Time Aggregation and Sparse Distributed Representation Encoding for Pattern Detection

Abstract

A spatial and temporal memory system (STMS) processes input data to detect whether spatial patterns and / or temporal sequences of spatial patterns exist within the data, and to make predictions about future data. The data processed by the STMS may be retrieved from, for example, one or more database fields and is encoded into a distributed representation format using a coding scheme. The performance of the STMS in predicting future data is evaluated for the coding scheme used to process the data as performance data. The selection and prioritization of STMS experiments to perform may be based on the performance data for an experiment. The best fields, encodings, and time aggregations for generating predictions can be determined by an automated search and evaluation of multiple STMS systems.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application is a continuation of co-pending U.S. application Ser. No. 13 / 218,202, filed Aug. 25, 2011, which is incorporated by reference herein in its entirety.[0002]This application is also related to U.S. patent application Ser. No. 13 / 218,170, entitled “Encoding of Data for Processing in A Spatial and Temporal Memory System”, filed Aug. 25, 2011; U.S. patent application Ser. No. 13 / 218,194, entitled “Automated Search for Detecting Patterns And Sequences in Data Using A Spatial and Temporal Memory System”, filed Aug. 25, 2011; and U.S. patent application Ser. No. 13 / 046,464, entitled “Temporal Memory Using Sparse Distributed Representation”, filed Mar. 11, 2011. All of the foregoing applications are incorporated herein in their entirety by reference for all purposes.BACKGROUND[0003]1. Field of the Disclosure[0004]The present invention relates to spatial and temporal memory system processing, and more specifically to automatically ...

AI Technical Summary

Benefits of technology

This patent relates to a method and system for extracting data from sources like databases and coding it into a distributed representation format. The system can identify patterns in the data and make predictions about future patterns. The performance of the system can be evaluated by comparing the predictions with the actual data. Additionally, the patent describes a method for searching for specific patterns in data and generating multiple memories to identify temporal sequences of patterns. The technical effects of this patent allow for improved data extraction and prediction, as well as improved performance evaluation and pattern search.

Problems solved by technology

However, most of these software products are complex to use, often requiring weeks of training, mathematical expertise and complex data management.

Hence, generating a useful predictive model is a daunting and expensive task for many enterprises.

Depending on which techniques the user applies and how the data sets are encoded, these predictive analytic products may or may not yield use predictions.

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