AI Comment Processing Using Decision-Factor Labels for Product Reviews

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Solution Overview

Problem

User comment content for commodities is often brief and lacks detail, making it less effective for other users, and existing systems fail to provide meaningful insights due to mechanical and stereotyped formats.

Innovation Solution

Analyze user comment content by commodity category to determine decision-making factor labels, revise base comment content using these labels, and generate suggested comment text content with AI assistance, optimizing picture/video content for better expression and relevance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If users are provided with reference labels for structured comment input, then comment quality may be improved, but user effort and time consumption increase significantly

Engineering Contradiction:

Improvecomment qualityVSAvoiduser time consumption

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system pre-analyzes user comment content and generates decision-making factor labels before the user needs to write comments. This preliminary processing creates a ready-to-use label library that users can directly select, eliminating the need for users to contemplate and express complex opinions manually, thus reducing time consumption while maintaining comment quality

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system automatically performs semantic recognition and label generation on user comments without requiring manual user intervention. The AI model autonomously analyzes comment content, extracts key factors, and presents relevant labels to users, allowing the system to serve itself in the label generation process rather than relying on user effort

Inventive Principle:

Principle #25Self-service

2Manufacturing precision

If users write detailed comment content manually, then comment quality improves, but user effort and time consumption increase

Engineering Contradiction:

Improvecomment detail qualityVSAvoiduser operation ease

Core Design Contradiction:

Manufacturing precisionVSEase of operation

Solution Approach 1:

The system introduces an AI model as an intermediary between the user's brief comment input and the final detailed comment output. The AI model acts as a mediator that automatically expands and refines user input into comprehensive decision-making factor analysis, eliminating the need for users to manually write detailed comments while maintaining high comment quality

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system replaces the mechanical process of manual detailed comment writing with an automated AI-based semantic recognition and label generation system. Instead of users mechanically typing out detailed opinions, the AI model automatically processes user input and generates structured decision-making factor labels, significantly improving ease of operation

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Device complexity

If uniform format is used for all comment labels, then system structure is simplified, but comment content becomes mechanical and stereotyped

Engineering Contradiction:

Improvesystem structure complexityVSAvoidcomment content quality

Core Design Contradiction:

Device complexityVSManufacturing precision

Solution Approach 1:

The system applies different formatting and presentation styles to different decision-making factor labels based on their specific characteristics and the commodity category. Instead of a uniform format, each label is adapted to its local context, allowing the system to maintain structural organization while avoiding mechanical and stereotyped content presentation

Inventive Principle:

Principle #3Local quality

4Manufacturing precision

If comprehensive comment guidelines are provided to users, then comment quality improves, but user understanding and implementation difficulty increase

Engineering Contradiction:

Improvecomment qualityVSAvoiduser understanding difficulty

Core Design Contradiction:

Manufacturing precisionVSDifficulty of detecting and measuring

Solution Approach 1:

The system extracts the essential decision-making factors from comprehensive comment guidelines and presents them as discrete, selectable labels to users. Instead of requiring users to understand and implement complex guidelines, the system extracts key elements into simple label choices, reducing user understanding difficulty while maintaining comment quality

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS20260087239A1Method for processing commodity comment content and electronic device

Publication Date: 2026.03.26 HANGZHOU ALIBABA INT INTERNET IND CO LTD

AI summary

Embodiments of this application disclose a method for processing commodity comment content and an electronic device. The method includes: analyzing, by using a commodity category as a unit, user comment content corresponding to multiple commodities, to determine a correspondence between a commodity category and a decision-making factor label; obtaining, after a target user initiates a request for filling user comment content for a target commodity object, base comment content inputted by the target user; and revising the base comment content according to the decision-making factor label corresponding to a commodity category to which the target commodity belongs, to generate suggested comment text content, so as to publish user comment content for the target commodity according to the suggested comment text content. According to the embodiments of this application, quality of user comment content of a commodity in a system can be improved.