Impromptu Community Streamer Cross-Platform Event Aggregation
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Solution Overview
Problem
Current social media platforms fail to enable users to discover and interact with a comprehensive collection of social media streams from a specific event in real-time, lacking the ability to integrate across platforms based on location and time, and do not allow for seamless interaction across multiple formats within an impromptu community.
Innovation Solution
A system that identifies and aggregates social media streams from the same location and time using an impromptu community (IC) indicator, allowing users to interact through text, audio, and video, and integrates across various social media platforms to form a community around events of interest, enabling communication channels and media sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If social media platforms aggregate streams from multiple platforms based on location and time, then users can discover and interact with a broader community around events, but the system complexity and data processing requirements increase significantly
Solution Approach 1:
The patent introduces an intermediary system that acts as a mediator between multiple social media platforms and user devices. This intermediary aggregates data sets from various platforms, applies filtering and sorting algorithms based on location and time parameters, and presents unified event-based streams to users. The intermediary handles the complexity of cross-platform integration internally, allowing users to access diverse content without directly managing platform-specific complexities.
Solution Approach 2:
The system implements a universal data aggregation layer that can process and integrate data from multiple different social media platforms through a common interface. This multi-functional approach allows the same system to handle various platform protocols and data formats, enabling cross-platform integration without requiring separate specialized systems for each platform.
2Quantity of substance
If the system processes and aggregates real-time data sets from multiple sources, then comprehensive event coverage is achieved, but the data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary filtering and pre-processing of data sets as they arrive from multiple sources. By applying location and time-based filters early in the data pipeline, the system eliminates irrelevant data before it enters the main aggregation and processing stage. This preliminary action reduces the volume of data that requires intensive processing, thereby improving overall processing efficiency while maintaining comprehensive event coverage.
Solution Approach 2:
The data processing system is segmented into multiple independent modules: data collection from various platforms, filtering based on location and time parameters, aggregation of relevant data sets, and presentation to users. This segmentation allows each module to operate independently and efficiently, with parallel processing capabilities that improve overall productivity while handling large volumes of data.
3Ease of operation
If the system allows interaction across multiple communication formats (text, audio, video), then user engagement and community interaction improve, but the bandwidth and resource requirements increase
Solution Approach 1:
The system dynamically adapts the communication format and quality based on user preferences, device capabilities, and network conditions. For example, the system can automatically switch between video, audio, and text modes, or adjust video resolution and bitrate in real-time. This dynamic adjustment allows users to engage with multiple communication formats while optimizing bandwidth consumption according to current resource availability.
Solution Approach 2:
The system applies different quality levels and compression ratios to different communication formats and user groups. High-definition video may be provided to users with suitable devices and network conditions, while lower-resolution or alternative formats are provided to others. This local quality adjustment ensures that each user receives the most appropriate format for their situation, improving ease of operation while minimizing overall bandwidth consumption.
Data Source
AI summary
Various systems and methods for streaming an impromptu community are described herein. In one example, a system for streaming impromptu communities includes a data storage device, a community analyzer, and a publisher. In an example, the data storage device can store a number of incoming data sets, wherein each data set identifies a time and location for the origin of the data, a media instance, and an identification of an origin device or origin user. In an example, the community analyzer can operate from the data storage device to use a processor in order to calculate an IC boundary and generate an impromptu community (IC) indicator based on the proximity and temporality of the number of incoming data sets. In an example, the processor can modify the number of data sets to include the IC indicator.