Extended Reality Environment Generation for Physical Space Planning
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Solution Overview
Problem
Conventional tools for planning physical spaces are inefficient and inaccurate due to reliance on outdated data sources and failure to account for movable assets, leading to wasteful expenditure of time and resources.
Innovation Solution
A system and method for generating an extended reality environment that incorporates diverse data formats and sources to accurately represent physical spaces and their assets, using a processor, content receiver, content pre-processor, and asset linker to create a digital layout and model the placement of assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional tools use limited and outdated data sources, then the tool complexity is reduced, but the accuracy and reliability of space planning deteriorates
Solution Approach 1:
The system integrates multiple data sources including IoT sensors, building management systems, and manual input interfaces into a single unified platform. This multi-functional approach allows the system to collect, process, and update spatial data from diverse sources simultaneously, improving reliability without proportionally increasing complexity through standardized integration protocols
Solution Approach 2:
The patent introduces an intermediary data processing layer that acts as a mediator between various data sources and the core planning engine. This intermediary layer standardizes data formats, filters redundant information, and manages data flow, thereby improving data reliability while containing complexity through modular architecture
2Productivity
If conventional tools do not track movable assets, then the device complexity is reduced, but the productivity and completeness of space planning deteriorates
Solution Approach 1:
The system segments asset tracking into modular components: detection modules (sensors, RFID readers), tracking modules (data collection and storage), and optimization modules (analysis and recommendation). This segmentation allows the system to progressively enhance productivity by adding only necessary tracking capabilities rather than implementing a complete complex system at once
Solution Approach 2:
The system implements self-updating asset tracking where movable assets are automatically detected and registered by sensors and RFID readers without manual intervention. The system autonomously updates asset locations and statuses, improving productivity while minimizing the complexity of manual asset management procedures
3Loss of time
If manual planning methods are used, then the device complexity is reduced, but the loss of time and resources increases
Solution Approach 1:
The system performs preliminary actions by pre-collecting and organizing spatial data from IoT sensors and building management systems before planning begins. It pre-processes this data into structured formats and pre-identifies potential optimization opportunities, thereby reducing the time required for actual planning execution while justifying the tool complexity through automated preparatory work
Solution Approach 2:
The patent replaces manual mechanical planning processes with computer-implemented automated systems. Sensors, processors, and algorithms substitute for human manual measurement and calculation, dramatically reducing planning time while the resulting tool complexity is managed through user-friendly interfaces and automated workflows
4Measurement precision
If conventional tools cannot detect structural changes, then the device complexity is reduced, but the reliability and measurement precision of space assessment deteriorates
Solution Approach 1:
The system merges multiple sensing capabilities (IoT sensors, RFID readers, cameras) into an integrated detection network. By combining these different sensor types, the system achieves high measurement precision for detecting structural changes and asset movements, while the complexity is managed through unified data processing protocols and centralized coordination
Data Source
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AI summary
Examples of extended reality environment generation are provided. The techniques for extended reality environment generation may include receiving digital content and metadata associated with a physical environment and generating a digital layout of the physical environment by scaling the received digital content. Next, Global Positioning System (GPS) coordinates and functional coordinates are associated with placement locations in the digital layout. Further, a pre-defined placement of an asset is received in the digital layout and a set of GPS coordinates and a set of functional coordinates are identified for the asset placed in the digital layout. Next, the set of GPS coordinates and the set of functional coordinates are associated with metadata of the asset and the asset stored along with the metadata as a model linked with the digital layout. Further, an extended reality environment for the physical environment is generated with the assets by extracting the model.