Modular Semantic Architecture for Ambiguous Conversational Transactions

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Solution Overview

Problem

Existing AI agents struggle to respond effectively in conversational transactions, particularly in handling ambiguous user requests, changes in entries, and maintaining conversational state without requiring expensive retraining or fine-tuning.

Innovation Solution

A multi-stage modular large language model architecture, including a semantic understander and a semantic matcher, that processes conversational inputs to generate structured representations, maintain conversational state, and map user requests to appropriate actions without the need for extensive retraining.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If a conventional AI agent processes conversational transactions, then it can handle basic user requests, but it struggles with ambiguous requests and maintaining conversational state

Engineering Contradiction:

Improveaccuracy in handling ambiguous requestsVSAvoidcomplexity of processing architecture

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The AI agent is divided into multiple specialized modules: a semantic understander module that processes conversational input and maintains state, a semantic matcher module that handles disambiguation, and an action generator module that produces responses. This segmentation allows each module to specialize in specific tasks, improving reliability for ambiguous requests while keeping individual module complexity manageable.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

A semantic representation layer is introduced as an intermediary between raw conversational input and action generation. This layer creates structured semantic representations that capture meaning and context, serving as a mediator that bridges the gap between understanding ambiguous language and generating appropriate actions, thereby improving accuracy without proportionally increasing overall system complexity.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If specialized AI models are trained for specific domains, then accuracy for domain-specific requests improves, but retraining costs increase when entries change

Engineering Contradiction:

Improveaccuracy for domain-specific requestsVSAvoidcost of retraining models

Core Design Contradiction:

ReliabilityVSEase of manufacture

Solution Approach 1:

The semantic understander and semantic matcher modules are designed as universal components that can process any domain-specific requests without requiring domain-specific training. These modules learn general semantic understanding and matching capabilities that transfer across domains, allowing the system to handle domain-specific requests accurately while avoiding the need for expensive retraining when entries change.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

Instead of retraining model parameters when domain entries change, the system updates its knowledge base and semantic representations with new entries. This parameter-free update approach allows the universal semantic processing modules to adapt to new domains and entries by simply adding new information to their knowledge structures, eliminating retraining costs while maintaining domain-specific accuracy.

Inventive Principle:

Principle #35Parameter changes

3Reliability

If the AI agent maintains detailed conversational state, then response accuracy improves, but computational resources increase

Engineering Contradiction:

Improveconversational response accuracyVSAvoidcomputational energy consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system extracts only the essential semantic elements and state information needed for accurate response generation, rather than maintaining complete conversational histories. The semantic understander extracts key semantic representations and critical state variables, discarding redundant information. This extraction approach maintains response accuracy by preserving essential meaning while reducing computational energy consumption by eliminating unnecessary data retention.

Inventive Principle:

Principle #2Taking out (Extraction)

4Speed

If the system processes incomplete user requests, then response time improves, but accuracy decreases

Engineering Contradiction:

Improveresponse timeVSAvoidrequest completion accuracy

Core Design Contradiction:

SpeedVSReliability

Solution Approach 1:

The semantic understander performs preliminary processing of user requests to identify incomplete information before full response generation. It pre-identifies missing elements and ambiguous references, allowing the system to either complete the request using contextual inference or prompt the user for clarification. This preliminary action enables faster processing of complete requests while maintaining accuracy by catching incomplete requests early in the pipeline.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250078820A1Scalable High-Accuracy Transactional Agents

Publication Date: 2025.03.06 GOOGLE LLC

AI summary

Aspects of the disclosure are directed to a transactional agent for user interactions. The agent can seamlessly respond to user requests in a conversational manner while maintaining the conversational state. The agent can include a multi-stage modular model architecture, including a semantic understander and a semantic matcher. The semantic understander can be configured to understand common conversation conventions and/or patterns to produce a structure representation of a user request. The semantic matcher can be configured to map items and modifiers to product entries for a particular domain.