Language Model Item Replacement Explanation System

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Solution Overview

Problem

Existing online systems face challenges in identifying suitable replacement items for unavailable products, often resulting in poor user experiences due to unsuitable substitutions, which can lead to transaction delays and user dissatisfaction.

Innovation Solution

An online system utilizes a machine learning-based language model to evaluate and explain the suitability of replacement items by processing images and text data, providing real-time explanations to users when a replacement item is deemed unsuitable, thereby improving the selection process.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If automated replacement item selection is used, then productivity is improved, but measurement precision deteriorates

Engineering Contradiction:

Improvetransaction completion speedVSAvoidreplacement suitability accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system implements feedback by evaluating replacement items using quality measures and generating explanations for poor replacements. The language model provides feedback on why a replacement is unsuitable, allowing the system to learn and improve future replacement selections while maintaining automated operation.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent replaces traditional rule-based or manual replacement selection mechanisms with a machine learning language model. This substitution enables the system to understand nuanced product characteristics and user preferences, improving measurement precision of replacement suitability while maintaining automated productivity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Device complexity

If simple replacement rules are used, then device complexity is reduced, but reliability deteriorates

Engineering Contradiction:

Improvesystem structure simplicityVSAvoidreplacement quality

Core Design Contradiction:

Device complexityVSReliability

Solution Approach 1:

The language model acts as an intermediary between simple replacement rules and reliable replacement selection. It processes product information, images, and user preferences to generate quality evaluations and explanations, bridging the gap between system simplicity and replacement reliability without requiring complex rule sets.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If detailed evaluation of replacement items is performed, then measurement precision is improved, but loss of time increases

Engineering Contradiction:

Improvereplacement quality assessmentVSAvoidevaluation time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs partial evaluation by focusing on key quality measures and generating explanations only when replacements are poor. This selective approach provides sufficient measurement precision for critical decisions while avoiding excessive evaluation time for obviously suitable replacements, balancing detail with efficiency.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20240362696A1Generating item replacements using machine learning based language models

Publication Date: 2024.10.31 MAPLEBEAR INC

AI summary

An online system uses a machine learning based language model, for example, a large language model (LLM) to identify replacement items for an item that may not be available at a store. The online system receives a request for an item and determines that the requested item is not available. The online system identifies a replacement item. If the online system determines that the replacement item has a replacement score below a threshold value indicating a low quality of replacement for the requested item, it uses a machine learning based language model, for example, a large language model to generate an explanation for why the replacement item has a replacement score below the threshold value. The online system sends the explanation to a client device.