UnitedHealth Group’s patent involves a dual-model system for predictive data labeling, particularly in medical data analysis for challenging diseases. The method includes generating predicted label probabilities using machine learning models trained on candidate data subsets. This innovation aims to enhance accuracy in predicting labels. 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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According to GlobalData’s company profile on UnitedHealth Group, Content recommendation models was a key innovation area identified from patents. UnitedHealth Group's grant share as of May 2024 was 38%. Grant share is based on the ratio of number of grants to total number of patents.
Predictive data labeling using dual-model system
A recently granted patent (Publication Number: US12002585B2) discloses a computer-implemented method involving machine learning models to predict label probabilities for candidate identifiers. The method includes training a first machine learning model to generate predicted label probabilities based on a label probability set and a data subset of a candidate data set. This data subset is determined by specific candidate identifier subsets and record thresholds. Additionally, a second machine learning model is utilized to identify the label probability set based on another data subset, leading to a comprehensive approach in predicting label probabilities for candidate identifiers.
Furthermore, the patent extends to a computing system and a computer program product that implement the described method. These systems are designed to train machine learning models, identify positive and negative candidate data subsets, and generate candidate label training subsets. By incorporating long-term and short-term record thresholds, along with candidate selection rule sets, the systems aim to enhance the accuracy and efficiency of predicting label probabilities for candidate identifiers. Overall, the patent presents a sophisticated approach to utilizing machine learning models in the context of candidate identification and label prediction, potentially offering valuable applications in various industries requiring precise data analysis and decision-making processes.
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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.
