UnitedHealth Group had 14 patents in big data during Q1 2024. The UnitedHealth Group Inc has filed patents for methods and systems related to resource allocation, automatic health data processing, optimizing procedure efficiency, and predicting payer response from claims data. These patents involve techniques such as using machine learning frameworks for resource allocation, generating predictive metrics for evaluating data sets, and utilizing deep learning frameworks for predicting claim denials. The inventions aim to improve efficiency, accuracy, and productivity in healthcare operations. 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 big data as a theme is 42% 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 specific subgroups for resource allocation based on the predicted causal effects.
The system includes a computing device with a processor and memory configured to implement the resource allocation method. It involves receiving historical data, generating predictive risk scores, and predicting causal effects using machine learning models. The system is designed to identify and prioritize resource allocation to specific resource-requesting entity subgroups based on the predicted causal effects, ultimately optimizing resource allocation decisions. The apparatus and computer program product also follow a similar approach, emphasizing the use of machine learning models for efficient resource allocation based on historical data and causal effect predictions.
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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.
