Universal Machine Learning Pipeline Execution System and Method
Summary
USPTO published patent application US20260099305A1 titled 'Systems and Methods for Universal Machine Learning Pipeline Execution,' filed December 10, 2025. The application discloses methods for automated machine learning model development, including parsing configuration files, generating model code, creating ML pipelines, monitoring execution, and producing trained models with performance data. Inventors: Rameshchandra Bhaskar Ketharaju, Anjeet Kumar, and Shuvam Sengupta. CPC classifications include G06F 8/35, G06F 8/31, G06F 11/3476, and G06N 20/00.
What changed
USPTO published patent application US20260099305A1 disclosing systems and methods for universal machine learning pipeline execution. The application covers configuration-driven model code generation, ML pipeline assembly combining model code with data processing engines, and runtime monitoring for model performance data. Key claims address automated model development workflows from configuration parsing through trained model output.
For technology companies and ML developers, this patent application indicates competitive intellectual property activity in automated ML tooling. Organizations developing similar pipeline execution technologies should review the claims upon grant to assess potential licensing needs or design-around considerations. The publication does not yet create enforceable rights but signals the direction of proprietary innovation in ML infrastructure.
What to do next
- Monitor for patent grant status
- Review application claims for potential licensing opportunities
Archived snapshot
Apr 15, 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.
SYSTEMS AND METHODS FOR UNIVERSAL MACHINE LEARNING PIPELINE EXECUTION
Application US20260099305A1 Kind: A1 Apr 09, 2026
Inventors
Rameshchandra Bhaskar Ketharaju, Anjeet Kumar, Shuvam Sengupta
Abstract
Systems, apparatuses, methods, and computer program products are disclosed for automated model development. An example method includes parsing, by configuration circuitry, a configuration file, and generating, by an execution engine and based on the parsed configuration file, model code for training and testing a machine learning model. The example method further includes generating, by the execution engine, a machine learning pipeline, wherein the machine learning pipeline comprises the model code and a data processing engine, and instantiating, by a monitoring driver, a monitoring engine to monitor the machine learning pipeline. The example method further includes causing execution, by the execution engine, of the machine learning pipeline, and during execution of the machine learning pipeline, generating model performance data by the monitoring engine. The example method further includes receiving, by from the execution engine, model output data, wherein the model output data comprises a trained model and model performance data.
CPC Classifications
G06F 8/35 G06F 8/31 G06F 11/3476 G06N 20/00
Filing Date
2025-12-10
Application No.
19414751
Named provisions
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