USPTO Patent Grant: Machine Learning Source Code Generation via Holochain Network

ChangeBridge: Patent Grants - AI & Computing (G06N)

Published March 24th, 2026

Detected March 24th, 2026

Summary

The USPTO has granted patent US12585437B2 to Bank of America Corporation for a system and method for decentralized machine learning source code generation via a holochain network. The patent covers a framework that uses machine learning to generate code segments based on predefined criteria and user input.

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What changed

The United States Patent and Trademark Office (USPTO) has granted patent US12585437B2 to Bank of America Corporation. This patent covers a "System and method for a machine learning source code generation via a holochain network," detailing a decentralized framework for generating code segments. The invention utilizes trained machine learning models to determine subsequent code segments based on user-provided criteria and characteristics of new code segments, assigning scores to ensure alignment with predetermined code requirements.

This patent grant is primarily an intellectual property matter and does not impose direct compliance obligations on regulated entities. However, companies involved in AI development, software engineering, or blockchain technology may find the patented methods relevant to their own innovation or intellectual property strategies. The filing date for this application was October 11, 2023, and the patent is set to be granted on March 24, 2026.

Source document (simplified)

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System and method for a machine learning source code generation via a holochain network

Grant US12585437B2 Kind: B2 Mar 24, 2026

Assignee

BANK OF AMERICA CORPORATION

Inventors

Shailendra Singh, Ankit Dholakiya, Arup Francis

Abstract

Systems, computer program products, and methods are described herein for a decentralized machine learning source code generation framework via a holochain network. The present invention is configured to receive a predetermined code criteria, receive a new code segment from an editor on an endpoint device, retrieve characteristics of the new code segment, determine, using a trained machine learning model, at least one subsequent code segment, based on at least the characteristics of the new code segment, determine, using an evaluation engine, at least one score of the subsequent code segment, wherein the at least one score is associated with the predetermined code criteria, and transmit to the editor of the endpoint device for displaying a predetermined number of the at least one subsequent code segments above a predetermined threshold of the at least one score.