Nice had 12 patents in artificial intelligence during Q1 2024. Nice Ltd has filed patents for computerized systems and methods for managing data items, predicting fraudulent financial account access, managing workload of agents based on readability scores, monitoring quality of interactions, and generating high-quality synthetic fraud data. These innovations utilize machine learning, deep learning, and fraud detection techniques to improve efficiency and accuracy in various aspects of data management and security. 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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Nice grant share with artificial intelligence as a theme is 41% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: System and method for intelligent personalized screen recording (Patent ID: US20240086796A1)
The patent filed by Nice Ltd. describes a computerized system and method for determining the recording, storing, or deleting of data items received from remotely connected computer systems, particularly interaction recordings associated with agents. This system uses a supervised classification machine learning approach to extract features from data items, predict evaluation likelihood values, calculate storing percentages for remote computing devices, and make decisions on recording, storing, or deleting data items based on these values and percentages.
The computerized method involves personalized screen recording by calculating evaluation likelihood values and recording percentages for interactions with remote computing devices, based on features associated with data items. A recording policy is determined for remote computers based on features and percentages, with data items including agents' data, metrics, and historical elements. The system assembles feature vectors, clusters them, and trains interaction classification models based on the clustering, while also normalizing recording percentages and periodically performing tasks such as calculating likelihood values, recording or deleting data items, determining recording policies, and storing data items. Additionally, the system can receive data items from remote computing devices and is designed for intelligent optimization of storage usage by predicting evaluation likelihood values, deriving storing percentages, and making decisions on storing or deleting data items based on these values and percentages, with the option to determine recording policies based on features and storing percentages through supervised classification machine learning models and comparing predicted likelihood values with observed values.
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