Dual-Encoder Image Ranking for Informative Query Results
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Solution Overview
Problem
Traditional learning systems lack the ability to effectively incorporate visual information into automated text distractors for multiple-choice questions, limiting the educational impact and memory recall for users.
Innovation Solution
A computer-implemented method using a dual encoder model with a ranking loss function to map text and image inputs, determining relevant images and their informativeness based on similarity scores, and providing them as automated text distractors for flashcards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If visual information is incorporated into automated text distractors, then learning efficiency and memory retention are improved, but system complexity increases
Solution Approach 1:
The patent introduces a dual encoder model as an intermediary component that bridges text and image data. The text encoder processes question text while the image encoder processes candidate images, and their combined representations enable semantic matching. This intermediary architecture allows the system to handle visual information integration without directly complexifying the core learning algorithm.
Solution Approach 2:
The system segments the image processing task by first filtering images based on text similarity scores generated by the dual encoder, then selectively processing only the top-k relevant images through additional analysis modules. This segmentation reduces the computational burden on the entire system while maintaining the benefit of visual information for learning efficiency.
2Productivity
If multiple relevant images are provided for each flashcard, then user engagement and memory retention improve, but information processing time increases
Solution Approach 1:
The system applies partial action by providing only the top-k most relevant images (where k is a small constant) rather than all possible relevant images. The dual encoder model ranks images by similarity score, and the system selectively presents only the highest-ranked images, achieving sufficient memory retention benefits while avoiding excessive processing time.
Solution Approach 2:
The dual encoder model performs preliminary ranking of images based on text similarity before the actual learning process. This preliminary action filters and orders the image candidates in advance, so that during flashcard review, the system only needs to present the pre-ranked top images rather than processing all images in real-time, reducing information processing time.
3Ease of manufacture
If automated text distractors use definition side text, then implementation simplicity is maintained, but visual learning effectiveness is reduced
Solution Approach 1:
The patent merges text-based definition side text with image-based visual information into a unified flashcard structure. The dual encoder model processes both text and image inputs simultaneously, creating a hybrid representation that combines the simplicity of text definitions with the effectiveness of visual learning. This merging allows the system to maintain implementation simplicity while significantly improving visual learning effectiveness.
Data Source
AI summary
In one embodiment, a computer-implemented method can use a server computer to obtain from a client computer a text input in a query from a user and access in digital data storage coupled to the server computer a plurality of digital images. The computer-implemented method can train a deep learning model to determine a first embedding for the text input and a second embedding of each of the plurality of images. The computer-implemented method can identify one or more relevant images based on the respective similarity of the first embedding to the second embedding. The computer-implemented method can determine image informativeness and confidence scores for information terms of each of the one or more relevant images. The computer-implemented method can transmit to the client computer in response to obtaining the text input, instructions for presenting a user interface comprising the one or more relevant images and the confidence scores.