F. Hoffmann-La Roche grant share with cybersecurity as a theme is 50% in Q3 2023. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Privacy-preserving computing on subject data used to develop artificial intelligence tools (Patent ID: US20230259654A1)
The present disclosure describes techniques for privacy-preserving computing to protect the privacy of subjects while using their data for secondary purposes such as training and deploying artificial intelligence tools. The method involves a local cloud server receiving subject data from a computing device associated with the subject. The server then performs de-identification, anonymization, or both on the subject data to generate processed subject data. The processed subject data is stored in a processed data store accessible to the local cloud server. A batch of data, including the processed subject data, is sent to a remote cloud server. The remote server provides a production model to the local server, which includes parameters derived from the processed subject data. Subsequent data regarding a second subject is received from a second computing device associated with the second subject. The subsequent data is inputted into the production model to analyze and generate an inference or prediction. The inference or prediction is then sent to a computing device for use in various operations.
The method also includes additional features such as the local cloud server being physically located in the same geographic region as the subject, which could be the same country. If the subject data is health care data with individually identifiable health information, the de-identification and anonymization operations are performed based on a set of data regulations shared by the same geographic region. The processed subject data is sent to the remote cloud server as part of a batch of data unless a request for deletion of the processed subject data is received. The method also involves storing the subject data in a raw data store before performing the de-identification or anonymization operation, and deleting the subject data from the raw data store upon receiving a deletion request from the remote cloud server.
The inference or prediction generated by the production model can be related to various aspects such as diagnosis, treatment, disease detection, biomarker identification, cost reduction, image analysis, marketing, administrative tasks, medical procedures, and more. The method also includes sending subsequent batches of data, processed subsequent data, and processed output data to the remote cloud server. The local cloud server can receive response data generated in response to the inference or prediction and perform de-identification or anonymization on the response data before sending it to the remote cloud server. The remote cloud server receives processed subject data from the local cloud server, associates it with a versioned dataset, determines an expiration date for the dataset, and stores it in a version data store. The remote server then trains a production model using the versioned dataset and sends it back to the local cloud server for analyzing subsequent data and generating inferences or predictions.
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