Model-Generated Image Queries for More Accurate Search Results
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face difficulties in searching for items like clothing, art, or music without an example to provide to a search engine, leading to mixed, unaligned, and off-topic search results, especially when relying on freeform text or Boolean strings, and image generation systems can be non-intuitive and time-consuming.
Innovation Solution
A computing system that leverages machine-learned models to generate images based on user inputs, allowing users to select model-generated images as queries to refine search results, using interactive user interfaces with category and descriptor elements to enhance prompt generation and search precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If freeform text or Boolean strings are provided as search queries, then users can perform searches without examples, but the search results become mixed, unaligned, and off-topic
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 inputs to the search engine, creating a mediating transformation that improves search result alignment and accuracy while maintaining ease of use
Solution Approach 2:
The patent replaces the traditional text-based search mechanism with an image-based search mechanism. Instead of directly processing text queries through search algorithms, the system substitutes this with a two-stage process: generating images from text using a machine learning model, then using those images for search, thereby improving result precision
2Measurement precision
If image queries are used to provide more tailored results, then search accuracy improves, but users may not have access to an image of what they are looking for
Solution Approach 1:
The patent performs preliminary action by automatically generating the image that would normally need to be provided by the user. The image generation model creates the search image based on the user's text description, eliminating the need for the user to manually obtain or create an image before searching
Solution Approach 2:
The system provides self-service by automatically generating the search image from the user's text query without requiring external input from the user. The machine learning model serves itself by converting the text query into the appropriate image format needed for accurate search results
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 patent applies partial action by using only the essential elements needed for image generation - the user's text query - without requiring the full, complex prompt engineering process. The system generates sufficient image quality for search purposes without demanding excessive user input or iteration
Solution Approach 2:
The image generation model serves multiple functions: it translates user queries into visual representations, acts as a search query processor, and provides intuitive interaction. This multi-functionality eliminates the need for separate prompt input interfaces and reduces overall search time
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.