User Experience System for Adaptive Media Delivery
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Solution Overview
Problem
Current media content search methods require cognitive input and are inefficient, as users must navigate complex devices and networks to find content that matches their unique tastes and preferences, often consuming significant time and effort.
Innovation Solution
A system that passively collects and measures media connectedness values using behavioral, contextual, and experiential data from wearable and camera technologies, analyzing physiological and physical responses to media experiences to guide machine learning-assisted searches and improve content delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional media content search methods are used, then users can access media content, but users must navigate complex devices and networks consuming significant time and effort
Solution Approach 1:
The system enables self-service by automatically collecting media experience data from users without requiring manual input. Wearable devices and cameras passively gather physiological, behavioral, and contextual data, which the system then processes to generate personalized media recommendations, eliminating the need for users to manually search through complex networks
Solution Approach 2:
The system implements feedback loops by continuously collecting media experience data from users during their media consumption, analyzing this data to refine understanding of user preferences, and using this refined understanding to improve future media recommendations, creating an adaptive system that learns from user behavior patterns
2Productivity
If passive collection of media experience data is implemented, then content search process efficiency is dramatically improved, but complex data collection and analysis systems are required
Solution Approach 1:
The system achieves multi-functionality by using a single integrated platform that simultaneously collects multiple types of data (physiological, behavioral, contextual) from various sources (wearable devices, cameras, sensors) and processes all this data through unified machine learning algorithms to generate comprehensive media recommendations
Solution Approach 2:
The system introduces intermediary processing layers including machine learning algorithms and data analysis frameworks that act as mediators between the raw data collected from wearables and cameras and the final media recommendations, automatically translating complex physiological and behavioral data into actionable insights
Data Source
AI summary
A method for improving a user experience through the use of at least one communications device. The method initially senses feedback of the user experience, wherein the user experience include one or more of the following three dimensional geolocation status, temporal status, experiential status, physiological status and emotional status. The method creates at least an initial contextual data set from the initially sensed feedback, and transmits media content to the user. The method subsequently senses feedback of the user experience, creating one or more subsequent contextual data set from the subsequent sensed feedback. The method measures changes in the user experience by comparing the initial contextual data set with the subsequent contextual data set. The method generates personalized user data based on measuring changes, wherein the generated personalized data is indicative of adjustments in one or more of the three dimensional geolocation, experiential status, physiological status and emotional status of the user when the comparing of the initial contextual data set with the subsequent contextual data set.