Audience Segmentation via 360-Degree Viewing Angle Tracking
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Solution Overview
Problem
Current audience segmentation techniques fail to capture user preferences effectively for 360 degree videos, as they do not account for the varying viewing angles of users, limiting the ability to tailor marketing strategies based on individual interests.
Innovation Solution
A system that records and analyzes user-controlled viewing angles of 360 degree videos to identify predominant sequences, assigns audience segment tags, and classifies users into segments based on their viewing trails, enabling targeted marketing communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional audience segmentation techniques are used, then the segmentation process is simple, but user preferences cannot be captured effectively for 360 degree videos
Solution Approach 1:
The patent segments the viewing environment into multiple discrete angles and positions, tracking user viewing trails as sequences of angular positions over time. This segmentation of the continuous viewing space into measurable angular segments enables precise capture of user preferences while maintaining manageable data structures for analysis
Solution Approach 2:
The patent adds angular dimensionality to traditional audience segmentation by incorporating viewing angle data (azimuth and elevation angles) into the segmentation criteria. This transforms conventional 1D demographic segmentation into multi-dimensional segmentation that includes spatial viewing behavior, thereby capturing user preferences with greater precision
2Measurement precision
If viewing angle data is recorded and analyzed, then audience segmentation precision is improved, but data processing complexity increases
Solution Approach 1:
The system performs preliminary processing of viewing angle data by converting raw angular coordinates into viewing trail representations and pre-segmenting the viewing environment into discrete angular zones. This preliminary organization of viewing data simplifies subsequent analysis and reduces the computational complexity of real-time processing
Solution Approach 2:
The patent introduces viewing trail sequences as an intermediary representation between raw viewing angle data and final audience segmentation results. These viewing trails serve as a simplified intermediate data structure that captures essential viewing behavior patterns while reducing the complexity of raw angular data for further analysis
3Productivity
If user viewing behaviors are tracked in detail, then marketing targeting effectiveness is enhanced, but information processing requirements increase
Solution Approach 1:
The patent extracts only the essential viewing behavior features from complete viewing angle sequences, focusing on key metrics such as predominant viewing directions, viewing duration in specific zones, and viewing trail patterns. This extraction of critical features reduces the volume of data that needs to be processed while retaining the information necessary for effective marketing targeting
Data Source
AI summary
Audience segmentation can be based on a viewing angle of a user viewing a video of a multi-angle viewing environment. During playback, a sequence of the user-controlled viewing angles of the video are recorded. The sequence represents the viewing angle of the user at a given point in time. Based on the sequences of several users, a predominant sequence of viewing angles of the video is determined. One or more audience segment tags are assigned to the predominant sequence of viewing angles. During subsequent playbacks of the video, the sequence(s) of user-controlled viewing angles of the video are recorded. The recorded sequence(s) of the subsequent user(s) are compared to the predominant sequence of viewing angles of the video, and the subsequent user(s) are assigned to an audience segment based on the comparison and the corresponding audience segment tags.