Display Interface Viewer Affinity Content Selection
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Solution Overview
Problem
Current display devices lack the ability to effectively customize content selection based on viewer identification, failing to present images that are most likely to be of interest to the viewer, especially in a dynamic and social context where subject affinity and relationships play a crucial role.
Innovation Solution
The system identifies viewer faces, matches them with corresponding user accounts, computes affinity scores using social distance and co-occurrence data, and sorts images based on these scores to select and display the most relevant content, incorporating features like zone weighting and face size ratios to refine the presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If images are selected based on simple rotation or fixed algorithms, then device complexity is reduced, but content personalization and viewer engagement deteriorate
Solution Approach 1:
The system pre-computes affinity scores for all subject-face pairs and stores them in lookup tables before image display. When selecting images, the system simply retrieves these pre-computed scores and sums them, rather than performing complex real-time analysis. This preliminary computation approach enables personalized content selection without requiring complex runtime processing.
Solution Approach 2:
The image selection process is divided into independent components: face detection, subject identification, affinity score retrieval, and score aggregation. Each component operates independently and can be implemented separately, allowing the system to achieve complex personalization through simple, modular operations rather than a monolithic complex algorithm.
2Loss of information
If subject information is displayed for all subjects in upcoming images, then information completeness is improved, but display clutter and user attention dilution worsen
Solution Approach 1:
The status bar displays subject information with varying prominence based on local affinity scores. Subjects with higher affinity to the viewer are displayed with greater emphasis (such as larger icons or different colors), while subjects with lower affinity are displayed with less prominence. This allows the interface to provide complete information while guiding user attention to the most relevant subjects.
Solution Approach 2:
The system extracts and highlights only the most relevant subject information in the status bar, separating it from less important information. By using affinity scores to determine which subjects to feature prominently, the system provides complete information about all subjects while visually extracting and emphasizing only those most likely to interest the viewer.
Data Source
AI summary
Methods are described for presenting in a user interface information regarding the subject faces that will appear in upcoming images. In general, many of the images available to display will be comprised of images containing subject faces. Based on a subject affinity score between the viewer and the subjects, and image affinity is computed. Based on the image affinity scores, images are selected for presentation on the display. As images are displayed, the system analyzes some portion of the upcoming images to determine the subject information to display. As each image is displayed, subject information comprised of the subjects face and subject prominence information may be shown in an area of the display adjacent to the current image. The subject prominence information may be determined as a function of a face size ratio value and a zone value.