Predictive data analysis techniques using graph-based code recommendation machine learning models
Summary
The USPTO granted Patent US12596936B2 to Optum Technology, Inc. on April 7, 2026. The patent covers graph-based machine learning methods for predictive code recommendation, involving compressed forward-adjusted temporal distance measures and inferred edge weight values to identify related codes from training predictive code occurrences.
What changed
USPTO issued Patent US12596936B2 to Optum Technology, Inc. for predictive code recommendation using graph-based machine learning models. The patent describes methods for processing input predictive codes through a graph-based model to generate related codes, with edge weights updated based on temporal distance measures for co-occurrence patterns in training data.
This patent grant does not create compliance obligations but establishes intellectual property rights that may affect competitive dynamics in healthcare technology and machine learning. Organizations developing similar code recommendation systems should review potential licensing needs or design-around considerations.
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Apr 7, 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.
Predictive data analysis techniques using graph-based code recommendation machine learning models
Grant US12596936B2 Kind: B2 Apr 07, 2026
Assignee
Optum Technology, Inc.
Inventors
Nilav Baran Ghosh, Abhilash Sivva, Srikanth B. Adibhatla
Abstract
Solutions for more efficient and effective predictive code recommendation are disclosed. In one example, a method includes identifying a graph-based code recommendation machine learning model, wherein each inferred edge weight value of the graph-based code recommendation machine learning model is updated based at least in part on each compressed forward-adjusted temporal distance measure for an observed co-occurrence of any observed co-occurrences of a predictive code pair for the inferred edge weight value within one or more temporally-proximate occurrence subsets determined based at least in part on a plurality of training predictive code occurrences; processing the input predictive code using the graph-based code recommendation machine learning model to generate one or more related codes of the plurality of predictive codes for the input predictive code; and performing one or more prediction-based actions based at least in part on the one or more related codes.
CPC Classifications
G06N 5/04 G06N 20/00 G06F 8/10 G06F 8/20 G06F 8/35
Filing Date
2021-03-18
Application No.
17205704
Claims
20
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