Nice has been granted a patent for a computerized method that recommends effective energy break activities for contact center agents during their work shifts. The method utilizes machine learning to calculate motivation scores and sentiment analysis, ultimately providing a tailored list of recommended activities based on agent preferences and historical data. GlobalData’s report on Nice 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 Nice, Intelligent contact centers was a key innovation area identified from patents. Nice's grant share as of July 2024 was 61%. Grant share is based on the ratio of number of grants to total number of patents.
Activity recommendation system for contact center agents
The patent US12056649B2 outlines a computerized method and system designed to recommend effective energy break activities for agents working in contact centers. The method utilizes a processor, databases for interaction data and user preferences, and a memory to store these databases. Key components of the system include an analytics application that calculates sentiment analysis scores, speech analysis scores, and customer sentiment changes based on voice recordings of previous interactions. A machine learning model, trained using a support vector machine, computes a motivation score by aggregating various data points, including sentiment scores, performance indicators, and personal traits of the agent. This data is sourced from an Automatic Call Distributor (ACD) system and a workforce management system.
The activities recommendation module operates during the agent's work shift, calculating a motivation score and associating it with a preconfigured score range. It collects activities that fit this range and retrieves the agent's favorite and historical activities from the user preferences database. The system then calculates an activity occurrence score for each recommended activity, allowing the agent to select activities through an interactive interface. The process includes setting a time duration for each selected activity and displaying a timer to track the remaining time. This structured approach aims to enhance agent well-being and productivity by providing tailored activity recommendations based on real-time performance and preferences.
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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.
