Walmart has patented a system using machine learning to predict user repurchases and desired time slots for transactions. The system generates feature vectors, makes predictions, and facilitates reservations for users. The technology integrates with a digital shopping cart system for upcoming transactions. 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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According to GlobalData’s company profile on Walmart, Transaction splitting was a key innovation area identified from patents. Walmart's grant share as of January 2024 was 37%. Grant share is based on the ratio of number of grants to total number of patents.
Predicting user repurchase and time slot for reservation in shopping
A recently granted patent (Publication Number: US11887023B2) describes a system that utilizes machine learning models to predict user behavior and preferences for upcoming transactions. The system generates feature vectors for users, including transaction-based and slot-based features, to predict repurchase likelihood and desired time slots for transactions. By using separate machine learning models, the system can accurately predict user behavior and offer reservation options to users through a graphical user interface. The system integrates these predictions with a digital shopping cart system to facilitate upcoming transactions based on user preferences.
Furthermore, the patent details a method implemented through computing instructions stored on non-transitory media that follows a similar process of generating feature vectors, predicting repurchase probabilities, and determining optimal time slots for transactions. The method leverages machine learning models, such as random forest decision trees or logistic regression models, to enhance prediction accuracy based on historical user data. By tracking user transaction frequency, timing, and engagement patterns, the system can tailor reservation functions to individual users, automatically reserving time slots and notifying users of availability. Overall, the patented system aims to streamline the transaction process by utilizing predictive analytics and machine learning to enhance user experience and increase transaction efficiency on electronic platforms.
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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.
