SAP had 40 patents in cybersecurity during Q1 2024. The patents filed by SAP SE in Q1 2024 involve techniques for evaluating machine learning on remote datasets while preserving confidentiality, implementing secure chaos experiments using certificates, obscuring directional data for privacy, optimizing connection pools for tenants, and automating authorization role processes for users. These innovations aim to enhance data security, privacy, and efficiency in various computing environments. GlobalData’s report on SAP gives a 360-degree view of the company including its patenting strategy. Buy the report here.

SAP grant share with cybersecurity as a theme is 70% in Q1 2024. Grant share is based on the ratio of number of grants to total number of patents.

Recent Patents

Application: Evaluating machine learning on remote datasets using confidentiality-preserving evaluation data (Patent ID: US20240095397A1)

The patent filed by SAP SE involves a system and method for evaluating machine learning on remote datasets while preserving data confidentiality. The method includes connecting to a remote customer database, determining data quality and quantity, generating feature data, training baseline and machine learning models, generating aggregate evaluation data, and sharing it with a software provider. The process also involves executing the machine learning pipeline after successful completion of a build process, accessing customer data through a data compatibility layer, and standardizing and tokenizing customer data for evaluation.

Furthermore, the system described in the patent includes computers and computer-readable instructions for performing the method steps. It involves connecting to the remote customer database, generating feature data, training models, generating aggregate evaluation data, and sharing it with the software provider. The system also executes the machine learning pipeline based on an execution queue, after a build process, and includes features like identifying and removing database objects created during the process. Overall, the patent outlines a comprehensive approach to evaluating machine learning on remote datasets while maintaining data confidentiality and ensuring efficient execution of the machine learning pipeline.

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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.