Morgan Stanley Services Group Inc.

Vijay Kumar Butte

Mehak Mehta

Sanjit Vijay Mehta

Mayank Jain

Rahul Suresh

Ashish Kumar Singh

Abhijit Anant Naik

Meet Singh

Satya Verma

Sourabh Kumar Janghel

Aditya Ramesh

Himani Garg

Alex Gavrilov

Lokin Makkena

A method of implementing a regulation and directive to code platform comprises automatically pulling updates of a new regulation document from policy regulation sources, chunking the regulation document into smaller segments, generating embeddings of the segments of the using an LLM, classifying and extracting metadata from the new regulation document based on the classification, storing the embeddings of the segments with metadata tags in a retrieval automated generation (RAG) database, storing the extracted metadata extracted in an enterprise policy repository, extracting actionable items from the segments into a code form based on a prompt using an LLM, extracting template artifacts from the segments based on another prompt using an LLM, storing the extracted actionable items and template artifacts in the enterprise policy repository, and generating a central policy user interface through which users can access, review and update all regulation and directive information including the actionable items and template artifacts.

Regulations and directives to code platform

A system and method may create and manage a code repository structure and related files for a ML system such as a NN by executing code to create a version control management platform template commit including a pipeline folder comprising deploy files; a configuration folder comprising key-value pairs describing variables used as input to the neural network; a notebooks folder; a steps folder including ML or NN code; a tests folder comprising test scripts; and a utils folder; and executing code to create a tag associated with a pull request and alter version control management platform data such that the tag is displayed in conjunction with data describing the branch associated with the pull request. Executing code to create a tag may include searching a commit history for a unique commit number associated with a pull request, and based on this search creating a tag.

System and method for machine learning development

Systems and methods for translating natural language to SQL on a custom enterprise data warehouse powered by Generative AI. With an embodiment of the present invention, a natural language question may be converted to a meaningful and accurate database query, e.g., SQL query, relevant to tables existing in an enterprise data warehouse. An embodiment of the present invention is directed to a comprehensive approach of transforming a natural language query to a focused SQL query using domain specific data models across firmwide metadata systems and data systems. In response to a user query, an embodiment of the present invention performs metadata analysis, targeted data retrieval and then SQL generation. An embodiment of the present invention may apply data warehousing standards and guidelines followed in the enterprise and provide a plug-and-play type architecture and solution that is scalable to large warehousing and other systems.

Natural language to SQL on custom enterprise data warehouse powered by generative artificial intelligence

Growing community of inventors

Princeton Junction, NJ, United States of America

Vijay Kumar Butte