Bottomline Technologies has patented a method using machine learning to suggest user actions in a payments or banking environment. The application analyzes user behavior to recommend actions based on parameters, utilizing algorithms like DensiCube, random forest, or k-means. GlobalData’s report on Bottomline Technologies 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 Bottomline Technologies, Dynamic storage rebalancing was a key innovation area identified from patents. Bottomline Technologies's grant share as of May 2024 was 31%. Grant share is based on the ratio of number of grants to total number of patents.
Machine learning application for suggesting user actions in banking
A recently granted patent (Publication Number: US11995563B2) outlines a method and system for automatically suggesting actions to a user in a user interface. The method involves receiving input parameters, accessing a list of possible actions, filtering out unavailable actions, and executing a machine learning model on each possible action to obtain a machine learning score. This score is then used to sort the list of possible actions. The machine learning model is built by iterating through possible rule sets using a data set of previous user behavior, which includes data from multiple users up to a threshold of data items for a specific user before solely using data from that user.
Furthermore, the patent describes a system that includes a special purpose server, a data storage device holding user behavior history, possible actions, and user behavior models, an internet connection, and a computing device. Users log into an application on the computing device, which sends input parameters to the special purpose server. The server then filters and processes the list of possible actions, executes the machine learning model on each action, stores the machine learning score, and sorts the list of actions. The machine learning model is built by the server through iterations of possible rule sets using previous user behavior data from multiple users, transitioning to data solely from the specific user after reaching a threshold of data items.
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