Cross-Modal Product Search With Catalog Enrichment Feedback
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Solution Overview
Problem
Existing product search systems face inaccuracies due to sparse and poor-quality catalog data, especially in domains like spare parts, where traditional text and image matching techniques struggle with limited data availability and user incentives to use multiple search modalities are lacking, leading to inefficient and inaccurate search results.
Innovation Solution
A multi-modal search system that leverages cross-modal techniques, such as Image-To-Text and Text-To-Image matching, enriches the catalog with user query data from successful search sessions, using confirmations to improve the representation of parts, and integrates with customer service platforms for expert validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional text and image matching techniques are used for product search, then the search system is simple to implement, but the search accuracy deteriorates due to sparse and poor-quality catalog data
Solution Approach 1:
The patent combines multiple search modalities (text search, image search, and cross-modal search) into a unified search system. The system integrates text-to-text matching, image-to-image matching, and cross-modal (text-to-image and image-to-text) matching to improve search accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The patent introduces cross-modal search capabilities that operate in additional dimensional spaces. By enabling searches across different modalities (text and image) and implementing progressive enrichment that adds new dimensions of catalog representation, the system improves accuracy without linearly increasing complexity.
2Reliability
If catalog data is enriched with user query data from successful search sessions, then the catalog representation quality improves over time, but the system complexity increases due to data collection and processing requirements
Solution Approach 1:
The patent implements a feedback mechanism where user confirmations from successful search sessions are captured and used to enrich the catalog data. The system collects confirmation signals, processes them through a data processing pipeline, and uses the enriched data to improve future search results, creating a continuous improvement loop.
Solution Approach 2:
The patent performs preliminary processing of user query data during successful search sessions. The system pre-processes and validates confirmation data, structures it appropriately, and prepares it for catalog enrichment before it is actually needed, reducing the complexity of real-time processing.
3Loss of information
If multiple search modalities are incentivized for use, then novel data is captured to improve catalog representation, but the ease of operation decreases due to increased user effort requirements
Solution Approach 1:
The patent implements a self-service mechanism where the system automatically captures and processes data from user search behaviors and confirmations without requiring explicit user actions. The confirmation signals are automatically detected, recorded, and used for catalog enrichment, eliminating the need for users to manually contribute data.
Solution Approach 2:
The system provides immediate feedback to users when their search queries successfully return results, automatically capturing this confirmation signal. This feedback loop incentivizes users to continue using the system while the captured data automatically improves catalog representation without additional user effort.
4Measurement precision
If cross-modal search techniques are implemented, then search accuracy improves by matching different modality types, but the computational resources required increase
Solution Approach 1:
The patent implements progressive enrichment that processes and enriches catalog data in stages rather than all at once. The system performs partial enrichment iterations, processing subsets of data at different times, which reduces the peak computational energy required while still achieving comprehensive catalog improvement over time.
Data Source
AI summary
A products search methodology can include receiving a first search query associated with searching products, the first search query having a first set of modalities, generating matches based on a cross-modal search using a machine learning model trained to search for matches in a products catalog that match the first search query, wherein matches in the products catalog have a second set of modalities, receiving an indication that one or more of the matches from the products catalog is a confirmed match to the first search query, responsive to receiving the indication, extracting embeddings, based on a neural network, of at least one modality of the first set of modalities of the first search query, and updating the one or more matches from the products catalog with at least one of the extracted embeddings and the first search query.