State Farm Mutual Automobile Insurance had eight patents in big data during Q1 2024. State Farm Mutual Automobile Insurance Co has filed patents for systems and methods involving predictive data analytics, analysis of parametric events, user analytics computing device for processing telematics data, propensity analyzer for user activities, and transportation analytics computing device for calculating insurance premiums based on telematics data. These inventions aim to improve decision-making processes and enhance user experience in the insurance industry. GlobalData’s report on State Farm Mutual Automobile Insurance gives a 360-degree view of the company including its patenting strategy. Buy the report here.

State Farm Mutual Automobile Insurance grant share with big data as a theme is 37% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: Simplistic machine learning model generation tool for predictive data analytics (Patent ID: US20240095599A1)

The patent filed by State Farm Mutual Automobile Insurance Co. describes systems and methods for predictive data analytics. The method involves generating a guided user interface (GUI) to guide user operations, obtaining a dataset from a database, determining characteristics of data objects, identifying a subset of the dataset, selecting a machine learning algorithm, training a machine learning model, and implementing the trained model in a cloud server for distribution to client devices. The claims detail the steps involved in the method, including generating visualizations, selecting ML algorithms, performing dimension reduction, handling dataset statistics, and predicting target values. The system described in the patent includes means for generating the GUI, receiving inputs, obtaining datasets, generating visualizations, selecting algorithms, and implementing ML model generation tools in a cloud server.

In summary, the patent outlines a method and system for predictive data analytics using machine learning models. The method involves a guided user interface, dataset manipulation, algorithm selection, model training, and cloud-based implementation for distribution. The claims provide additional details on the specific operations involved in the method, such as visualization generation, dimension reduction, dataset handling, and target value prediction. The system described in the patent includes components for generating the GUI, receiving inputs, dataset management, algorithm selection, and cloud-based implementation of ML model generation tools.

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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.