Microsoft had 58 patents in regtech during Q4 2023. Microsoft Corp’s patent involves a technology that allows for the creation of a joint data set by combining first-party and third-party data sets in a protected environment. This joint data set is used to tune a first-party trained model, resulting in a third-party tuned model. The tuned model parameters are then sent to an aggregator node to receive a globally tuned version of the model, which is applied to a second third-party data set to produce a scored third-party data set for distribution through a content distribution service. GlobalData’s report on Microsoft gives a 360-degreee view of the company including its patenting strategy. Buy the report here.
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Microsoft grant share with regtech as a theme is 39% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Decentralized cross-node learning for audience propensity prediction (Patent ID: US20230351247A1)
The patent filed by Microsoft Corp. describes a method that involves receiving a first-party trained model and data set, along with third-party data, into a protected environment. This data is then processed in a data clean room to create a joint data set, tune the model, and generate a globally tuned version. The tuned model is applied to additional third-party data sets and provided to a content distribution service. The process includes semantic alignment, mapping, and logical isolation of nodes to ensure privacy and accuracy in the data processing.
Furthermore, the method involves an aggregator node receiving model parameter data from multiple third-party nodes, applying this data to the first-party trained model to create a globally tuned version, and providing it back to the nodes. The nodes process third-party data sets in data clean rooms to learn model parameter data and create tuned models. The patent emphasizes the opt-in mechanism for data aggregation, where sources can choose to participate or not, affecting the application of the tuned model. This approach ensures efficient collaboration between first-party and third-party systems while maintaining data privacy and accuracy through controlled data processing environments.
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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.
