2D Sketch to 3D Model Conversion for Activity Tracking
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Solution Overview
Problem
Existing systems lack an efficient method to create accurate three-dimensional models of dwellings using two-dimensional sketches and real-time user feedback, particularly for tracking activities within a residence, especially for patients with dementia, where immediate location communication and emergency response are critical.
Innovation Solution
An intelligent secure networked architecture utilizing a processor, machine learning, and convolutional neural networks to transform 2D sketches into 3D models, integrating geolocation and real-time feedback for activity tracking, with data transmission over internet or cellular networks for remote monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual 3D modeling methods are used for dwelling mapping, then measurement precision can be achieved, but productivity is significantly reduced due to time-consuming processes
Solution Approach 1:
The system uses 2D floor plan sketches as copies/templates to generate 3D models automatically. Users draw simple 2D representations of rooms, and the machine learning system transforms these 2D copies into accurate 3D models, dramatically reducing the time required while maintaining precision through automated dimension estimation and room structure inference.
Solution Approach 2:
The patent replaces manual mechanical modeling processes with machine learning-based automated systems. Instead of manually constructing 3D models through complex measurements and calculations, the system uses trained neural networks to automatically infer room dimensions, structures, and spatial relationships from 2D sketches, achieving both speed and accuracy.
2Manufacturing precision
If detailed 3D modeling is performed manually, then manufacturing precision improves, but loss of time increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on extensive datasets of floor plans and 3D room structures. This preliminary training enables the models to quickly and accurately generate detailed 3D models from 2D sketches without requiring time-consuming manual processing during actual use, achieving both detail accuracy and time efficiency.
Solution Approach 2:
The patent replaces time-consuming manual 3D modeling operations with automated machine learning systems that can rapidly generate detailed models. The neural networks automatically perform dimension estimation, room structure reconstruction, and spatial relationship inference, eliminating the need for manual measurement and modeling while maintaining high detail accuracy.
3Ease of operation
If simple 2D sketching interface is used, then ease of operation improves, but measurement precision may be compromised
Solution Approach 1:
The system implements feedback mechanisms where the machine learning model provides real-time dimension estimates and room structure suggestions based on the 2D sketch being drawn. Users can see preliminary 3D model generation results and make adjustments to their sketches, creating an iterative feedback loop that maintains ease of operation while improving measurement precision through automated dimension inference and validation.
Solution Approach 2:
The patent replaces manual measurement tools with machine learning-based dimension estimation. Instead of requiring users to make precise manual measurements and calculations, the neural networks automatically infer accurate dimensions from the 2D sketch geometry, maintaining interface simplicity while achieving high measurement precision through automated image processing and geometric analysis.
Data Source
AI summary
Exemplary embodiments include an intelligent secure networked architecture configured by at least one processor to execute instructions stored in memory, the architecture comprising a data retention system and a machine learning system, a web services layer providing access to the data retention and machine learning systems, an application server layer that provides a user-facing application that accesses the data retention and machine learning systems through the web services layer and performs processing based on user interaction with an interactive graphical user interface provided by the user-facing application, the user-facing application configured to execute instructions for a method for room labeling for activity tracking and detection, the method including making a 2D sketch of a first room on an interactive graphical user interface, and using machine learning to turn the 2D sketch of the first room into a 3D model of the first room.