Autonomous Message Augmentation Pipelines for Semantic Consistency

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Solution Overview

Problem

Existing language models struggle with model drift and fail to make enterprise-specific predictions due to reliance on domain-level terminologies, leading to gaps in understanding text context and information loss across message handling stages.

Innovation Solution

Implement a machine learning semantic search framework integrated with a domain knowledge index to align data from existing and new protocols, using a coded model output and shared embedding codes to transition between enterprise, domain-level, and user-level terminologies.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If language models are integrated with domain-level terminologies to make domain-specific predictions, then domain-level understanding is improved, but enterprise-specific prediction capability deteriorates

Engineering Contradiction:

Improvedomain-level understandingVSAvoidenterprise-specific prediction capability

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The patent segments the terminology system into multiple hierarchical levels: domain-level terminologies (common to all enterprises) and enterprise-specific terminologies (custom to each enterprise). This segmentation allows the language model to access both general domain knowledge and specific enterprise vocabularies, resolving the contradiction between domain-level understanding and enterprise-specific prediction capability

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent creates a universal terminology framework that serves multiple functions simultaneously: it provides domain-level common understanding through shared terminologies while enabling enterprise-specific predictions through customized terminology layers. This multi-functional terminology system allows the same language model to operate effectively at both domain and enterprise levels

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Ease of operation

If traditional language models are used for message interpretation, then basic natural language processing is achieved, but semantic consistency across message handling stages deteriorates

Engineering Contradiction:

Improvenatural language processingVSAvoidsemantic consistency

Core Design Contradiction:

Ease of operationVSStability of the object's composition

Solution Approach 1:

The patent introduces terminology mappings as an intermediary layer between natural language input and computer interpretation. These mappings serve as mediators that translate user-friendly natural language into consistent computer-interpretable concepts across all message handling stages, preserving semantic consistency while maintaining ease of natural language processing

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent implements feedback mechanisms where terminology mappings are continuously refined based on their performance in maintaining semantic consistency across message handling stages. This feedback loop ensures that the terminology system adapts to maintain stability and consistency while processing natural language messages

Inventive Principle:

Principle #23Feedback

3Productivity

If data is transformed across multiple message handling stages, then message processing is achieved, but information loss increases

Engineering Contradiction:

Improvemessage processingVSAvoidinformation loss

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The patent establishes continuous terminology mappings that persist across all message handling stages from initial interpretation through processing to final response generation. This continuity ensures that semantic information is preserved throughout the entire message processing pipeline, preventing information loss while maintaining high processing productivity

Inventive Principle:

Principle #20Continuity of useful action

Data Source

PatentUS20250322266A1Autonomous and semantically consistent message augmentation pipelines

Publication Date: 2025.10.16 OPTUM INC

AI summary

Various embodiments of the present disclosure provide automated message processing techniques that improve traditional communication systems, such as those that interface between a user and a plurality of requesting entities. The techniques include identifying a message that (i) is directed to a user inbox, (ii) is associated with an automated task category of a plurality of different automated task categories, and (iii) comprises message text data reflective of the automated task category. The techniques include generating a coded model output (i) based on the message text data and a domain knowledge index and (ii) that comprises a semantic intent classification and a shared embedding code and identifying the automated task category based on the semantic intent classification and the shared embedding code. The techniques include generating, using the domain knowledge index, a predicted response for the message based on the automated task category and modifying message with the predicted response.