Supply Chain Network Prescriptions Based on Artificial Intelligence Techniques
Summary
USPTO granted US Patent 12602630B1 to Coupa Software Incorporated on April 14, 2026. The patent covers AI-driven supply chain network analysis methods using machine learning models and heuristic algorithms for descriptive, diagnostic, and prescriptive analytics. The invention creates scenario prescriptions specifying changes to supply chain nodes and generates visualizations of updated network models.
What changed
USPTO granted a new patent to Coupa Software for AI-based supply chain network prescription technology. The patent covers methods for executing machine learning model inferences on supply chain data, identifying cost drivers at the path level using feature scores, and generating scenario prescriptions with visualizations of updated network models. The patent includes 16 claims and was filed on December 27, 2023.
Affected parties should monitor this patent if they develop or offer competing AI-powered supply chain optimization solutions. While patent grants do not impose direct compliance obligations, they establish exclusive rights that could affect competitive dynamics in the supply chain software market. Technology companies offering supply chain analytics solutions may need to assess whether their products overlap with the protected claims.
What to do next
- Monitor for potential patent infringement by competitors using similar AI supply chain techniques
- Review patent claims to understand protected technology scope
Archived snapshot
Apr 14, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
Supply chain network prescriptions based on artificial intelligence techniques
Grant US12602630B1 Kind: B1 Apr 14, 2026
Assignee
Coupa Software Incorporated
Inventors
Sushant J. Khopkar, Nicole Ogden, Zheng Ouyang, Aishwarya Sivasurya, Siddharth Chakravarthy, Prasanna Ragavan, Varshini Ramaraj, Yun Xu
Abstract
Embodiments provides a method executed by a server computer executing a supply chain network analysis application of a supply chain model. The method includes receiving supply chain network data associated with a supply chain network having one or more supply chain nodes. The method then programmatically executes inferences on the supply chain network data using one or more machine learning models and one or more heuristic algorithms to implement descriptive analytics, diagnostic analytics, and prescriptive analytics to create and store one or more scenario prescriptions that specify one or more changes to the one or more supply chain nodes. The method performs steps for programmatically executing inferences on the supply chain network data that includes extracting one or more data features at a path level, the one or more data features indicating descriptive insights related to one or more paths in the supply chain network. The method includes a step of identifying, using one or more path-level machine learning models, one or more cost drivers of the one or more paths in the supply chain network by computing a feature score of each of the one or more data features at the path level. The method includes creating and storing, using the one or more path-level machine learning models and the feature score of each of the one or more data features, one or more digital representations of the one or more scenario prescriptions. The method includes generating and displaying one or more visualizations of one or more updated network models that implements the one or more scenario prescriptions.
CPC Classifications
G06Q 30/0201 G06Q 10/06315
Filing Date
2023-12-27
Application No.
18397029
Claims
16
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