ARTIFICIAL INTELLIGENCE (AI) MODEL TRAINED USING CLUSTERING AND REINFORCEMENT LEARNING FOR DATA DE-IDENTIFICATION ENGINE
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United States of America Patent
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Issued Date -
Sep 12, 2024
app pub date -
Jun 15, 2023
filing date -
Mar 6, 2023
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Abstract
The disclosed system and method provide an artificial intelligence (AI) model trained using clustering and reinforcement learning. Data in a dataset can be loaded for de-identification, along with data scope answers. The data can be audited, and once audited, the audited data and the data scope answers can be provided to a strategy recommendation engine including the trained AI model. The engine can determine a cluster corresponding to the dataset and assesses strategies for data de-identification based on the determined cluster. The strategies can be ranked and provided as output, providing the ability to better de-identify the dataset by indicating which techniques will be the most effective. Additionally, the system and method can automatically implement a top-ranked strategy satisfying certain criteria as a determined optimal approach for data de-identification. Clustering and reinforcement learning may efficiently and automatically glean information from unlabeled data. Feedback-based retraining may improve performance further.
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