Holographic Image Adaptation Using Environmental Reasoning Feedback
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Solution Overview
Problem
Holograms in phygital meetings often misalign with their physical environments due to lack of real-time adaptation, leading to a noticeable disconnect between digital and physical elements.
Innovation Solution
A reasoning engine-based system that uses sensor data to infer environmental changes and adjusts holographic images in real-time to align with the physical environment, utilizing a deep fusion reasoning engine (DFRE) for accurate projection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If holographic images are projected into physical environment without real-time adaptation, then device complexity is reduced, but alignment precision between hologram and physical environment deteriorates
Solution Approach 1:
The system dynamically adjusts holographic image parameters in real-time based on sensor data from the physical environment. The reasoning engine continuously processes environmental changes and modifies hologram positioning, scaling, and orientation to maintain precise alignment, transforming a static projection system into an adaptive dynamic system.
Solution Approach 2:
The system implements a feedback loop where sensors continuously monitor the physical environment, the reasoning engine processes this data to infer environmental changes, and the holographic projection is adjusted accordingly. This closed-loop feedback mechanism ensures precise alignment by constantly comparing the hologram's position with the actual physical environment and making corrective adjustments.
2Manufacturing precision
If reasoning engine is used to adjust holographic image in real-time, then alignment precision is improved, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing sensor data and pre-calculating potential environmental changes before they fully manifest. The reasoning engine anticipates environmental variations and proactively adjusts holographic parameters, reducing the need for reactive corrections and minimizing processing delays.
Solution Approach 2:
The system maintains continuous processing of sensor data and continuous adjustment of holographic images without interruption. This uninterrupted flow of data processing and image adjustment ensures that alignment precision is maintained consistently over time, preventing cumulative errors that would require lengthy correction periods.
3Adaptability or versatility
If sensor data is continuously captured and processed, then adaptability to environmental changes is improved, but energy consumption increases
Solution Approach 1:
The system applies partial processing by selectively analyzing only the most relevant sensor data that indicates significant environmental changes. Rather than processing all sensor inputs at full capacity, the reasoning engine identifies and focuses on critical changes that affect holographic alignment, reducing overall computational energy consumption while maintaining adaptability.
Data Source
AI summary
In one embodiment, a device transmits data characterizing a holographic image to a physical environment. A visible rendering of the holographic image is created in the physical environment. The device uses a reasoning engine to make an inference regarding a state of the physical environment based on sensor data captured in the physical environment indicative of one or more real-time characteristics of the physical environment. In response to the inference, the device adjusts the holographic image. Then, the device transmits data characterizing the adjusted holographic image to the physical environment, and a visible rendering of the adjusted holographic image is created in the physical environment.