Dual-Pipeline Utterance Output Construct for Intent Prediction

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing language processing systems struggle to accurately determine user intent from utterances that lack sufficient context, leading to inefficiencies and errors in response generation.

Innovation Solution

A dual-pipeline system that processes utterances through both contextual and non-contextual pipelines, utilizing persistent memory to leverage prior conversations and sentiment scores to enhance intent prediction and response construction.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If only non-contextual pipeline is used for utterance processing, then processing speed is faster and resource consumption is lower, but intent prediction accuracy deteriorates for contextual utterances

Engineering Contradiction:

Improveprocessing speedVSAvoidintent prediction accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system divides utterance processing into two separate pipelines: a non-contextual pipeline for fast processing and a contextual pipeline for accurate intent prediction. The non-contextual pipeline handles basic utterances quickly, while the contextual pipeline processes utterances requiring context analysis, thereby maintaining both speed and accuracy.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically selects which pipeline to use based on the characteristics of the utterance and conversation context. The decision to use the contextual pipeline is made adaptively when context information is available and necessary, allowing the system to optimize between speed and accuracy in real-time.

Inventive Principle:

Principle #15Dynamics

2Measurement precision

If contextual pipeline is always used for utterance processing, then intent prediction accuracy is improved, but resource consumption and processing time increase

Engineering Contradiction:

Improveintent prediction accuracyVSAvoidresource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system segments the processing workload by routing only utterances that require contextual analysis through the contextual pipeline, while other utterances are handled by the more efficient non-contextual pipeline. This selective approach reduces overall resource consumption while maintaining accuracy where needed.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system changes the processing parameters (contextual vs. non-contextual analysis) based on the specific requirements of each utterance. By adjusting the level of processing depth according to the utterance type and conversation state, the system optimizes resource utilization while preserving prediction accuracy.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If contextual information from prior conversations is leveraged, then intent prediction accuracy is improved, but system complexity increases

Engineering Contradiction:

Improveintent prediction accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system separates contextual processing into a dedicated pipeline that handles only the complex task of analyzing prior conversations and current utterances together. This isolates the complexity to a specific module while keeping the rest of the system simple and efficient.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces a contextual pipeline as an intermediary processing layer that bridges the gap between raw utterances and intent prediction. This intermediary handles the complex contextual analysis and transforms it into meaningful intent information, simplifying the overall system architecture.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Productivity

If dual-pipeline processing is implemented, then overall system efficiency is improved by reducing errors, but device complexity increases

Engineering Contradiction:

Improvesystem efficiencyVSAvoiddevice complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system segments processing into two specialized pipelines, each optimized for specific types of utterances. This segmentation reduces errors by ensuring that contextual utterances receive appropriate analysis while maintaining overall system efficiency through specialized processing paths.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The dual-pipeline system creates a universal processing framework that can handle both simple and complex utterances effectively. Each pipeline serves multiple functions: the non-contextual pipeline handles fast processing and simple cases, while the contextual pipeline handles accurate prediction and complex contextual analysis.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20240169993A1Dual-pipeline utterance output construct

Publication Date: 2024.05.23 BANK OF AMERICA CORP

AI summary

A method for utilizing a dual-pipeline utterance output construct to obtain an output corresponding to an utterance of a user is provided. The method includes receiving the utterance, transmitting the utterance through a non-contextual pipeline to determine a first output prediction, transmitting the utterance through a contextual pipeline to determine a second output prediction, transmitting the first output prediction and the second output prediction to a decider to formulate a final prediction of the user's input, constructing a response to the utterance based on the final prediction; and executing the response to the utterance.