Polyhedral Data Sets for Immersive Remote Event Attendance
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Solution Overview
Problem
Existing technologies fail to effectively enable remote attendance and participation in events by not fully replicating the immersive experience of being physically present, lacking sound and genuine participation capabilities.
Innovation Solution
A system and method that utilize polyhedral data sets to transfer and simulate experience space data and client space data, allowing remote clients to attend and participate in events by collecting, broadcasting, and projecting audio and video feedback, and synchronizing client data with experience space data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional broadcast methods are used to transmit event footage, then the basic information transfer is achieved, but the immersive experience and realism of remote attendance cannot be replicated
Solution Approach 1:
The experience space is divided into multiple polyhedral regions, each captured by distributed sensors. This segmentation allows complex spatial data to be managed in manageable chunks, improving realism while controlling system complexity through modular architecture.
Solution Approach 2:
The system transforms two-dimensional sensor data into three-dimensional polyhedral representations of experience space. This dimensional transformation enables immersive remote attendance by creating volumetric data structures that preserve spatial relationships and enhance realism.
2Reliability
If detailed polyhedral data sets are collected and processed to enhance immersion, then the realism and engagement of remote attendees improve, but the data processing complexity and computational requirements increase
Solution Approach 1:
The polyhedral data sets are segmented into manageable regions, each processed independently. This segmentation reduces computational complexity by dividing the overall data processing task into smaller, parallelizable units while maintaining the authenticity of the overall experience.
Solution Approach 2:
Experience space data is pre-collected, pre-processed, and pre-synchronized before the actual event. This preliminary action reduces real-time computational requirements and simplifies the data extraction and synchronization processes during live events.
3Measurement precision
If distributed sensor arrays are used to collect experience space data, then the quality and detail of captured information improve, but the system complexity and deployment difficulty increase
Solution Approach 1:
The distributed sensor array is organized into segmented polyhedral regions, with each region handled by local sensors. This segmentation improves measurement precision by ensuring localized data quality while reducing overall system complexity through modular sensor deployment.
Solution Approach 2:
The distributed sensor array is designed with universal, multi-functional sensor units that can be deployed in various configurations. This universality maintains high measurement precision across different environments while simplifying deployment by using standardized, interchangeable sensor modules.
4Adaptability or versatility
If polyhedral data sets are used to represent experience space, then the scalability of data insertion and extraction improves, but the complexity of data management and processing increases
Solution Approach 1:
The polyhedral data structure is segmented into hierarchical levels and regions, enabling scalable insertion and extraction of data at different granularities. This segmentation provides adaptability for various data management needs while controlling complexity through standardized operations on uniform data structures.
Solution Approach 2:
The system manages polyhedral data through standardized dimensional operations (creation, extraction, insertion, transformation) that work consistently across different data scales. This dimensional approach enables scalability by treating data of different sizes through the same operational framework, reducing management complexity.
Data Source
AI summary
A method of transferring data can comprise dividing an experience space into polyhedrons comprising a network of interconnected planes. Sheets of experience space data of the experience space can be collected via a distributed sensor array. Each sensor of the distributed sensor array can comprise a corresponding sheet of experience space data. One of the polyhedrons can be selected as a client location polyhedron. Client location data can be extracted from the experience space data. The client location data can correspond to the client location polyhedron. Extraction can be by intersecting ones of the sheets of experience space data to form a polyhedral data set.