Semantic Concept Synthesis Method Patented by Primal Fusion
Summary
USPTO granted Patent US12596741B2 to Primal Fusion Inc. on April 7, 2026. The patent covers methods for semantic concept definition and relationship synthesis using machine learning techniques. Inventors include Peter Sweeney and Alexander David Black, with the patent application filed on June 14, 2024.
What changed
USPTO issued Patent US12596741B2 to Primal Fusion Inc. for a method assessing input coherence using synthesized concepts in a data processing system. The patent includes claims covering semantic processing protocols that derive virtual concept definitions forming tree-structure graphs, with coherence measured using confidence gradients based on proximity and co-occurrence metrics.
This patent grant establishes intellectual property rights for Primal Fusion Inc. in semantic concept synthesis technology. Organizations developing AI-based semantic processing, knowledge graph systems, or natural language understanding tools should review these 20 claims to ensure their products do not infringe on this intellectual property.
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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.
Systems and methods for semantic concept definition and semantic concept relationship synthesis utilizing existing domain definitions
Grant US12596741B2 Kind: B2 Apr 07, 2026
Assignee
Primal Fusion Inc.
Inventors
Peter Sweeney, Alexander David Black
Abstract
A method for assessing the coherence of an input with a data processing system using synthesized concepts is provided. The method includes obtaining an active concept definition from the input of a cognitive agent, extracting real concept definitions composed of a set of attributes from an analyzed domain, matching the active concept definition to the extracted definitions, deriving virtual concept definitions from the real concept definitions using a semantic processing protocol such that the derived virtual concept definitions form a tree-structure graph of concepts and concept relationships, and measuring the attribute set coherence of the virtual concept definitions using a confidence gradient. The confidence gradient is based on at least one metric of relative proximity and co-occurrence. The method further includes assessing the probability of coherence, of the input with the data processing system, based on the measure of coherence within the confidence gradient.
CPC Classifications
G06N 20/00 G06N 20/10 G06N 20/20 G06N 5/00 G06N 5/01 G06N 5/02 G06N 5/022 G06F 16/36 G06F 40/30 G06F 40/40 G06F 40/42
Filing Date
2024-06-14
Application No.
18743449
Claims
20
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