US Patent Application 17738851. METHOD AND SYSTEM TO EXTRACT DATA DEPENDENCIES FOR MACHINE LEARNING MODELS simplified abstract

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METHOD AND SYSTEM TO EXTRACT DATA DEPENDENCIES FOR MACHINE LEARNING MODELS

Organization Name

Microsoft Technology Licensing, LLC


Inventor(s)

Laurent Boue of Petah Tikva (IL)

Kiran Rama of Bangalore (IN)

Vijay Srinivas Agneeswaran of Bangalore (IN)

Chepuri Shri Krishna of Bangalore (IN)

Swarnim Narayan of Bangalore (IN)

METHOD AND SYSTEM TO EXTRACT DATA DEPENDENCIES FOR MACHINE LEARNING MODELS - A simplified explanation of the abstract

This abstract first appeared for US patent application 17738851 titled 'METHOD AND SYSTEM TO EXTRACT DATA DEPENDENCIES FOR MACHINE LEARNING MODELS

Simplified Explanation

The patent application describes techniques for detecting anomalies in machine learning models using sparse judgmental samples.

  • Techniques involve analyzing the textual representation of a machine learning model and generating a set of tokens using lexical analysis.
  • The tokens are then used to create an abstract syntax tree (AST) that represents the structure of the machine learning model.
  • The AST helps identify data dependencies within the model, indicating which data sources are used by the model.
  • Potential issues with the data sources are detected, and alert notifications are sent based on the identified data dependencies.


Original Abstract Submitted

Example aspects include techniques for anomaly detection via sparse judgmental samples. These techniques may include generating, via lexical analysis, a plurality of tokens from a textual representation of a machine learning (ML) model and generating, via a parser, based on the plurality of tokens, an abstract syntax tree (AST) corresponding to the ML model. In addition, the techniques may include identifying a data dependency of the ML model based on an AST node within the AST, the AST node corresponding to a data source and the data dependency indicating the ML model depends on the data source. Further, the techniques may include detecting a potential issue associated with the data source, and transmitting, based on the data dependency, an alert notification in response to the potential issue.

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