Rimini Street. has been granted a patent for a computing system that utilizes machine learning to detect anomalies and sentiments in client case data. The system integrates historical and current data, validates signals, and presents actionable insights through a user-friendly interface, enhancing proactive client case management. GlobalData’s report on Rimini Street 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 Rimini Street, Facial recognition AI was a key innovation area identified from patents. Rimini Street's grant share as of July 2024 was 20%. Grant share is based on the ratio of number of grants to total number of patents.
Machine learning-based anomaly detection and user interface arrangement
The granted patent US12026076B2 outlines a computing system and method for arranging user interface elements based on signals detected in case data. The system employs a machine learning-based data signal detection framework that includes an anomaly signal processor and a sentiment signal processor, both operating independently. The method involves collecting historical and current case data, training machine learning models to detect anomalous and sentiment-based signals, and generating validated signal data. The system can automatically identify and separate cases with negative and positive signals, determine which cases require attention, and generate real-time reports that visually depict the validated data. The user interface is customized based on the validated signals, enhancing the agent's ability to respond effectively to client needs.
Additionally, the patent details various features of the system, such as the assignment of weights and types to vectors in the historical case data, the use of different machine learning models for anomaly detection and sentiment analysis, and the inclusion of feedback mechanisms to refine model training. The system can also take corrective actions, such as escalating cases to different agents based on the identified signals. This comprehensive approach aims to improve the efficiency and effectiveness of client service operations by leveraging advanced machine learning techniques to analyze and respond to case data in real-time.
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