Ear-wearable Device Modeling via Machine Learning Shell Generation
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Solution Overview
Problem
The manual design of ear-wearable devices is time-consuming and expensive, leading to inconsistencies due to variations in operator skill levels and techniques, making it challenging to efficiently produce custom devices that fit comfortably and meet user-specific requirements.
Innovation Solution
A computing system uses machine-learning techniques to generate ear-wearable device models based on ear modeling data, applying shell-generation and component-placement models to determine the optimal shape and component arrangement for a specific user's ear canal, enabling automated design and production of custom devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual modeling and shaping methods are used by highly skilled operators, then customization and comfort are improved, but production time and cost increase significantly
Solution Approach 1:
The patent replaces the manual mechanical modeling process with an automated computational system. The shell generation model uses machine learning algorithms to automatically create 3D shell models from ear scan data, eliminating the need for operators to manually model each device. This substitution of mechanical/manual operations with automated computational processes resolves the contradiction by maintaining customization capability while dramatically reducing production time.
Solution Approach 2:
The system enables self-service automation where the computer automatically performs shell generation and component placement without human intervention. The shell-generation model and component-placement models work autonomously to produce customized device models, allowing the system to serve itself in the design process rather than requiring skilled operators for each customization task.
2Manufacturing precision
If manual modeling by highly skilled operators is used, then device quality and comfort are improved, but production cost increases
Solution Approach 1:
The patent replaces expensive manual modeling operations with automated machine learning models. The shell generation model and component-placement models provide consistent, high-quality results without requiring highly skilled operators, thereby maintaining manufacturing precision while significantly reducing production costs associated with skilled labor.
Solution Approach 2:
The system changes the parameters of the design process from manual operations to automated computational parameters. By using machine learning models with trained parameters and algorithms, the system achieves consistent high-quality output without the variability and cost associated with human operator skill levels.
3Adaptability or versatility
If manual modeling methods are used, then operator control and customization are improved, but consistency decreases due to variations in operator skill level
Solution Approach 1:
The patent replaces manual operations with automated machine learning models that eliminate human variability. The shell generation model and component-placement models provide consistent results for each customization task, ensuring that the same input data always produces the same output quality, thereby resolving the contradiction between customization capability and result consistency.
Solution Approach 2:
The system uses trained machine learning models that have learned from existing high-quality designs. These models copy the expertise of skilled operators into algorithmic form, allowing consistent reproduction of high-quality customized designs without relying on individual operator skill levels.
Data Source
AI summary
A method comprises obtaining ear modeling data, wherein the ear modeling data includes a 3D model of an ear canal; applying a shell generation to generate a shell shape based on the ear modeling data, wherein the shell-generation model is a machine learning model and the shell shape is a 3D representation of a shell of an ear-wearable device; applying a set of one or more component-placement models to determine, based on the ear modeling data, a position and orientation of a component of the ear-wearable device, wherein the component-placement models are independent of the shell-generation model and each of the component-placement models is a separate machine learning model; and generating an ear-wearable device model based on the shell shape and the 3D arrangement of the components of the ear-wearable device.