Image Generation Model for Text-to-Visual Search
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Solution Overview
Problem
Users face difficulties in searching for items like clothing, art, and music without examples, as freeform text queries yield mixed and unaligned results, and image generation systems struggle with user intent due to the lack of intuitive prompts.
Innovation Solution
A computing system that processes user inputs to generate model-generated images, which are then used as queries to search engines, providing a more directed and tailored search by leveraging machine-learned models for image generation and dataset creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If freeform text queries are used to search for items, then the search process is simple to initiate, but the search results become mixed and unaligned, reducing search precision
Solution Approach 1:
The patent introduces an image generation model as an intermediary between the user's text query and the search engine. The model generates images based on the text query, and these generated images are then used as input for the search engine, creating a mediating representation that bridges the gap between simple text input and precise visual search results
Solution Approach 2:
Instead of directly searching for text queries or uploading existing images, the patent inverts the traditional search approach by first generating images from text descriptions and then using those generated images as the search input. This reversal allows users to search using simple text while achieving image-based search precision
2Measurement precision
If image queries are used to search for items, then the search results become more tailored and precise, but the user may not have access to an image of what they are looking for
Solution Approach 1:
The system performs self-service by automatically generating the image input that would normally need to be provided by the user. The image generation model creates images from text descriptions without requiring the user to have or find an existing image, making the system self-sufficient in creating its own search input
3Adaptability or versatility
If current image generation systems with prompt input boxes are used, then users can generate images, but the process becomes non-intuitive and time-consuming
Solution Approach 1:
The system performs preliminary action by pre-generating multiple images based on the text query before the user needs to select or refine them. This allows users to see multiple options immediately and reduces the need for iterative prompt refinement, saving time in the overall search process
Solution Approach 2:
The system takes excessive action by generating multiple images beyond what a single prompt would typically produce. This provides users with more options to choose from and reduces the need for repeated generation attempts, making the process more efficient despite the additional computational effort
4Measurement precision
If users refine text queries iteratively to improve search results, then search precision may improve, but the process becomes time-intensive
Solution Approach 1:
The patent inverts the traditional iterative refinement process by first generating images from the initial text query and then using those images to guide further search. This eliminates the need for iterative text refinement and allows users to quickly visualize results and make adjustments if needed
Data Source
AI summary
Systems and methods for searching using machine-learned model-generated outputs can provide a user with a medium for generating a theoretical dataset that can then be matched to a real world example. The systems and methods can include selecting a plurality of terms, which can be utilized to generate a prompt input that can be processed by a dataset generation model to generate a plurality of model-generated datasets. A selection can then be received that selects a particular model-generated database to utilize to query a database.