Text-to-Image Search Ranking for Ambiguous Keyword Queries
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Solution Overview
Problem
Existing search technologies often require multiple queries and filters due to ambiguous text-based searches, leading to inefficient use of computing resources and inadequate search results, which can be costly in terms of time and resource consumption.
Innovation Solution
A search engine that utilizes a machine learning model to generate an image from text-based keywords, combining text-based and image-based search results to provide a single, ranked set of results, reducing the need for repetitive user inputs and improving search accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple text-based search queries and filters are used to improve search accuracy, then search precision improves, but computing resource consumption increases
Solution Approach 1:
The patent combines text-based search and image-based search into a single unified search operation. The search system generates an image from text keywords using a machine learning model, then simultaneously performs both text-based matching and image-based similarity matching, merging the results into a single ranked list. This eliminates the need for multiple sequential queries and filters, achieving high search accuracy while reducing computing resource consumption.
Solution Approach 2:
The system performs preliminary action by pre-generating an image representation from the text keywords before executing the search. This allows the system to prepare the image-based search query in advance, enabling simultaneous processing of both text and image modalities in a single search operation rather than requiring multiple sequential queries.
2Measurement precision
If multiple search queries are submitted to achieve accurate results, then search precision improves, but time consumption increases
Solution Approach 1:
The patent merges text-based search and image-based search into a single unified operation that returns results simultaneously. By generating an image from text keywords and performing both search modalities in parallel, the system delivers accurate search results in one operation rather than requiring multiple sequential queries, significantly reducing time consumption.
Solution Approach 2:
The system maintains continuity of useful action by performing text-based matching and image-based similarity matching concurrently in a single search operation. Both search modalities operate simultaneously and continuously, delivering comprehensive results without the interruptions and delays associated with multiple sequential queries.
3Measurement precision
If image search is performed to reduce search ambiguity, then search accuracy improves, but device complexity increases
Solution Approach 1:
The patent uses a machine learning model as an intermediary to convert text keywords into an image representation. This intermediary component bridges the text-based and image-based search modalities, allowing the system to leverage image-based search for reduced ambiguity while maintaining a unified, manageable system architecture rather than requiring completely separate search systems.
Solution Approach 2:
The search system achieves multi-functionality by integrating both text-based search and image-based search capabilities into a single unified system. The same search infrastructure handles both modalities, with the machine learning model enabling text-to-image conversion, allowing the system to perform accurate visual searches without requiring separate dedicated image search infrastructure.
Data Source
AI summary
A text-based search optimization via implicit image search augmentation eliminates or reduces the need for providing an image query input, performing multiple search queries, displaying multiple user interfaces, and the like by enabling a search engine to return a single set of search results comprising an aggregated and ranked set of text-based results and a set of image-based results based on one or more text-based keywords of a search query. Initially, a search query comprising one or more text-based keywords is received at a search engine. A machine learning model is utilized to generate an image based on a first portion of the one or more text-based keywords. Image-based results are generated based on the image. Text-based results are generated based on a second portion of the one or more text-based keywords. The image-based results and the text-based results are aggregated and ranked in a single set of search results.