Granular Multimedia Tagging via Emotion Detection
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Solution Overview
Problem
Current methods for tagging multimedia content in connected networks fail to provide granular, contextual, and personalized information, lacking the ability to pinpoint specific areas of content relevance, capture true user reactions, and enable scalable and generic tagging and search functionality.
Innovation Solution
A system and method for granular tagging of multimedia content using client devices with emotion detection modules that record and generate instantaneous emotional scores and profiles, allowing for continuous and uniform tagging based on individual cues, which are then stored and shared in a cloud network for enhanced content analysis and monetization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional content tagging methods are used, then content classification is achieved, but granular and personalized information is lost
Solution Approach 1:
The patent segments content tagging into granular temporal units (e.g., 5-second intervals) rather than treating content as a whole. Each segment receives its own emotional score and tags based on user reactions occurring at that specific time, enabling precise localization of content features and preservation of contextual information through time-stamped metadata.
Solution Approach 2:
The system applies different tagging qualities to different portions of content based on local user reactions. Instead of uniform tagging, each content segment receives tags reflecting the specific emotional state and user engagement at that moment, creating locally optimized metadata that captures nuanced contextual information where it matters most.
2Productivity
If aggregate user feedback is collected, then general content ratings are obtained, but individual user reactions are averaged out
Solution Approach 1:
The system segments user feedback into individual reaction events tied to specific content segments, rather than aggregating into single overall ratings. Each user's emotional state is captured at multiple time points throughout content consumption, creating a sequence of discrete reaction data points that preserve individual variation while maintaining analytical efficiency through structured formatting.
Solution Approach 2:
The patent adds the temporal dimension to user feedback by recording when each reaction occurred relative to content playback. This transforms flat aggregate ratings into time-series data where individual reactions are distinguished not just by intensity but by their position in the content timeline, enabling both individual reaction analysis and efficient aggregate patterns recognition.
3Loss of information
If manual content tagging is performed, then detailed metadata is created, but scalability is limited
Solution Approach 1:
The system enables automatic self-service tagging by capturing user emotional states through sensors and device data during natural content consumption. Users implicitly generate metadata through their involuntary physiological and behavioral responses (screen interactions, device usage patterns, sensor data) without manual intervention, achieving both comprehensive metadata coverage and high scalability simultaneously.
Solution Approach 2:
The patent replaces manual mechanical tagging processes with automated detection systems using sensors, device sensors, and algorithmic analysis of user behavior patterns. This substitution eliminates the bottleneck of human annotators while maintaining or improving metadata quality through objective, continuous measurement of actual user reactions at scale.
4Measurement precision
If continuous user monitoring is implemented, then detailed emotional profiles are captured, but system complexity increases
Solution Approach 1:
The system uses existing multi-functional device components (screen, sensors, processors) already present in smartphones and tablets for content consumption, making them simultaneously serve emotional detection purposes. This universal reuse of existing hardware infrastructure captures detailed emotional profiles without adding separate dedicated monitoring devices, thereby limiting the increase in overall system complexity.
Solution Approach 2:
The patent introduces software intermediaries that mediate between raw sensor data and emotional profile generation. These intermediary layers process and interpret sensor inputs through algorithms that translate physical measurements into emotional states, simplifying the architecture by creating manageable abstraction layers rather than requiring direct complex hardware-emotion mapping.
Data Source
AI summary
A system and a method for generating an emotional profile of the user and deriving inference from the analytics of generated emotional profile is provided. The method involves sharing media content or online event in a connected environment; capturing user's reaction to the said content or event; generating an emotional score of the user to rate the media content or event; and sharing the emotional score within the connected environment.