UnitedHealth Group had 47 patents in artificial intelligence during Q1 2024. The patents filed by UnitedHealth Group Inc in Q1 2024 focus on resource allocation using machine learning frameworks, personalized autocomplete predictions, processing data with different timescales, generating predictive insights for users based on physiological features, and automatic health data processing through symptom mapping and categorization. These innovations aim to improve healthcare services and efficiency through advanced technology and data analysis. 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 grant share with artificial intelligence as a theme is 48% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Causal inference for optimized resource allocation (Patent ID: US20240104407A1)
The patent filed by UnitedHealth Group Inc. describes a method and system for resource allocation using machine learning models. The method involves receiving historical data related to resource allocation decisions and outcomes, utilizing predictive and causal inference machine learning models to generate risk scores and predict causal effects, respectively. The models are trained to determine causal effect values for different resource-requesting entity subgroups, allowing for the identification and prioritization of these subgroups for resource allocation. The system aims to optimize resource allocation by taking into account past actions and outcomes, as well as expert knowledge data stored in databases.
The invention outlined in the patent claims involves a computer-implemented method, an apparatus, and a computer program product for allocating resources based on historical data and predictive and causal inference machine learning models. The method includes actions such as identifying resource-requesting entity subgroups, ranking them based on causal effect values, and performing prediction-based actions to allocate resources efficiently. The apparatus and computer program product are configured to receive historical data, generate predictive risk scores, and predict causal effects using machine learning models, ultimately improving resource allocation decisions by prioritizing certain resource-requesting entity subgroups. The patent emphasizes the importance of utilizing both predictive and causal inference models to optimize resource allocation strategies.
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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.
