LLM Typeahead Evaluation System for Relevance Filtering

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Solution Overview

Problem

Existing search systems face challenges in providing high-quality and personalized typeahead suggestions based on partial search queries, leading to irrelevant suggestions that distract users and waste computing resources.

Innovation Solution

A system utilizing a generative language model to evaluate typeahead suggestions by simulating user intent through context data, including search history, profile data, and intent prediction, to rank suggestions as high-quality or low-quality.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional typeahead suggestion systems are used, then the system is simple and fast, but the suggestions are irrelevant and waste computing resources

Engineering Contradiction:

Improverelevance of suggestionsVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent introduces an intermediary evaluation system that acts as a mediator between the typeahead suggestion generator and the user. This evaluation system uses machine learning models to assess the relevance of suggestions by simulating user intent, filtering out low-quality suggestions before they reach the user, thus improving reliability without requiring complete system redesign

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent applies preliminary action by evaluating and ranking typeahead suggestions before presenting them to users. The system pre-computes relevance scores using context data and machine learning models, so that only high-quality suggestions are displayed. This preliminary filtering prevents waste of computing resources on generating and displaying irrelevant suggestions

Inventive Principle:

Principle #10Preliminary action

2Reliability

If more context data is processed to improve suggestion quality, then relevance improves, but computing resource consumption increases

Engineering Contradiction:

Improvequality of suggestionsVSAvoidcomputing resource consumption

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The patent applies partial action by selectively processing only the most relevant context data for evaluation. Rather than analyzing all possible user data, the system focuses on key contextual factors that most strongly predict user intent, such as recent search history and profile information, achieving high suggestion quality with reduced computing resource consumption

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The patent changes parameters by dynamically adjusting the depth and scope of context data processing based on the evaluation needs. The machine learning models use configurable parameters to control how much context data is analyzed, allowing the system to optimize between suggestion quality and resource consumption by adjusting these parameters based on operational conditions

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250209266A1Evaluating typeahead suggestions using a large language model

Publication Date: 2025.06.26 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

Embodiments of the disclosed technologies are capable of evaluating typeahead suggestions using a partial search query. The embodiments describe obtaining a typeahead suggestion responsive to a partial search query. The embodiments further describe creating a prompt based on the typeahead suggestion. The embodiments further describe causing a large language model (LLM) to evaluate the typeahead suggestion based on the prompt. The embodiments further describe providing, to a computing device, an evaluation output by the LLM in response to the prompt.