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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of detecting anomaly, comprising: updating a predictive device responsive to receiving input data at a first time; generating a prediction output based on the updated predictive device, the prediction output representing a predicted value or a state of a system at a second time subsequent to the first time; and detecting an anomaly by monitoring the prediction output. 2. The method of claim 1, wherein updating of the predictive device comprises activating or deactivating cells in the predictive device. 3. The method of claim 2, wherein generating the prediction output comprises detecting cells that are active in the updated predictive device. 4. The method of claim 2, wherein the activating or deactivating of cells in the predictive device causes the predictive device to perform online learning. 5. The method of claim 2, further comprising storing activating or deactivating states of at least a subset of the cells corresponding to the anomaly, and wherein detecting the anomaly comprises detecting activation or deactivation of at least the subset of cells. 6. The method of claim 2, wherein detecting the anomaly comprises detecting whether the predicted output deviates from the actual value or state above at the second time beyond a threshold difference. 7. The method of claim 1, wherein the predictive system is configured to learn temporal sequences of spatial patterns in the input data by establishing connections between cells in the predictive device based on the received input data. 8. The method of claim 7, wherein the predictive output is generated by at least detecting cells that are predictively activated based on activation of other cells connected to the predictively activated cells. 9. The method of claim 8, wherein a cell is predictively activated responsive to at least a subset of cells connected to the cell being activated. 10. 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, 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. 11. A non-transitory computer-readable storage medium storing instructions thereon, the instructions when executed by a processor causing the processor to: update a predictive device responsive to receiving input data at a first time; generate a prediction output based on the updated predictive device, the prediction output representing a predicted value or a state of a system at a second time subsequent to the first time; and detect an anomaly by monitoring the prediction output. 12. The computer-readable storage medium of claim 11, wherein instructions to update the predictive device comprises instructions to activate or deactivate cells in the predictive device. 13. The computer-readable storage medium of claim 12, wherein instructions to generate the prediction output comprises instructions to detect cells that are active in the updated predictive device. 14. The computer-readable storage medium of claim 12, wherein the activating or deactivating of cells in the predictive device causes the predictive device to perform online learning. 15. The computer-readable storage medium of claim 12, wherein further comprising instructions to store activating or deactivating states of at least a subset of the cells corresponding to the anomaly, and wherein instructions to detect the anomaly comprises instructions to detect activation or deactivation of at least the subset of cells. 16. The computer-readable storage medium of claim 12, wherein instructions to detect the anomaly comprises instructions to detect whether the predicted output deviates from the actual value or state above at the second time beyond a threshold difference. 17. The computer-readable storage medium of claim 12, wherein the predictive system is configured to learn temporal sequences of spatial patterns in the input data by establishing connections between cells in the predictive device based on the received input data. 18. The computer-readable storage medium of claim 17, wherein the predictive output is generated by detecting at least cells that are predictively activated by activation of other cells connected to the predictively activated cells. 19. The computer-readable storage medium of claim 19, wherein a cell is predictively activated responsive to at least a subset of cells connected to the cell being activated. 20. The computer-readable storage medium of claim 11, wherein instructions to generate the prediction output comprises instructions to: generate a series of spatial pooler outputs in sparse distributed representation by processing the input data over time, 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. 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, the prediction output representing a predicted value or state at a second time subsequent to the first time; and an anomaly detector configured to detect an anomaly by monitoring the prediction output.