Machine Learning Prediction of Long-Term Hedonic Response
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Solution Overview
Problem
The evolution of hedonic responses to sensory stimuli over time is unpredictable, making it challenging to develop consumer products with consistent long-term appeal, leading to potential waste and resource inefficiency.
Innovation Solution
A computer-implemented method using machine learning to predict long-term hedonic responses by exposing individuals to sensory stimuli multiple times, collecting data on their responses, and training algorithms to forecast future reactions, which can be applied to product development to optimize sensory stimuli.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If repeated exposure to sensory stimulus is conducted to assess long-term hedonic response, then prediction accuracy is improved, but time consumption and measurement complexity increase
Solution Approach 1:
The patent applies preliminary action by conducting multiple exposures to the sensory stimulus during an initial time period before the actual prediction time point. This preliminary exposure phase allows the system to collect hedonic response data and train the machine learning algorithm in advance, enabling accurate long-term prediction without requiring extended measurement periods later. The exposure pattern (number of exposures, intervals, initial time period) is optimized to achieve sufficient prediction accuracy within a reasonable time frame.
2Reliability
If machine learning algorithm is trained with extensive exposure data, then prediction reliability is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent replaces complex manual assessment mechanisms with a machine learning algorithm that automatically processes exposure data and predicts long-term hedonic response. Instead of requiring expert sensory evaluation or complex statistical analysis by human operators, the system uses computational algorithms to analyze the exposure pattern and hedonic response data, significantly reducing operational complexity while maintaining or improving prediction reliability.
3Productivity
If product development uses traditional hedonic response assessment, then development speed is maintained, but resource waste and manufacturing inefficiency increase
Solution Approach 1:
The patent enables preliminary assessment of long-term hedonic response during the product development phase through repeated exposure and machine learning prediction. This allows developers to identify promising sensory stimuli and product formulations early in the development process, before full-scale manufacturing. By predicting which products are likely to maintain or improve hedonic response over time, the system prevents resource waste on products that would fail in the market, thereby improving overall development efficiency and reducing material waste.
Data Source
AI summary
A method of predicting the long-term hedonic response to at least one predetermined sensory stimulus for an individual is disclosed. The method comprises the steps of (a) exposing the individual to the at least one sensory stimulus for a number of times over an initial time period of exposure according to an exposure pattern, (b) for each exposure, obtaining data indicative of the individual's hedonic response to the at least one sensory stimulus, (c) providing the data indicative of the individual's hedonic response and the exposure pattern to a machine learning algorithm, and (d) predicting the individual's long-term hedonic response to the sensory stimulus by the machine learning algorithm for a time point a predetermined prediction time period after the initial time period of exposure.