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AssigneeIntertrust Technologies Corporation
InventorsDavid Maher, Sami Ben Jamaa, Talal Shamoon, Michael Manente
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Embodiments of the disclosed systems and methods provide for techniques for managing generative AI services that allow for generative AI models to better use confidential, proprietary, sensitive, and/or otherwise managed data while maintaining the security of such data. Various embodiments may provide for generative AI model training based on variable and/or otherwise tuned reliance on input training data sets and queries may leverage this differential training. Differential analysis techniques may be further employed to identify confidential data included in model outputs. In further embodiments, query and/or output labeling may be used in connection with access rights management techniques to manage access to proprietary information that may be included in model outputs. Embodiments of the disclosed systems and methods may be used in a variety of applications, use cases, and/or contexts, including in personalized medicine and differential diagnosis applications.
A method for managing a generative artificial intelligence model query performed by a generative artificial intelligence service executing on a system comprising a processor and a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, cause the system to perform the method, the method comprising:
Dependencies are inferred from the claim text.