Image-Based Search Query Generation Using Neural Networks
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Solution Overview
Problem
Users face challenges in identifying specific items within electronic marketplaces using image recognition, as conventional approaches often return irrelevant results due to the inability to discern features beyond the object category, leading to a tedious search process.
Innovation Solution
A machine learning-based approach utilizing neural networks to analyze images, generating search strings and refinements that include key words and categories, allowing for more precise searches within electronic marketplaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image recognition is used to identify objects, then object category can be determined, but specific item features cannot be identified leading to irrelevant search results
Solution Approach 1:
The patent segments the image analysis process into multiple specialized neural networks: one for object category identification and another for extracting specific visual features (color, pattern, texture, shape). This segmentation allows each network to specialize in extracting particular types of information, thereby preserving comprehensive item features while maintaining accurate category identification.
Solution Approach 2:
The patent transitions from traditional single-dimension object recognition to multi-dimensional analysis by extracting features across different visual dimensions (color, pattern, texture, shape) simultaneously. This dimensional expansion enables the system to capture comprehensive item characteristics that go beyond basic category classification.
2Adaptability or versatility
If more search results are displayed to users, then users have more options, but users must navigate through many irrelevant results increasing search time
Solution Approach 1:
The system implements feedback by using extracted visual features to continuously refine and adjust search queries. The neural network analyzes image features and automatically generates optimized search strings that incorporate specific item characteristics, providing feedback that improves search result relevance and reduces the time users need to spend filtering through irrelevant results.
Solution Approach 2:
The patent performs preliminary action by automatically extracting visual features and generating optimized search queries before the user even initiates a search. This pre-processing of image information and automatic query formulation eliminates the need for users to manually filter through irrelevant results, significantly reducing search time while maintaining comprehensive result coverage.
3Measurement precision
If users manually apply filters to search results, then search precision can be improved, but user effort and complexity increase
Solution Approach 1:
The patent implements self-service by enabling the system to automatically extract visual features from images and generate optimized search queries without requiring user intervention. The neural network autonomously identifies item characteristics and formulates precise search strings, eliminating the need for users to manually apply filters while maintaining high search precision.
Solution Approach 2:
The patent replaces the mechanical process of manual filter application with an automated neural network-based system that extracts visual features and generates search queries automatically. This substitution eliminates the need for users to manually interact with filter interfaces, significantly improving ease of operation while maintaining or enhancing search precision.
Data Source
AI summary
Approaches include using a machine learning-based approach to generating search strings and refinements based on a specific item represented in an image. For example, a classifier that is trained on descriptions of images can be provided. An image that includes a representation of an item of interest is obtained. The image is analyzed using the classifier algorithm to determine a first term representing a visual characteristic of the image. Then, the image is analyzed again to determine a second term representing another visual characteristic of the image based at least in part on the first term. Additional terms can be determined to generate a description of the image, including characteristics of the item of interest. Based on the determined characteristics of the item of interest, a search query and one or more refinements can be generated.