C3.ai. has filed a patent for a computer-implemented method that uses machine learning to detect network interruptions and attribute them to specific components or nodes. The method involves mapping a network with dynamic islands and predicting local events at individual component and island levels using a disaggregation model. GlobalData’s report on C3.ai gives a 360-degree view of the company including its patenting strategy. Buy the report here.

According to GlobalData’s company profile on C3.ai, AI for workflow management was a key innovation area identified from patents. C3.ai's grant share as of January 2024 was 22%. Grant share is based on the ratio of number of grants to total number of patents.

Network interruption detection using machine learning

Source: United States Patent and Trademark Office (USPTO). Credit: C3.ai Inc

A recently filed patent (Publication Number: US20240022483A1) describes a computer-implemented event assignment method that involves mapping a network consisting of multiple islands that can dynamically change through splitting and merging. These islands are made up of individual components, and the method includes predicting, detecting, and localizing local events at both the individual component level and the island level using a disaggregation model.

The patent claims to offer a novel approach to event assignment in complex networks by considering the dynamic nature of the islands within the network. By utilizing a disaggregation model, the method aims to provide more accurate predictions and detections of local events, enhancing the overall efficiency and effectiveness of event assignment processes. This innovation could have significant implications for various industries that rely on network monitoring and event management systems, potentially improving the ability to respond to and address events in a timely and precise manner.

To know more about GlobalData’s detailed insights on C3.ai, buy the report here.

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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.