Cloud Software Group had 19 patents in artificial intelligence during Q4 2023. The Cloud Software Group Inc patents filed in Q4 2023 cover a range of technologies including machine performance classification, audio quality verification in web conferences, language detection in virtual computing sessions, multi-factor authentication using biometric data from wearables, and proactive prediction of virtual service session failures. These patents aim to improve user experience, security, and efficiency in various computing environments. GlobalData’s report on Cloud Software Group gives a 360-degreee view of the company including its patenting strategy. Buy the report here.
Cloud Software Group grant share with artificial intelligence as a theme is 47% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Determining comprehensive health scores for machines hosting virtual desktops based on performance parameters (Patent ID: US20230403224A1)
The patent filed by Cloud Software Group Inc. describes systems and methods for classifying a machine based on its performance. The method involves identifying attributes of the machine and session, determining a score using weighted attributes, calculating the probability of failure, generating a performance score, and classifying the machine into different performance levels based on the score. The system includes a device with processors and memory to perform these functions, including updating weights, identifying trends in session failure, training models, and providing output based on the classification.
The system further allows for the modification of scores based on factors indicating differences in performance, maintaining weights through time windows, and updating models with new attributes and scores. The method also involves training models using datasets with user information, application data, and session status. Additionally, the system can provide output based on the classification of the machine into different performance levels. The non-transitory computer-readable medium stores program instructions for implementing the method, including identifying attributes, calculating scores, determining failure probabilities, generating performance scores, and classifying machines based on performance levels.
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