Hartford Financial Services Group has patented a system for risk relationship transaction automation. The system uses a back-end application server to analyze resource allocation transactions between an enterprise and entities, predicting approval likelihood based on medical codes. This reduces electronic messages and improves efficiency. GlobalData’s report on Hartford Financial Services Group 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 Hartford Financial Services Group, was a key innovation area identified from patents. Hartford Financial Services Group's grant share as of May 2024 was 52%. Grant share is based on the ratio of number of grants to total number of patents.
Risk relationship resource allocation tool for enterprise back-end server
A recently granted patent (Publication Number: US11990244B2) discloses a system designed to provide a risk relationship resource allocation tool through a back-end application computer server within an enterprise. The system includes a risk relationship data store containing electronic records representing transactions related to resource allocations between the enterprise and various entities. The back-end application server is programmed to receive and process requested resource allocation transactions, utilizing a predictive model trained with machine learning to generate a likelihood of acceptance score. This score determines the recommendation category for the transaction, streamlining the approval process and reducing the number of electronic messages transmitted. The system also generates a "next best action" based on machine learning assets, which is transmitted to a claim handler device for efficient decision-making.
Furthermore, the system incorporates international classification of diseases codes for medical conditions, particularly focusing on workers' compensation insurance claims. By utilizing data science model scoring and hierarchical variable clustering processes, the system can accurately assign recommendation categories such as likely related, unable to recommend, or unlikely related. The "next best action" generated by the system can be transmitted through various channels like email servers, calendar functions, and workflow applications, enhancing communication and decision-making efficiency within the enterprise. Overall, this patented system offers a comprehensive and automated approach to resource allocation decision-making, leveraging machine learning and data science for improved outcomes and reduced communication overhead.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
