Customized Digital Streams with Privacy-Safe Content Inference
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Solution Overview
Problem
Existing content delivery systems face challenges in customizing digital data streams efficiently while adhering to data privacy laws, particularly in the context of streamed sports events and other live or pre-recorded content, leading to suboptimal consumer engagement and increased energy consumption.
Innovation Solution
A system and method for customized digital content generation that infers the type of content based on the source and locale without user-specific data, using a first and second content server to select and stitch supplemental content pieces into the stream, allowing for localized and timely customization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If user-specific data is collected and shared for content customization, then content personalization improves, but data privacy compliance deteriorates
Solution Approach 1:
The patent extracts and removes user-specific personal data from the content customization process. Instead of using identifiable user data, the system uses anonymized aggregate data and contextual information (device type, location, time) to deliver personalized content without violating privacy regulations. This separates the personalization function from the data collection function.
Solution Approach 2:
The patent introduces an intermediary layer (content analysis system) that sits between data collection and content delivery. This intermediary processes data through anonymization, aggregation, and contextual analysis to create personalized content recommendations without exposing or transmitting sensitive user information, thus mediating between personalization needs and privacy requirements.
2Productivity
If real-time content customization is performed during streaming, then consumer engagement improves, but processing time and energy consumption increase
Solution Approach 1:
The patent performs preliminary content analysis and customization decisions before the actual streaming begins. The system pre-processes content metadata, pre-identifies relevant segments, and pre-determines customization parameters based on available contextual data. This preliminary preparation reduces the processing burden during real-time streaming, enabling faster response times while maintaining high engagement.
3Measurement precision
If extensive content metadata is analyzed for customization, then content selection accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the content metadata analysis into distinct hierarchical layers: core metadata (essential for basic personalization), secondary metadata (for enhanced personalization), and tertiary metadata (for optional refinement). The system selectively processes segments based on available resources and personalization needs, allowing accurate content selection without requiring full analysis of all metadata, thus reducing computational complexity while maintaining precision.
Data Source
AI summary
Methods of digital content generation according to an aspect of the invention include the steps of receiving a request for content to supplement a first digital content piece that is being or will be streamed to one or more user digital data devices; inferring a type (if not identity) of the first digital content piece and identifying one or more supplemental digital content pieces to supplement it; and, using that/those supplemental content piece(s) to customize the first digital content piece upon delivery to user devices in specific locales.