Liberty Mutual Holding has patented a computer-implemented method for generating temporally dynamic predictions using machine learning models. The method processes prediction inputs through trained models to predict property damage claims or service center operational loads, automatically redirecting calls based on predictions. GlobalData’s report on Liberty Mutual Holding 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 Holding, was a key innovation area identified from patents. Liberty Mutual Holding'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 generation using ensemble machine learning models
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 forwards calls directed to service center devices to other devices, enhancing operational efficiency.
Furthermore, the ensemble model in the method is designed to identify a selected temporally trained machine learning model based on validation scores associated with each model. These validation scores are determined using validation data entries, which can include real-time or recent data. The ensemble model is also trained using real-time training data entries to improve the accuracy of predictions. Additionally, the method includes provisions for load balancing policies on communication systems associated with service centers based on the temporally dynamic predictions, ensuring optimal resource allocation. Overall, the patent outlines a sophisticated approach to generating predictions and optimizing service center operations through the integration of machine learning models and ensemble techniques, showcasing advancements in predictive analytics and operational efficiency in service-oriented industries.
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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.
