International Business Machines Corporation

Krishna Teja Rekapalli

Sushain Pandit

Aly Megahed

Ilyas Mohamed Iyoob

Theodora H Cheng

Alexander Leblanc Greer

A computer-based method, system, and computer program product for automatically identifying significant events for food traceability. The method may comprise receiving a series of events from an agriculture supply chain entity, automatically determining, at a machine learning model of an event analysis module, one or more events in the series having a significance for food traceability greater than a threshold, and automatically reporting the one or more events to a ledger.

Determining significant events within an agribusiness system

Computer systems, methods and program products for automating pseudonymization of personal identifying information (PII) using machine learning, metadata, and crowdsourcing patterns to identify and replace PII. Machine learning models are trained for classifying known column names or key names for processing, using metadata. Column or key names are classified to be unprocessed, anonymized or pseudonymized by a pseudonymizer without revealing PII or scrubbing data into a useless format. A library of crowdsourced patterns are utilized for matching PII to data values within column or key names and PII is mapped to replacement methods. Feedback from user annotations retrains the algorithms to improve classification accuracy and Deep Learning algorithms automate the identification of PII using regular expression generation to concisely articulate how pseudonymizers search for PII patterns within a data set. PII replacement is mapped consistently across entire data packages and the crowdsourced pattern library is updated with generated regular expressions.

Cognitive data pseudonymization

A method for automatically adjusting vehicle media may include obtaining personal data of a vehicle operator and obtaining contextual data about the vehicle operating. The method may further include generating a distraction-level value that represents a degree to which the vehicle operator may be distracted from the media. The method may further include generating a distraction threshold value. The method may further include determining that the distraction-level value exceeds the distraction threshold value and adjusting the media in response.

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Austin, TX, United States of America

Krishna Teja Rekapalli