Block-Based Haptic Data Structure for Parallel Streaming
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing haptic data encoding formats are not suitable for streaming applications and network distribution, as they require sequential data access, making them inefficient for real-time processing.
Innovation Solution
A block-based structure for haptic data is proposed, using Network Abstraction Layer units (NALu) to encapsulate haptic data into access units (AUs) that can be processed in parallel, allowing for scalable network transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If haptic data is encoded in sequential access format, then data integrity is maintained, but processing speed and real-time performance deteriorate
Solution Approach 1:
The haptic data structure is segmented into independent Network Abstraction Layer (NAL) units, each representing a self-contained access unit. These units can be processed independently and in parallel, eliminating sequential processing requirements while maintaining data integrity through standardized unit boundaries and synchronization mechanisms.
Solution Approach 2:
The patent transitions from a one-dimensional sequential data structure to a multi-dimensional block-based structure where data is organized in parallel access units with hierarchical metadata. This dimensional change enables simultaneous processing of multiple data blocks while preserving temporal relationships through timestamp and synchronization metadata.
2Productivity
If haptic data is processed sequentially, then memory access is simplified, but network distribution efficiency and scalability worsen
Solution Approach 1:
Haptic data is divided into discrete Network Abstraction Layer units that can be independently transmitted over networks. Each unit contains complete information for a specific time segment, allowing parallel transmission and reception without requiring sequential access, thus improving network distribution efficiency while maintaining operational simplicity through standardized unit interfaces.
Solution Approach 2:
The NAL unit structure serves multiple functions simultaneously: it acts as a transmission packet for network distribution, a processing block for parallel computation, and a storage unit for haptic data. This multi-functionality enables the same data structure to optimize both network efficiency and access simplicity without compromise.
3Adaptability or versatility
If proprietary haptic formats are used, then data precision is maintained, but adaptability to different platforms and streaming applications deteriorates
Solution Approach 1:
The NAL unit structure provides a universal container format that can accommodate different haptic data types and precision requirements while maintaining platform independence. The standardized metadata and syntax elements enable precise haptic data representation across diverse platforms, achieving both adaptability and precision simultaneously.
Solution Approach 2:
The patent uses parameter-based metadata within NAL units to control data precision and format characteristics. By adjusting parameters such as quantization levels, sample rates, and compression settings within the standardized structure, the system maintains high data precision while adapting to different platform requirements and streaming applications.
Data Source
AI summary
Methods, device and data stream are provided to generate, transmit and decode haptic data. The present principles generally relate to the domain of encoding haptic data. In particular, the present principles relate to a block-based structure for haptic data allowing to process chunks of data in parallel for scalable network distribution like streaming applications.