Experience Feed Analysis Engine for Viewer Interaction Metrics
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Solution Overview
Problem
Current data search and analysis technologies fail to provide a comprehensive understanding of how experience feeds are viewed, interacted with, and managed, lacking objective metrics that account for various attributes and viewer interactions, leading to inaccurate and unreliable systems for stakeholders like broadcasters and advertisers.
Innovation Solution
An experience analysis system comprising an experience database and an experience feed analysis engine that processes data to generate experience metrics, including viewer interaction metrics, feedback, and broadcaster performance metrics, to objectively quantify the experience and optimize feed presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive experience metrics are generated to objectively quantify viewer interactions and feed attributes, then measurement precision and reliability are improved, but device complexity and computational requirements increase
Solution Approach 1:
The experience analysis system segments the complex task of measuring viewer experience into distinct components: experience feeds (capturing viewer interactions, device attributes, and contextual data), experience objects (structured representations of specific experiences with attributes like duration, quality ratings, and interaction types), and experience metrics (aggregated measurements derived from multiple experience objects). This segmentation allows each component to be processed independently, improving measurement precision while managing system complexity through modular architecture.
Solution Approach 2:
Experience objects serve as intermediaries between raw experience feed data and final experience metrics. The experience objects structure and normalize diverse input data from multiple sources (viewer interactions, device attributes, contextual information) into a standardized format that can be systematically analyzed. This intermediary layer enables comprehensive measurement without requiring direct complex processing of all raw data, thus improving reliability while managing computational complexity.
2Loss of information
If multiple experience feeds and attributes are analyzed to provide comprehensive understanding, then information completeness is improved, but loss of information decreases (more information is retained)
Solution Approach 1:
The experience analysis system implements a universal experience object structure that can represent multiple types of experiences (video viewing, audio listening, interactive content) through a common framework. Experience feeds capture diverse attributes (viewer demographics, device characteristics, environmental context, interaction patterns) within a unified data model. This multi-functional approach ensures comprehensive information retention across different experience types without requiring separate complex analysis systems for each.
Solution Approach 2:
The system transforms raw experience feed data into experience objects by applying parameter changes - selecting, filtering, and transforming raw data into structured attributes with defined schemas. Experience objects contain standardized parameters (duration, quality ratings, interaction types, device attributes) that preserve essential information while organizing it in a manageable format. This parameter transformation maintains information completeness while reducing data complexity for subsequent metric generation.
3Productivity
If experience metrics are generated in real-time to provide immediate feedback, then productivity and response time are improved, but use of energy and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-defining experience object schemas, attribute relationships, and metric calculation rules before actual experience data arrives. Experience feeds are structured according to predetermined templates, and experience objects are instantiated using pre-configured parameter mappings. This preliminary structuring enables rapid real-time processing of incoming data without requiring complex runtime decision-making, thus improving productivity while managing energy consumption through efficient template-based processing.
Solution Approach 2:
The system creates simplified copies of complex experience data through experience objects that replicate essential characteristics in a condensed format. Instead of processing all raw experience feed data in real-time, the system generates experience objects containing key attributes and metrics that capture the essential experience information. These copied representations enable fast metric generation and feedback provision while reducing the computational energy required compared to analyzing complete raw datasets in real-time.
Data Source
AI summary
A remote experience system is presented. The inventive subject matter provides apparatus, systems and methods in which one can use an experience feed analysis engine to gain better understanding of how experience feeds are viewed, processed, interacted, created, broadcasted, or otherwise managed by means of experience objects and generating experience metrics that can help analyze viewing patterns, broadcasting attributes, event management characteristics, among other features that can help improve and analyze remove viewing experience.