Nice had nine patents in digitalization during Q2 2024. Nice Ltd filed patents in Q2 2024 for computerized methods related to fraud detection in financial transactions, generating classification ML models in a cloud-based environment, quantifying monotony in repetitive tasks across computing devices, and automatically validating stored information related to contact sessions using blockchain technology. These innovations aim to improve efficiency, accuracy, and security in various aspects of computerized systems. 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 had no grants in digitalization as a theme in Q2 2024.
Recent Patents
Application: Computerized-method and computerized-system for identifying fraud transactions in transactions classified as legit transactions by a classification machine learning model (Patent ID: US20240193608A1)
The patent filed by Nice Ltd. describes a computerized method for identifying fraud transactions within a financial system that have been classified as legitimate by a Machine Learning (ML) model. The method involves training ML models on datasets of fraud-labeled and legit-labeled transactions to mark transactions as 'similar' or 'novel'. These trained models are then deployed in a computerized environment along with a classification ML model to identify fraud transactions that were classified as legitimate. The trained classification ML model is based on preconfigured transactions from the data store to mark transactions as either 'legit' or 'fraud'.
Furthermore, the method includes sending transactions classified as 'legit' by the ML model to a trained ML legit model for processing, and those marked as 'novel' are then sent to a trained ML fraud model. Transactions marked as 'novel' by the fraud model are identified as potential unknown fraud transactions, while those marked as 'similar' are identified as potential missed fraud. The method also involves calculating novelty and similarity scores for transactions marked as 'novel' and 'similar' respectively, with the highest scoring transactions being transmitted to a user for further investigation. The legit and fraud models are trained using an unsupervised algorithm, such as a one-class Support Vector Machine (SVM), to classify transactions based on similarity to the provided dataset.
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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.
