Query Result Ranking With Cross-Modal Asset Retrieval
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Solution Overview
Problem
Existing systems face challenges in accurately interpreting nuanced language and user intent in text-based queries to provide relevant digital assets, such as images, that seamlessly integrate with text-based results, while ensuring high quality and efficient retrieval.
Innovation Solution
A method utilizing two machine learning models to receive queries, generate text-based answers, and identify relevant digital assets, which are then organically incorporated into the results, using techniques like joint embedding spaces and cross-modal retrieval to ensure relevance and visual appeal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search algorithms are used to interpret queries, then the system is simple and fast, but the accuracy of understanding nuanced language and user intent deteriorates
Solution Approach 1:
The patent introduces machine learning models as intermediary components between the query input and the digital asset selection process. These ML models act as mediators that bridge the gap between simple keyword matching and complex semantic understanding, enabling accurate interpretation of nuanced language while maintaining system modularity.
Solution Approach 2:
The patent replaces traditional mechanical search algorithms with machine learning-based semantic analysis systems. This substitution enables the system to understand user intent, contextual meaning, and nuanced language patterns that conventional keyword-based approaches cannot capture, significantly improving measurement precision in query interpretation.
2Ease of operation
If digital images are added to text-based results, then user experience is enhanced, but the complexity of integrating and placing images deteriorates
Solution Approach 1:
The patent segments the result presentation into distinct modular components: text-based results and digital asset components. Each component is independently processed and optimized, with clear separation between content generation and visual enhancement. This segmentation simplifies the integration process by allowing independent optimization of each component while maintaining overall coherence.
Solution Approach 2:
The patent adds a visual dimension to traditional text-based search results by integrating digital assets. This dimensional expansion transforms the results from purely textual to multi-modal (text + visual), enhancing user experience by providing complementary visual information without fundamentally altering the underlying text processing architecture.
3Adaptability or versatility
If multiple digital assets are retrieved for each query, then relevance coverage is improved, but the retrieval time and system performance deteriorate
Solution Approach 1:
The patent implements a strategy of retrieving a superset of potentially relevant digital assets and then filtering/ranking them based on relevance criteria. Rather than attempting to retrieve only the perfect matches (which would require excessive computation), the system retrieves multiple candidates and applies efficient filtering, achieving high relevance coverage while maintaining acceptable retrieval speeds.
Solution Approach 2:
The patent performs preliminary indexing and categorization of digital assets in advance, organizing them by topic, relevance, and other metadata attributes. This preliminary action enables rapid retrieval and filtering during actual query processing, allowing the system to quickly identify relevant assets from pre-organized collections without performing exhaustive searches in real-time.
Data Source
AI summary
Disclosed are techniques for providing relevant results for queries. A method can be implemented by a server computing device, and includes (1) receiving a query from a client computing device, (2) providing the query to a first machine learning (ML) model to produce a text answer to the query, (3) providing, to a second ML model, (i) the query, and (ii) the text answer, to obtain one or more digital assets that correspond to the query and the text answer, (4) generating results based on (i) the query, (ii) the text answer, and (iii) the one or more digital assets, and (5) causing the results to be output by way of a user interface on the client computing device. Other embodiments include generating text answers that include a plurality of text segments, where at least one image is obtained for each text segment of the plurality of text segments.