Machine Learning Model for Handheld Device Condition Assessment
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Solution Overview
Problem
Current methods for assessing the condition of mobile phones for trade-in are unreliable and subjective, leading to unfair valuations for both customers and carriers due to limitations in visual inspection and qualitative assessments.
Innovation Solution
A dedicated mobile application uses a machine learned model to collect image data and determine the make, model, and physical condition of electronic devices, including touchscreen operability, to objectively assign a trade-in value, eliminating the need for subjective assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual visual inspection by technician is used to assess mobile phone condition, then the process is simple and quick, but the assessment reliability and accuracy deteriorate due to subjective perception limits
Solution Approach 1:
The patent replaces the manual visual inspection system with an automated image processing and machine learning system. The machine learned model analyzes images of the mobile device to objectively determine its condition, substituting human perception with algorithmic analysis that eliminates subjective bias and perception limitations while maintaining assessment reliability.
Solution Approach 2:
The patent introduces an intermediary system consisting of image capture devices and machine learning algorithms between the mobile device and the assessment process. This intermediary automatically analyzes device condition through images and provides objective measurements, bridging the gap between simple visual inspection and reliable condition assessment without requiring direct human evaluation.
2Measurement precision
If subjective condition assessment is used, then the process is fast and requires minimal resources, but measurement precision and objectivity deteriorate
Solution Approach 1:
The patent performs preliminary actions by capturing images of the mobile device and pre-processing them through the machine learned model before final assessment. This preliminary automated analysis prepares detailed condition data in advance, enabling precise measurements without requiring time-consuming manual inspection during the actual trade-in process.
Solution Approach 2:
The patent substitutes manual inspection with automated machine learning-based image analysis, achieving both high measurement precision through algorithmic detail detection and rapid processing speeds. The system can analyze multiple images and detect subtle conditions that human technicians might miss, all within seconds.
3Difficulty of detecting and measuring
If manual inspection checklist is used, then the method is easy to operate, but the detection precision and completeness worsen due to human vision limitations
Solution Approach 1:
The patent replaces the manual checklist inspection method with an automated machine learning system that processes images and automatically detects defects. This substitution enables comprehensive detection of various device conditions including screen quality, body damage, and component functionality without requiring operators to manually check each item on a checklist.
Solution Approach 2:
The patent creates a universal assessment system that can detect and evaluate multiple types of device conditions simultaneously through a single automated process. The machine learned model is designed to identify various defects and operational issues across different device components, providing comprehensive detection capability that surpasses specialized manual checklists.
Data Source
AI summary
Introduced here is a computer-implemented system for determining current conditions of electronic devices. The system receives an indication from an electronic device that its user is seeking to trade-in the handheld wireless device. The system presents a user interface with a touch screen verification widget for determining operability of a touchscreen of the electronic device. The system receives an identification number and verifies that the electronic device is available for trade-in using the identification number. The system prompts upload of image data depicting the electronic device via a display mechanism. The system inputs the image data to a machine learned model that is trained to determine make, model, and physical condition of electronic devices. The system receives outputs from the machine learned model and determines the current condition of the electronic device based on the outputs, the operability, and the verification.