Progress Software has been granted a patent for a system and method that models and predicts asset anomalies using sensor data. The process involves receiving and smoothing sensor data, generating asset state spaces, identifying anomalies, and training models specific to asset groups. GlobalData’s report on Progress Software gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Progress Software, Data backup blockchains was a key innovation area identified from patents. Progress Software's grant share as of July 2024 was 71%. Grant share is based on the ratio of number of grants to total number of patents.
Asset anomaly prediction using sensor data modeling
The patent US12067504B1 outlines a method and system for analyzing sensor data from assets to determine behavioral patterns and states. The process begins with the collection of sensor data from a first asset at two different times, which is then used to generate asset state spaces for each time point. By comparing these state spaces with those of a second asset, the system identifies asset-level behavioral groups, including a specific group that encompasses the first asset. The method also involves training a base model tailored to this group, which can be further refined into a final model specific to the first asset.
Additionally, the patent details various techniques for enhancing the analysis of sensor data. This includes embedding information about sub-components of the asset within the state space and generating a comprehensive string that captures sensor identifiers, group identifiers, and any detected anomalies. The method employs change point detection to categorize sensor stages and utilizes statistical measures to identify correlations among sensors. Furthermore, the system can identify rare asset states based on the compiled asset state space information, thereby providing insights into asset behavior and performance.
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