UnitedHealth Group had 17 patents in big data during Q2 2024. The UnitedHealth Group Inc has filed patents for methods and systems that improve resource allocation based on non-linear causal effect predictions, detect feature bias in machine learning models, process medical claims for predictive analytics and fraud detection, optimize machine-learning models for investigative processes, and orchestrate complex data processing schemes using a machine-learning based orchestration model. These innovations aim to enhance efficiency and accuracy in various healthcare-related processes. GlobalData’s report on UnitedHealth Group gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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UnitedHealth Group had no grants in big data as a theme in Q2 2024.
Recent Patents
Application: Machine learning techniques for maintaining optimum number of resources for distrubution to selected entities based on non-causal inference (Patent ID: US20240211779A1)
The patent filed by UnitedHealth Group Inc. describes a method and system for improving the allocation of limited resources by predicting the non-linear causal effects of resource allocation on specific entities based on historical data and expert knowledge. The system uses a resource allocation machine learning framework to determine optimal resource allocation amounts for different entity cohorts and then configures operations accordingly. The method involves generating non-linear causal effect predictions, determining an optimum operation configuration, and initiating prediction-based actions based on the optimal resource allocation amounts.
The patent claims detail the computer-implemented method, computing apparatus, and computer program product for implementing the resource allocation system. The method involves receiving historical data and directed acyclic graph data, generating non-linear causal effect predictions using a machine learning model, determining optimal resource allocation values, and initiating actions based on the determined configuration. The system utilizes expert knowledge data, causal variable values, and supervised machine learning regression to optimize resource allocation for various resource-receiving entities. Additionally, the method includes determining optimal causal variable values based on a causal benefit curve and a cost threshold associated with the causal variable. Overall, the patent aims to enhance resource allocation efficiency by leveraging predictive analytics and machine learning techniques.
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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.
