Walmart had eight patents in big data during Q2 2024. The patents filed by Walmart Inc in Q2 2024 include methods and systems for utilizing machine learning modules to determine risk scores for change requests, segmenting users based on propensity scores, generating predictions for repurchases and time slots for users, mapping interior spaces of product storage facilities, and predicting price affinities for users based on price band activity data. These innovations aim to improve decision-making processes, user experiences, and operational efficiency within the retail industry. GlobalData’s report on Walmart gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Walmart had no grants in big data as a theme in Q2 2024.
Recent Patents
Application: System and method for automatically assessing change requests (Patent ID: US20240211544A1)
The patent filed by Walmart Inc. describes a method and system for training machine learning modules to determine risk scores associated with change requests. The method involves detecting and correcting label noises in historical change data, training the machine learning modules based on this data, determining risk scores for change requests, and making change approvals based on these risk scores and a threshold. The system also includes features such as imputing missing values, encoding categorical features, and augmenting minority class data points to improve the training dataset. Additionally, the system can detect concept drift and re-train the machine learning module accordingly. Furthermore, the system allows for feedback from domain experts to be incorporated into the training dataset for re-training the machine learning module.
Overall, the patent outlines a comprehensive system and method for effectively training machine learning modules to assess risk associated with change requests. By addressing label noises, imputing missing values, and incorporating feedback from domain experts, the system aims to enhance the accuracy and reliability of determining change approvals based on risk scores. The system's ability to detect concept drift and re-train the machine learning module accordingly ensures that the system remains adaptive and up-to-date with changing data distributions.
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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.
