Odor Discrimination Using Sensor Output Merging and Indicator Extraction
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Solution Overview
Problem
Current odor discrimination methods rely heavily on human olfaction, which is subjective, time-consuming, and labor-intensive, and existing sensing systems require numerous sensors to mimic human olfactory mechanisms, resulting in excessive data and calculation processing.
Innovation Solution
An information processing apparatus that uses a smaller number of sensors to obtain a larger number of indicators by combining sensor outputs, allowing for odor discrimination through indicator-value-patterns, reducing the need for extensive data processing and sensor numbers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of sensors are used to mimic human olfactory mechanisms, then odor discrimination capability is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent combines outputs from multiple sensors to generate composite indicators that represent odor characteristics. Instead of processing each sensor output separately, the system merges sensor responses into integrated indicator values that capture essential odor information, thereby reducing complexity while maintaining discrimination capability
Solution Approach 2:
The patent extracts key odor-related information from sensor outputs by generating a smaller set of meaningful indicators. Rather than using all raw sensor data, the system identifies and extracts the most relevant features that distinguish different odors, reducing the data dimensionality while preserving discriminatory power
2Measurement precision
If a large number of sensors are used to mimic human olfactory mechanisms, then odor discrimination capability is improved, but calculation processing amount increases
Solution Approach 1:
The patent extracts essential odor characteristics from sensor data by computing a limited number of indicator values that capture the most discriminative information. This extraction process reduces the calculation burden by focusing computational resources on the most relevant features rather than processing all sensor outputs equally
Solution Approach 2:
The patent combines multiple sensor outputs into fewer indicator values through mathematical operations. This merging reduces the total number of calculations required while preserving the essential information needed for odor discrimination, as the combined indicators represent aggregated sensor responses
3Ease of operation
If human olfaction is used for odor evaluation, then subjective discrimination is achieved, but time consumption and labor increase
Solution Approach 1:
The patent replaces the human olfactory system with an automated sensor-based system that mimics human odor perception. Instead of relying on human specialists to evaluate odors subjectively, the system uses sensors combined with indicator generation to objectively assess odor characteristics, thereby eliminating time-consuming manual evaluation while maintaining discrimination capability
Solution Approach 2:
The patent creates an artificial system that copies the functional aspects of human olfaction using sensors and computational indicators. By replicating the information processing aspects of human odor perception through indicator-value-patterns, the system achieves automated odor evaluation that substitutes for human sensory capabilities
Data Source
AI summary
According to one embodiment, an information processing apparatus comprises a processor. The processor is configured to receive a first number of outputs from the first number of sensors mutually different in response to an odor, obtain a second number of indicators by using the first number of outputs from the first number of sensors, the second number being larger than the first number, obtain the second number of indicator values by using the first number of outputs and the second number of indicators, and discriminate the odor based on the second number of indicator values.