Method for Automatically Classifying Data Items
AssigneeVaronis Systems, Inc.
InventorsAmit Cohen, John Eugene Neystadt, Miri Brusilovsky
Life of this patent
Estimating the term…
Abstract
Broadly speaking, the present techniques provide an automatic way of classifying data items within an environment (e.g. a business, workplace, organisation, etc.). This is advantageous over existing techniques which require manual classification of data items, which is time consuming in environments where hundreds of new data items may be generated or modified in a day or week. The present techniques use the content of a data item together with metadata associated with the data item to classify the data item, and thereby label the data item.
Claims20
A computer-implemented method for autonomously classifying data items within an environment, the method comprising:
- obtaining, from a data source within the environment, a data item that is newly created or newly modified;
- generating at least one embedding vector for the data item, wherein the at least one embedding vector represents content of the data item;
- obtaining metadata for the data item;
- generating at least one classification label for the data item together with an associated confidence level for each classification label, by processing the generated at least one embedding vector and obtained metadata using a trained machine learning, ML, model;
- determining whether the associated confidence level is greater than or equal to a pre-defined confidence level;
- and
- applying the generated at least one classification label to the data item when the generated at least one classification label has an associated confidence level that is greater than or equal to the pre-defined confidence level.
A computer-implemented method for training a machine learning, ML, model to autonomously classify uncategorised data items within an environment, the method comprising:
- obtaining a training dataset comprising a plurality of labelled data items from data sources within the environment, and metadata for each labelled data item;
- generating at least one embedding vector for each labelled data item, wherein the at least one embedding vector represents content of the data item;
- and
- training the ML model to:
- cluster the plurality of labelled data items into a plurality of clusters, using the at least one embedding vector of each labelled data item and metadata for each labelled data item, wherein each cluster contains a subset of the plurality of labelled data items that are more similar to each other than to the labelled data items in other clusters.
A computer-implemented method for autonomously controlling actions performed with respect to a data item within an environment, the method comprising:
- obtaining, from a data source within the environment, a data item that is newly created or newly modified;
- generating at least one embedding vector for the data item, wherein the at least one embedding vector represents content of the data item;
- obtaining metadata for the data item;
- generating at least one classification label for the data item together with an associated confidence level for each classification label, by processing the generated at least one embedding vector and obtained metadata using a trained machine learning, ML, model;
- determining whether the generated at least one classification label has an associated confidence level that is greater than or equal to a pre-defined confidence level;
- applying the generated at least one classification label to the data item when the generated at least one classification label has an associated confidence level that is greater than or equal to the pre-defined confidence level;
- and
- controlling an action performed with respect to the data item based on the applied generated at least one classification label.
Dependencies are inferred from the claim text.