Electronic Nicotine Delivery Device Assessment System
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Solution Overview
Problem
Current methods for assessing electronic nicotine delivery systems (ENDS) are inadequate, as they fail to accurately determine device characteristics such as power, voltage, and nicotine delivery, leading to uncertainties in health and behavioral effects, and lack user-friendly, convenient assessment methods.
Innovation Solution
A system and method using a computing platform with a processor and memory to identify and assess ENDS devices by receiving images or other inputs from mobile devices, comparing them to a database to determine device type, power settings, and liquid information, allowing for the computation of delivered active agent amounts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If self-report surveys are used to assess ECIG device characteristics, then users can report information about their devices, but the accuracy and reliability of the reported information is poor
Solution Approach 1:
The patent uses image copying technology where users take photographs of their ECIG devices and e-liquid bottles. The system then uses image recognition algorithms to automatically identify device models, specifications, and e-liquid nicotine concentrations from these images, replacing inaccurate self-reported data with objectively extracted information from visual copies of the actual products.
2Measurement precision
If extensive laboratory analysis methods are used to determine device power and nicotine delivery, then measurement accuracy is improved, but the complexity and time required for assessment increases
Solution Approach 1:
The patent replaces complex laboratory mechanical and chemical analysis systems with an automated digital image recognition system. Instead of using sophisticated lab equipment to physically analyze device characteristics and nicotine content, the system uses computer vision algorithms to extract this information from photographs, dramatically simplifying the assessment process while maintaining accuracy.
Solution Approach 2:
The system enables users to independently assess their own devices by simply photographing them. The automated image recognition system performs the identification and assessment without requiring professional laboratory analysis, allowing users to self-determine device characteristics and nicotine delivery information that would traditionally require complex external analysis.
3Loss of information
If detailed device specifications are collected through manual input, then comprehensive information is obtained, but the ease of use and user satisfaction decreases
Solution Approach 1:
The system captures comprehensive device information by having users photograph their ECIG devices and e-liquid bottles. Image recognition algorithms automatically extract device models, specifications, power ratings, and e-liquid nicotine concentrations from these images, obtaining complete information without requiring users to manually type or input any technical specifications.
Data Source
AI summary
Provided are systems and methods of identifying the functional characteristics and operational settings of any electronic inhalants or apparatuses that are designed to provide nicotine vapors to a user. The system includes a mobile device having a built-in camera to receive images of a nicotine delivery device, a system of remote backend servers with at least one image database for identifying the nicotine delivery device, and at least one processor for calculating an amount of nicotine delivered by the nicotine delivery device.