Smart Nose Machine Learning Odor Detection
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Solution Overview
Problem
Current smart nose technologies struggle to accurately detect and differentiate between various odors, especially dangerous ones, and lack the ability to update odor patterns in real-time, leading to potential safety hazards and limited functionality in environments where human olfactory detection is impaired.
Innovation Solution
A smart nose system integrated with machine learning and image recognition, utilizing a feedback loop to update odor pattern databases and create context-based odor predictions, combines odor detection with image recognition to enhance accuracy and safety by associating odors with visual objects, even in environments without internet connectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional odor detection methods are used, then the device complexity is low, but the measurement precision of odor detection is insufficient
Solution Approach 1:
The patent combines odor detection sensors with image recognition cameras and integrates them into a unified system with a single lens assembly. The odor sensor array and image sensor work together to detect both chemical and visual characteristics of targets, merging multiple detection modalities into one integrated device that shares common structural components.
Solution Approach 2:
The lens assembly serves multiple functions: it focuses light for the image sensor to capture visual data, and simultaneously focuses odor molecules onto the odor sensor array for chemical detection. This multi-functional design allows a single optical component to support both imaging and odor detection operations, reducing overall system complexity while improving measurement precision.
2Adaptability or versatility
If odor pattern database is updated in real-time, then the adaptability of the system improves, but the loss of time for data processing increases
Solution Approach 1:
The system pre-processes and stores odor pattern data in a database before actual detection is needed. By maintaining a pre-built library of odor patterns and their corresponding image characteristics, the system can quickly match detected odors against known patterns without requiring extensive real-time analysis, thus reducing data processing time while maintaining adaptability through periodic database updates.
Solution Approach 2:
The system implements a feedback mechanism where detected odor-image correlations are stored and used to refine the odor pattern database over time. This feedback loop allows the system to learn from previous detections and improve its accuracy without requiring complex real-time processing, as the learned patterns are cached for future rapid matching.
3Measurement precision
If image recognition is integrated with odor detection, then the measurement precision of odor identification improves, but the device complexity increases
Solution Approach 1:
The patent merges the odor detection system with image recognition by integrating an odor sensor array and an image sensor within the same device housing. Both sensors are positioned to detect the same target simultaneously, allowing the system to correlate odor data with visual data for more accurate identification of hazardous substances and objects.
Solution Approach 2:
The lens assembly is designed to serve dual purposes: it focuses light for the image sensor to capture visual images, and simultaneously focuses odor molecules onto the odor sensor array. This multi-functional optical design reduces the need for separate detection systems and minimizes integration complexity while maintaining high measurement precision through correlated multi-modal detection.
Data Source
AI summary
Methods, apparatuses, and systems associated with a smart nose with machine learning are described. A system can include a smart nose device configured to receive an odor and create a first odor vector associated with the odor. The system can include an image detection device configured to receive a plurality of images while the odor is received and identify a plurality of objects within the plurality of images. The system can also include a computing device to refine the first odor vector based on the identified plurality of objects, create, utilizing a machine learning model, a second odor vector based on the refined first odor vector and an odor pattern database, and predict the odor based on the second odor vector.