Image Search System Using Generative AI for Semantic Queries
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Solution Overview
Problem
Conventional digital image search systems are limited in their ability to accurately capture the semantic intent of natural language search queries, often returning irrelevant results due to the reliance on keyword matching and the need for an input digital image for image-based searches.
Innovation Solution
The implementation of a search system that uses a second machine learning model to generate prompts for a first generative machine learning model based on natural language search queries, allowing the system to generate digital images that reflect the semantic intent of the queries, which are then used for image-based searches within a digital image repository.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language search queries are processed using keyword matching, then the search system is simple to operate, but the search results fail to capture semantic intent and return irrelevant results
Solution Approach 1:
The patent introduces an intermediary system consisting of a generative AI model and an image generation model that translates natural language queries into synthetic images. This intermediary bridge enables semantic understanding without requiring complex keyword matching, resolving the contradiction between search accuracy and system simplicity
Solution Approach 2:
The patent replaces the mechanical keyword-matching system with an AI-based semantic understanding system. Instead of relying on text-to-text matching, the system uses natural language processing to generate visual representations, substituting traditional search mechanics with intelligent interpretation
2Measurement precision
If image-based search is used to improve search accuracy, then an input digital image is required, but this limits ease of operation for users who only have text queries
Solution Approach 1:
The system performs preliminary action by automatically generating synthetic images from text queries before the search process. This preliminary image generation eliminates the need for users to manually provide input images, maintaining ease of operation while enabling accurate image-based search
Solution Approach 2:
The system serves itself by automatically converting text queries into search-ready images using generative AI. This self-service capability removes the burden from users to create or locate input images, preserving user convenience while achieving accurate search results
3Reliability
If conventional keyword matching is used, then the search system is fast and simple, but it returns irrelevant results that do not match semantic intent
Solution Approach 1:
The patent transitions the search problem from text dimension to visual dimension by generating images from queries. This dimensional change allows the system to leverage visual feature matching and similarity comparison, achieving more reliable results through a different operational dimension rather than increasing text processing complexity
Data Source
AI summary
In implementations of systems for searching for images using generated images, a computing device implements a search system to receive a natural language search query for digital images included in a digital image repository. The search system generates a set of digital images using a machine learning model based on the natural language search query. The machine learning model is trained on training data to generate digital images based on natural language inputs. The search system performs an image-based search for digital images included in the digital image repository using the set of digital images. An indication of the search result is generated for display in a user interface based on performing the image-based search.