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 June 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 detected signals 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 functioning independently. The method involves collecting historical and current case data, which may be structured or unstructured, and utilizing a vector collector module to generate vector data. The anomaly signal processor employs machine learning models to identify anomalous signals in real-time, while the sentiment signal processor detects sentiment-based signals. The system further processes these signals to validate their authenticity and generates reports that visually represent the validated data, allowing service agents to prioritize cases needing attention.
Additionally, the patent details the assignment of weights and types to vectors during data generation, enhancing the model's ability to detect various anomaly and sentiment types. The reports generated include feedback features that can refine the training data for the machine learning models, ensuring continuous improvement in signal detection. The system also allows for corrective actions, such as escalating cases or reassigning them 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 case data in real-time.
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