Automated Media Stream Creation via Data Firehose Analysis
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Solution Overview
Problem
Conventional systems for delivering media content to users are limited by manual curation, requiring human intervention and metadata for content discovery, leading to restricted growth and undiscovered content due to the lack of automated interest-based networks.
Innovation Solution
The development of computerized systems that leverage internet-hosted data firehoses to automatically create and communicate media streams relevant to users' interests, using deep learning algorithms to analyze and filter content, thereby automating content discovery and social interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual curation is used to deliver media content to users, then content quality and relevance can be maintained, but system complexity and labor requirements increase significantly
Solution Approach 1:
The patent replaces manual curation (mechanical human operation) with automated computerized systems that use deep learning algorithms and data firehoses to discover and deliver media content. The system automatically analyzes uploaded content, identifies user interests, and communicates relevant media streams without human intervention, thereby maintaining content quality while eliminating system complexity and labor requirements.
Solution Approach 2:
The system enables self-service by allowing content to be automatically discovered, categorized, and delivered based on user interests without requiring manual curation. The automated interest-based network continuously monitors data firehoses, identifies relevant content, and communicates it to appropriate users autonomously, making the system self-sufficient and eliminating the need for human operators.
2Productivity
If automated interest-based networks are implemented, then content discovery and delivery efficiency improve, but system complexity increases
Solution Approach 1:
The patent implements a universal automated system that performs multiple functions: monitoring data firehoses, analyzing uploaded content, identifying user interests, creating media streams, and communicating content to users. This multi-functional system consolidates what would otherwise require separate manual processes into a single automated platform, improving content discovery efficiency while managing system complexity through integration rather than proliferation of separate components.
3Reliability
If manual group curation is used, then content relevance to user interests can be ensured, but the scale and growth of the system are limited
Solution Approach 1:
The patent replaces manual group curation with automated interest-based networks that use deep learning algorithms to identify and deliver relevant content at scale. The system automatically analyzes user interests, monitors data firehoses for relevant content, and communicates media streams to appropriate users without human intervention, ensuring content relevance while enabling unlimited system growth and scalability.
Solution Approach 2:
The automated system performs self-service by autonomously identifying user interests, discovering relevant content through data firehoses, and delivering appropriate media streams without requiring manual group management. This self-sufficient approach maintains content relevance to user interests while removing the scalability limitations inherent in manual curation systems.
4Measurement precision
If deep learning algorithms are used to analyze and filter content, then content quality and relevance improve, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and analyzing content as it is uploaded to the internet, before user requests occur. The system continuously monitors data firehoses and pre-identifies relevant content based on user interests, so that when users access the system, the content is already filtered and ready for immediate delivery. This eliminates the need for real-time analysis during user interactions, reducing perceived processing time while maintaining high analysis accuracy through deep learning algorithms.
Data Source
AI summary
Disclosed are systems and methods for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods automatically identify and communicate media content to users as the media content is uploaded to the internet. The disclosed systems and methods leverage an internet hosted data firehose in order to build and communicate streams of content that are relevant to users' determined interests. Real-time analysis of the continuous stream of content results in curated media streams being created and communicated to users thereby stimulating social interactivity between users and automating the discovery of other users on a network.