Machine-Learning Product Titles and Descriptions for Search Relevance

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Solution Overview

Problem

Existing online retail systems face challenges in generating product titles and descriptions that effectively match user search criteria, leading to overwhelming search results that fail to convey relevant product information.

Innovation Solution

The implementation of a method that collects user browsing activity data and product feature data, which are then analyzed using machine learning models to automatically generate unique and meaningful product titles and descriptions tailored to individual user search criteria.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If template-based formatting is used for product titles and descriptions, then the structure is standardized and easy to manage, but the titles and descriptions cannot be customized to match different user search criteria and needs

Engineering Contradiction:

Improvecustomization to user search criteriaVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent transforms static template-based titles and descriptions into dynamic, user-adaptive content. The system analyzes user search queries and browsing behavior in real-time, then dynamically generates customized titles and descriptions that match individual user needs while maintaining structural organization through the learning engine.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The learning engine automatically generates customized product titles and descriptions without requiring manual intervention for each user query. The system self-adapts by learning from user browsing patterns and search behavior, continuously improving its ability to generate relevant content autonomously.

Inventive Principle:

Principle #25Self-service

2Loss of information

If fixed template-based titles and descriptions are used, then the content is consistent across all users, but the titles and descriptions do not include effective words or phrases that assist users when browsing for a product

Engineering Contradiction:

Improverelevance of product informationVSAvoidtime to develop titles and descriptions

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The system performs preliminary analysis of user search queries and browsing behavior before generating product titles and descriptions. The learning engine pre-processes user intent data and product feature data to create optimized content that is ready for immediate display, eliminating the need for time-consuming manual content creation.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system continuously learns from user interactions with product listings, analyzing which titles and descriptions generate engagement and conversions. This feedback loop allows the learning engine to refine and improve its content generation over time, ensuring increasing relevance without additional manual effort.

Inventive Principle:

Principle #23Feedback

3Productivity

If manual content creation by technical writers is required, then the titles and descriptions can be crafted with care, but the process becomes rigid, cumbersome and time consuming

Engineering Contradiction:

Improvecontent generation speedVSAvoidease of content creation

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The patent replaces the mechanical process of manual content creation by technical writers with an automated learning engine that uses machine learning algorithms. This substitution dramatically increases content generation speed while maintaining quality through intelligent analysis of user behavior and product features.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The learning engine performs content generation autonomously without requiring manual intervention from technical writers. The system self-manages the entire process from analyzing user search criteria to generating optimized titles and descriptions, making content creation effortless and highly productive.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12346958B2Method and article of manufacture for automated generation of titles and descriptions for electronic commerce products

Publication Date: 2025.07.01 DELL PROD LP

AI summary

A method comprises collecting browsing activity data of at least one user in connection with at least one electronic commerce item, and collecting feature data of the at least one electronic commerce item. In the method, the browsing activity data and the feature data are analyzed using one or more machine learning models. At least one of a title and a description for the at least one electronic commerce item is generated based on the analysis, and are displayed on an interface for viewing by the at least one user.