Jpmorgan Chase Bank, N.a.

Santosh Chikoti

Jeffrey Kessler

Saurabh Gupta

Ita B Lamont

Saurabh Tiwari

Ashwarya Gupta

Tuhin Bhura

Shashanka Narayan

Mohit Arora

Deepak Jayadas

Sai Gumma

Vikas Kundalpady

Murali Yaddanapudi

In some aspects, the techniques described herein relate to a method including: receiving, at a query platform, a query and a document identifier; determining, based on the document identifier, a document-type parameter; generating, based on metadata related to the document-type parameter, an optimized query; generating a query vector embedding from the optimized query; executing a lexical search on a document pool, wherein the lexical search is based on the optimized query, and wherein the lexical search returns a first document set; executing a contextual search on a vector database, wherein the contextual search is based on the query vector embedding and returns a second document set; and generating a targeted document set from the first document set and the second document set.

Systems and methods for generating a targeted document set for input to a generative model

A method for auto discovery of sensitive data may include: (1) receiving, at data enrichment computer program in a metadata processing pipeline, raw metadata from a plurality of different data sources; (2) enriching, by the data enrichment computer program, the raw metadata; (3) converting, by the data enrichment computer program, the raw metadata and the enhanced raw metadata into a sentence structure; (4) predicting, by a category prediction computer program in the metadata processing pipeline, a predicted category for the sentence structure; (5) identifying, by a sensitive data mapping computer program, a sensitive data category that is mapped to the predicted category based on a policy mapping rule; (6) determining, by the sensitive data mapping computer program, a risk classification rating for the predicted category; and (7) tagging, by the sensitive data mapping computer program, the data source associated with the metadata based on the risk classification rating.

Systems and methods for auto discovery of sensitive data in applications or databases using metadata via machine learning techniques

Systems and methods for generalized structured data discovery utilizing contextual metadata disambiguation via machine learning are disclosed. A method may include receiving physical application metadata for an attribute, a database object, or a database; receiving reference data including tokens and their associated abbreviations and acronyms; parsing the physical application metadata into application tokens including known application tokens and unknown application tokens; identifying unknown application tokens by comparing the parsed application tokens to a corpus; performing probabilistic parsing on the unknown application tokens using the reference data resulting in token phrases; identifying monosemous tokens in the token phrases using a look-up dictionary; replacing the monosemous tokens with their expansions from the look-up dictionary; and outputting a mapping of the physical application metadata to enhanced physical application metadata that includes an expression for the physical application metadata comprising the expansions for monosemous tokens in a supported language.

Systems and methods for generalized structured data discovery utilizing contextual metadata disambiguation via machine learning techniques

A method for managing user stories in software development via artificial intelligence is disclosed. The method includes aggregating, via an application programming interface, raw data from a software development framework according to a predetermined schedule, the raw data corresponding to user stories from a plurality of users in a natural language format; ingesting the aggregated raw data to generate structured data sets; generating a language model by using a neural network and the structured data sets, the neural network including a transformer component; training, by using the structured data sets, the language model based on predetermined criterions; tuning the trained language model for tasks by adjusting parameters; and exposing, via a communication interface, the tuned language model.

Method and system for managing user stories via artificial intelligence

A method for generalized structured data discovery may include (1) receiving physical application metadata from data sources for an attribute, a database object, or a database; (2) receiving reference data comprising a plurality of tokens and their associated abbreviations/acronyms; (3) parsing the physical application metadata into a application tokens comprising known and unknown application tokens; (4) identifying unknown application tokens by comparing the parsed application tokens to a corpus; (5) performing probabilistic parsing on the unknown application tokens using the reference data; (6) performing bi-directional encoding to expand the polysemous tokens to relevant expressions using the reference data; (7) applying language tokens to the relevant expressions in the expanded polysemous tokens to disambiguate the relevant expressions; and (8) outputting a mapping of the physical application metadata to enhanced physical application metadata, wherein the enhanced physical application metadata comprises an expression for the physical application metadata in a supported language.

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Monroe Township, NJ, United States of America

Santosh Chikoti