USPTO Patent Grant: Autonomous Multifactor Generative AI Framework
Summary
The USPTO has granted a patent (US12586119B1) to U.S. Bank National Association for an "Autonomous multifactor generative artificial intelligence framework." The patent covers methods for detecting process triggers, identifying entity data, generating predictive asset outputs using machine learning, and creating multifactor contextualized asset representations.
What changed
The United States Patent and Trademark Office (USPTO) has issued patent US12586119B1 to U.S. Bank National Association. This patent grants exclusive rights to an "Autonomous multifactor generative artificial intelligence framework." The patented technology involves detecting process triggers, collecting entity-specific data, utilizing machine learning models to predict assets, and dynamically contextualizing these assets based on multifactor analysis before transmitting them.
This patent grant signifies a new intellectual property asset for U.S. Bank National Association in the domain of generative AI and predictive analytics. While this is a patent grant and not a regulatory rule imposing obligations on other entities, it highlights the increasing patentability of AI-driven business methods. Companies operating in the AI and financial services sectors should be aware of this granted patent as it may impact their freedom to operate or require licensing if their technologies infringe upon the patented claims.
Source document (simplified)
Autonomous multifactor generative artificial intelligence framework
Grant US12586119B1 Kind: B1 Mar 24, 2026
Assignee
U.S. Bank National Association
Inventors
Shubham Agarwal, Joseph Bakke
Abstract
Various embodiments are directed to apparatuses, methods, computer-readable media, computer program products, and systems related to detecting a process trigger that identifies a target entity; identifying, entity data associated with the target entity; generating, using a machine learning based prediction model, a predictive asset output based at least in part on the entity data, wherein the predictive asset output comprises at least one predicted asset; generating, using a dynamic contextualization model, a multifactor contextualized asset representation for the at least one predicted asset based at least in part on the at least one predicted asset and the entity data; and transmitting the multifactor contextualized asset representation to one or more computing devices via one or more communication channels.
CPC Classifications
G06Q 30/06-08
Filing Date
2025-03-18
Application No.
19083031
Claims
20
Named provisions
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