Odor Identification Using Context-Aware Neural Networks
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Solution Overview
Problem
Existing odor identification systems do not consider the conditions under which odor data is measured, leading to suboptimal object identification accuracy.
Innovation Solution
An information processing apparatus that acquires both odor data and its measurement conditions, using a learned model to identify objects by integrating odor data and acquisition conditions through a neural network-based identification model, which includes input layers for time-series odor data and categorical variables like domain, subdomain, state, and environmental information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If odor identification is performed without considering measurement conditions, then the system is simpler and faster, but the identification accuracy deteriorates
Solution Approach 1:
The patent combines odor data with acquisition condition data (measurement context) into a unified identification model. The neural network processes both odor sensor outputs and contextual information (temperature, humidity, time, location) simultaneously, merging multiple data sources to improve identification accuracy without requiring separate processing systems.
Solution Approach 2:
The patent adds a new dimension to odor identification by incorporating acquisition conditions as additional input features. Instead of relying solely on odor sensor data, the system extends the feature space to include environmental and temporal context, enabling more nuanced and accurate object identification through multi-dimensional data analysis.
2Reliability
If only odor data is used for identification, then the processing time is shorter, but the identification reliability deteriorates
Solution Approach 1:
The system performs preliminary data collection and organization of acquisition conditions alongside odor data. By pre-processing and structuring contextual information (categorizing environmental factors, time stamps, location data) before the identification process, the system prepares comprehensive datasets that enhance reliability without significantly increasing real-time processing delays.
Solution Approach 2:
The patent introduces an intermediary processing layer that integrates odor data with acquisition condition data through a neural network model. This intermediary layer synthesizes multiple data types into unified identification results, mediating between raw sensor data and final conclusions to improve reliability while managing computational requirements.
3Measurement precision
If acquisition conditions are considered in odor identification, then the identification becomes more accurate and context-aware, but the data processing complexity increases
Solution Approach 1:
The patent segments acquisition conditions into distinct categorical variables (temperature, humidity, time, location) and processes them separately before integration with odor data. This segmentation allows for systematic handling of diverse data types, reducing processing complexity by organizing information into manageable categories that can be efficiently processed by the neural network.
Solution Approach 2:
The system transforms acquisition conditions into standardized parameter formats suitable for neural network processing. By normalizing and transforming environmental and temporal data into consistent data types and scales, the patent reduces processing complexity while maintaining the richness of contextual information needed for precise odor identification.
Data Source
AI summary
An information processing apparatus includes: a first acquisition unit that acquires odor data obtained by measuring the odor of an object; a second acquisition unit that acquires an acquisition condition for the odor data; and an identification unit that identifies the object from the odor data and the acquisition condition acquired by the first and second acquisition units on the basis of a learned model obtained by performing learning on the odor data of the object and the acquisition condition, and on the object corresponding to the odor data. The acquisition condition can be text data that indicates the category of the odor and is inputted by a user who has measured the odor.