Predicting Olfactory Perception via Molecular Descriptor Correlation
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Solution Overview
Problem
Conventional approaches are unable to predict individual or group olfactory perception based on correlations between olfactory perception indicators (OPIs) and molecular descriptors, limiting the ability to tailor products with desired scents to specific users or user groups.
Innovation Solution
A method and system that correlate molecular structure with olfactory perception by obtaining OPI data and molecular descriptor data, executing training models to generate an output model specifying correlations between molecular descriptors and OPIs, and using this model to determine the olfactory characteristics associated with specific compounds or mixtures, allowing for targeted product development and recommendation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional approaches are used to analyze molecular compounds, then basic molecular properties can be obtained, but prediction of individual or group olfactory perception based on correlations between OPIs and molecular descriptors cannot be achieved
Solution Approach 1:
The patent introduces molecular descriptors as intermediary variables that bridge the gap between molecular structure and olfactory perception. These descriptors serve as mediators that translate complex molecular properties into quantifiable parameters that can be correlated with OPIs, enabling prediction without direct measurement of human perception.
Solution Approach 2:
The patent replaces direct human sensory measurement (mechanical/biological system) with computational modeling (information processing system). Instead of relying on human subjects to evaluate each compound's odor, the system uses trained models that process molecular descriptor data to predict olfactory perception, eliminating the need for repeated human testing.
2Reliability
If comprehensive OPI data and molecular descriptor data are collected and modeled, then accurate olfactory perception prediction is achieved, but data processing complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-collecting and organizing extensive OPI data and molecular descriptor data into structured datasets before actual prediction tasks. Training models are developed in advance using this pre-processed data, so that when predictions are needed, the system can quickly apply the trained models without performing complex data collection and analysis at prediction time.
Solution Approach 2:
The patent creates computational copies of olfactory perception through trained prediction models. Once a model is trained on comprehensive data, it produces simplified copies or representations of the complex perception data that can be rapidly generated for new compounds, reducing the computational burden of actual prediction tasks.
3Ease of manufacture
If molecular descriptor subsets are determined for specific compounds, then targeted olfactory analysis is achieved, but the scope of analysis is limited
Solution Approach 1:
The patent implements a dynamic analysis scope where the set of molecular descriptors considered can be adjusted based on the specific needs of each analysis. The system allows users to select subsets of descriptors relevant to particular compounds or desired olfactory characteristics, enabling flexible adaptation between comprehensive analysis and targeted evaluation without sacrificing either efficiency or versatility.
Data Source
AI summary
Predicting human olfactory perception based on molecular structure is described. Molecular descriptor data indicative of molecular descriptors associated with a group of molecular samples can be obtained. Olfactory perception indicator (OPI) data for a set of OPIs can also be obtained with respect to the molecular samples. A training model can be executed on the molecular descriptor data and the OPI data to yield an output model that correlates molecular attributes with OPIs for a single individual or across an aggregate of individuals. The output model can be used to predict olfactory perception for a particular compound or mixture based on which OPIs are correlated with molecular descriptors of the compound or mixture in the output model. The output model can also be inverted and used to identify molecular descriptors that are correlated with a desired set of OPIs. A molecular construct having the molecular descriptors can then be generated.