Epicor Software has been granted a patent for a system and method that processes demand data for resources in a technology platform. The method involves collecting demand profiles, creating cumulative demand plots, calculating distance metrics, clustering resources, and generating visualizations to aid in management and control. GlobalData’s report on Epicor 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 Epicor Software, Medical device secure data transmission was a key innovation area identified from patents. Epicor Software's grant share as of June 2024 was 48%. Grant share is based on the ratio of number of grants to total number of patents.
Resource demand processing and clustering system
The granted patent US11977925B2 outlines a resource demand processing system designed to efficiently manage and analyze demand data for various resources. The system comprises a memory and a processor that processes demand profiles sourced from a technology platform. Key functionalities include the reformatting of demand profiles into cumulative demand plots, which represent a running sum of demand values over time. The system calculates distance metrics between these plots based on the area defined by their intersections, enabling the clustering of resources into distinct groups. Additionally, the system generates visualizations of these clusters, enhancing the understanding of resource demand dynamics.
Further enhancements to the system include the generation of mathematical characterizations for each cluster, which can facilitate load balancing and resource management. The patent also describes the use of machine learning models to analyze clusters, producing outputs such as metatags, control parameters, and predictions. The resources addressed by the system encompass a wide range, including computing, energy, web, and communication resources, as well as physical and virtual components. The methodology emphasizes the normalization of cumulative demand plots and the application of various machine learning techniques for clustering, ensuring a robust approach to demand data processing and resource optimization.
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