Dynamic Image Search Reordering via User Interaction Analysis
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Solution Overview
Problem
Existing image search engines face challenges in accurately evaluating the relevance of digital image search results due to the lack of descriptive textual content associated with images, making it difficult for users to efficiently find specific images, especially on handheld devices where reviewing thumbnail images can be cumbersome.
Innovation Solution
The system evaluates user interactions with image search results, such as selections and manipulations of thumbnail images, to reorganize and filter search results, promoting images that align with the user's interests by analyzing keywords associated with the images and adjusting the display dynamically based on user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If image search results are presented as thumbnail images, then more images can be displayed on limited screen space, but it becomes cumbersome for users to review and evaluate image relevance
Solution Approach 1:
The patent implements dynamic reordering of thumbnail images based on real-time user interactions. As users view, select, or spend time on certain images, the system automatically adjusts the display order to promote relevant images and demote less relevant ones, making the review process more efficient without requiring additional screen space
Solution Approach 2:
The system continuously monitors user interactions with thumbnail images (selections, view duration, scrolling behavior) and uses this feedback to dynamically reorganize the display. This feedback loop allows the system to adapt to user preferences in real-time, improving ease of operation while maintaining efficient use of display space
2Device complexity
If digital images are not directly associated with descriptive textual content, then image storage and processing is simpler, but it becomes difficult to evaluate the relevance of image search results
Solution Approach 1:
The system performs self-service by automatically analyzing visual content of images and generating relevant keywords and descriptions without requiring manual annotation. This allows the system to maintain simple image storage while simultaneously achieving accurate relevance evaluation through automated image analysis and keyword extraction
Solution Approach 2:
The patent replaces manual text association with automated computer vision and machine learning algorithms that analyze image content and generate descriptive keywords automatically, eliminating the need for complex manual metadata creation while achieving precise relevance evaluation
3Productivity
If search results are organized using secondary factors such as tags and metadata, then image retrieval is faster, but the accuracy of relevance matching to user intent is reduced
Solution Approach 1:
The patent merges multiple approaches by combining fast secondary factor-based filtering (tags, metadata) with user interaction-based relevance signals. The system uses secondary factors for initial quick retrieval while incorporating real-time user behavior data to refine and reorder results, achieving both speed and accuracy simultaneously
Solution Approach 2:
The system performs preliminary organization of search results using secondary factors like tags and metadata for fast initial retrieval, then immediately follows up with dynamic reordering based on user interactions, ensuring both quick response time and high relevance accuracy
Data Source
AI summary
An image search is executed. The search results are presented as a collection of thumbnail images organized according to an initial ranking. As the user browses the thumbnail images, he/she encounters a thumbnail image of interest. Selecting this thumbnail causes an enlarged preview image to be displayed. The user can manipulate the enlarged preview image with panning and zooming operations, thereby enabling the user to more closely inspect portions of the preview image which attract the user's interest. These interactions with the search results, which include the initial selection of the thumbnail image and the subsequent manipulation of the enlarged preview image, provide insight into the user's interest. Once the user has interacted with a threshold quantity of search results, the collection of thumbnail images is filtered and reorganized to more prominently position those search results that correspond to the user's interest, as gleaned from analyzing the aforementioned user interactions.