediction output representing a predicted value or state at a second time subsequent to the first time;

store at least a subset of states of the predictive system before the second time, the states of the predictive changing depending on time responsive to receiving the input data;

determine an anomaly by at least comparing the predicted value or state corresponding to the predictive output with an actual value or state at the second time; and

associate the anomaly with one or more stored states of the predictive system before the second time.

12. The medium of claim 11, further comprising instructions causing the processor to receive a request to detect a subsequent occurrence of the anomaly.

13. The medium of claim 12, further comprising instructions causing the processor to monitor the predictive system to detect the one or more stored states of the predictive system associated with the anomaly at a third time subsequent to the second time responsive to receiving the request.

14. The medium of claim 13, further comprising detecting an occurrence of an event associated with the anomaly responsive to detecting the occurrence of the one or more stored states of the predictive system associated with the anomaly.

15. The medium of claim 12, wherein the anomaly is determined by comparing tallying accuracy of predicted values or states corresponding to predictive outputs over a first time period including the first time relative to actual values or states over a second time period including the second time.

16. The medium of claim 11, wherein the predictive system is configured to learn and detect temporal sequences of spatial patterns in the input data.

17. The medium of claim 11, wherein instructions for generating the prediction output comprises instructions causing the processor to:

generate a series of spatial pooler outputs in sparse distributed representation by processing the input data over time by a spatial pooler, each of the spatial pooler outputs indicating a spatial pattern detected in the input data at a time; and

generate the prediction output based on stored temporal relationships between the series of spatial pooler outputs by a sequence processor.

18. The medium of claim 17, wherein the subset of the states of the predictive system comprises states of the sequence processor.

19. The medium of claim 17, wherein instructions to determine the anomaly comprises instructions causing the processor to compare the prediction output at the first time with the spatial pooler output at the second time.

20. The medium of claim 11, wherein instructions to detect the anomaly comprises instructions causing the processor to determine whether the predicted value or state deviates from the actual value or state above at the second time beyond a threshold difference.

21. A computing system, comprising:

a processor;

a predicting system configured to generate a prediction output at a first time responsive to receiving an input data received, the prediction output representing a predicted value or state at a second time subsequent to the first time;

an anomaly detector configured to;

store at least a subset of states of the predictive system before the second time, the states of the predictive changing depending on time responsive to receiving the input data;

determine an anomaly by at least comparing the predicted value or state corresponding to the predictive output with an actual value or state at the second time; and

associate the anomaly with one or more stored states of the predictive system before the second time.