Distilled Generative Response Engine for Fast Search Summaries

Solution Overview

Problem

Generative response engines are slow when summarizing search results, failing to meet user expectations of fast Internet search response times, and lack personalization due to lacking user-specific information.

Innovation Solution

A distilled generative response engine is developed, trained on distillation data generated using a language model program, which is optimized for speed and tailored to specific search queries through a language model program with conditional transformations, allowing rapid iteration and targeted improvements.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If a generative response engine is used to summarize search results, then the quality of response is improved, but the response time becomes too slow

Engineering Contradiction:

Improveresponse qualityVSAvoidresponse time

Core Design Contradiction:

ReliabilityVSSpeed

Solution Approach 1:

The system segments the response generation process into multiple stages: an initial prompt phase, followed by conditional transformation phases. Each phase processes specific aspects of the query, allowing parallel and incremental computation that reduces overall response time while maintaining quality.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary actions by generating an initial response and then applying conditional transformations to refine it. This preliminary response provides a baseline that can be quickly improved through targeted transformations rather than generating the complete response from scratch.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If multiple prompt and response cycles are used to improve response relevance, then the accuracy is improved, but the time consumption increases

Engineering Contradiction:

Improveresponse relevanceVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary actions by generating an initial response and then applying conditional transformations to refine it. This preliminary response provides a baseline that can be quickly improved through targeted transformations rather than generating the complete response from scratch.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adjusts the number and type of transformations applied based on the initial response quality and query characteristics. This dynamic approach allows the system to invest more time only when necessary, reducing average time consumption while maintaining high relevance.

Inventive Principle:

Principle #15Dynamics

3Measurement precision

If a language model program with conditional transformations is implemented, then the response relevance is improved, but the device complexity increases

Engineering Contradiction:

Improveresponse relevanceVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system segments the response generation process into multiple stages: an initial prompt phase, followed by conditional transformation phases. Each phase processes specific aspects of the query, allowing parallel and incremental computation that reduces overall response time while maintaining quality.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces an intermediary language model program that acts as a mediator between the user query and the generative response engine. This intermediary applies conditional transformations to refine the response, improving relevance while isolating the complexity from the core response generation system.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12619603B2Generating a distilled generative response engine trained on distillation data generated with a language model program

Publication Date: 2026.05.05 OPENAI OPCO LLC

AI summary

The present technology provides a distilled generative response engine for generating responses to Internet search queries. The present technology utilizes a language model program that is made up of a collection of conditional dependencies that branch into many different sequences of prompts that are configured to transform answers to different types of search queries. This design of the language model program facilitates rapid iteration to improve responses. The language model program is used to generate distillation data that is used to train the distilled generative response engine.