Automatic Data Processing has patented a method using multimodal multi-task learning to predict changes in customer demand based on subscription data and customer activity metrics. The computer-implemented method involves collecting data, modeling events, and predicting types and timing of changes in customer bundle subscriptions. GlobalData’s report on Automatic Data Processing 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 Automatic Data Processing, Digital lending was a key innovation area identified from patents. Automatic Data Processing's grant share as of April 2024 was 58%. Grant share is based on the ratio of number of grants to total number of patents.
Predicting customer demand changes based on service activity metrics
A recently granted patent (Publication Number: US11966927B2) outlines a computer-implemented method for predicting changes in customer demand. The method involves collecting subscription data for customers, determining changes in bundle subscriptions, and analyzing customer activity metrics. Through a multimodal multi-task learning architecture, including recurrent neural networks and fully connected neural networks, the system models various bundle subscription change events like upgrades, downgrades, and terminations. By leveraging machine intelligence and predictive algorithms, the system can predict the types and timing of changes in customer bundle subscriptions based on customer service activities. This predictive model is trained using customer task metrics, predicting subscription change events, computing probability density functions, and calculating a weighted average of these functions.
Furthermore, the patent also describes a system and a computer program product for predicting changes in customer demand. The system includes a bus system, storage device, and processors that collect subscription data, determine changes in bundle subscriptions, and model bundle subscription change events using neural networks. Similarly, the computer program product, stored on a non-volatile computer-readable storage medium, enables processors to collect data, determine changes, and predict customer demand changes based on past activities and shared static features. By utilizing advanced neural network architectures and machine learning algorithms, these systems and programs aim to provide accurate predictions regarding customer demand changes, enhancing business strategies and decision-making processes in various industries.
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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.
