Query Gateway for Context-Retained Autosuggest to AI Chat

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current search engine services lack a framework for integrating context-retention between autosuggest and generative language models, leading to inefficient and inaccurate transitions and navigational disruptions.

Innovation Solution

A query gateway system that facilitates context-retained autosuggest queries from an autosuggest query system to a generative language model system, utilizing AI chat eligibility and reformulation models to enhance computational efficiency and accuracy, and minimize navigational disruptions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If services operate independently without integration framework, then service simplicity is maintained, but context retention between services is lost

Engineering Contradiction:

Improvecontext retentionVSAvoidintegration framework complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent introduces a query gateway as an intermediary component that mediates between the autosuggest service and the generative language model service. This gateway captures context from autosuggest queries and transmits it to the GLM service, enabling context retention without requiring deep integration between the service components themselves.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system is divided into distinct service components (autosuggest service, generative language model service, and query gateway) that operate semi-independently. Each component maintains its own functionality while the query gateway handles context transmission, allowing services to remain simple individually while achieving context retention collectively.

Inventive Principle:

Principle #1Segmentation

2Adaptability or versatility

If features are enhanced in one service, then service capability is improved, but mechanism to leverage advancements in other services is lacking

Engineering Contradiction:

Improveservice feature enhancementVSAvoidcross-service leverage mechanism

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The query gateway serves multiple functions: it acts as a context transmitter, a query reformulator, and a service coordinator. By making the gateway multi-functional, the system can leverage feature enhancements across services without creating separate mechanisms for each function, reducing overall complexity.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system implements feedback loops where the query gateway monitors autosuggest queries, determines their eligibility for GLM processing, and reformulates queries based on context from previous interactions. This feedback mechanism allows services to leverage each other's advancements dynamically.

Inventive Principle:

Principle #23Feedback

3Speed

If direct transition between autosuggest and GLM systems is implemented, then navigation speed is improved, but navigational disruptions and accuracy decrease

Engineering Contradiction:

Improvetransition speedVSAvoidnavigational accuracy

Core Design Contradiction:

SpeedVSReliability

Solution Approach 1:

The query gateway performs preliminary actions by capturing and storing context from autosuggest queries before the user actually transitions to the GLM service. It pre-processes and reformulates queries in advance, so when the transition occurs, the context is already prepared and accuracy is maintained without sacrificing transition speed.

Inventive Principle:

Principle #10Preliminary action

4Ease of operation

If manual query copying and pasting is required between services, then system simplicity is maintained, but user effort and time increase

Engineering Contradiction:

Improveuser effortVSAvoidquery transfer time

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The query gateway implements self-service by automatically capturing queries from the autosuggest service, determining their eligibility for GLM processing, reformulating them with appropriate context, and transmitting them to the GLM service without requiring any manual user intervention. This eliminates the need for users to manually copy and paste queries.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12475178B2Generating narrative query responses utilizing generative language models from search-based autosuggest queries

Publication Date: 2025.11.18 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

This disclosure describes a query gateway system that provides an efficient and flexible framework for providing context-retained autosuggest queries from an autosuggest query system (e.g., a search engine query experience) to a generative language model system (e.g., an AI chat experience). For instance, the query gateway system establishes a framework to leverage the features and services of the autosuggest query system and automatically provides context-retained queries to the generative language model system using separate user interfaces that do not disrupt user navigation or require manual duplicative user input. Additionally, the query gateway system incorporates additional enhancements, including an AI chat eligibility model and a query reformulation model, to improve the computational efficiency and accuracy of the AI chat system.