Liberty Mutual Insurance‘s patent involves a computer-implemented method for generating temporally dynamic predictions using machine learning models trained on historical weather and claim data. The system can predict property damage claims or service center operational loads and automatically redirect calls to service center devices based on the predictions. GlobalData’s report on Liberty Mutual Insurance 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 Liberty Mutual Insurance, was a key innovation area identified from patents. Liberty Mutual Insurance's grant share as of February 2024 was 100%. Grant share is based on the ratio of number of grants to total number of patents.
Dynamic prediction for property damage claim or service center load
A recently granted patent (Publication Number: US11922284B1) discloses a computer-implemented method for generating a temporally dynamic prediction for a prediction input. The method involves processing the prediction input using a plurality of temporally trained machine learning models to generate model-specific prediction inferences. These inferences are then processed using an ensemble model to generate a temporally dynamic prediction, which can include property damage claim predictions or service center operational load predictions. Upon generating the prediction, the method automatically performs actions such as forwarding calls to different service center devices based on the prediction.
Furthermore, the patent details the configuration of the ensemble model, which is designed to identify a selected temporally trained machine learning model based on validation scores determined using validation data entries. The ensemble model is trained using real-time training data entries to enhance the accuracy of predictions. Additionally, the patent describes the use of the ensemble model to enforce load balancing policies on communication systems associated with service centers based on the temporally dynamic predictions. Overall, the patented method and apparatus aim to improve prediction accuracy and automate decision-making processes in scenarios related to property damage claims and service center operations.
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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.
