Wine Recommendation via Infrared Spectroscopy and Computer Vision
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Solution Overview
Problem
Conventional methods for evaluating consumable items like wine are limited by expert experience and personal biases, and machine-based systems are often calibrated to detect specific chemical compounds, making them inefficient for consumer use and limited in identifying unknown compounds.
Innovation Solution
A system and method using computer vision classification models trained on visual image representations derived from infrared spectroscopy data, leveraging transfer learning and convolutional neural networks to classify and recommend consumable items based on their chemical composition, enabling detection of unknown compounds and providing data-driven recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-based systems are calibrated to detect specific chemical compounds, then measurement precision for known compounds is improved, but adaptability to identify unknown compounds deteriorates
Solution Approach 1:
The patent transforms chemical spectroscopy data from traditional calibrated measurement parameters to visual image parameters through conversion to visual representations. This allows the system to detect both known and unknown compounds by analyzing patterns in the visualized spectra rather than relying solely on pre-calibrated chemical parameters, thereby improving adaptability while maintaining measurement precision.
Solution Approach 2:
The patent introduces visual image representations as an intermediary between the spectroscopy data and the classification model. This intermediary layer converts complex spectral data into a format that can be processed by computer vision models, enabling the system to identify both known and unknown compounds through pattern recognition rather than direct calibrated measurement.
2Adaptability or versatility
If expert evaluation methods are used, then adaptability to different consumable items is improved, but reliability of objective assessment deteriorates
Solution Approach 1:
The patent replaces the mechanical system of human expert evaluation with an automated computer vision-based system. The system uses trained classification models to objectively assess consumable items based on their spectral characteristics, eliminating the subjectivity and bias inherent in human evaluation while maintaining the ability to adapt to different types of consumables through transfer learning.
Solution Approach 2:
The patent develops a universal classification model that can evaluate multiple types of consumable items (wine, beer, spirits, etc.) using the same underlying technology. The model is trained on diverse datasets and can apply the same assessment methodology across different consumable categories, providing both objectivity and broad adaptability.
3Manufacturing precision
If chemical analysis is performed during production stages, then manufacturing precision is improved, but ease of operation for consumers deteriorates
Solution Approach 1:
The patent creates a digital copy of the complex chemical analysis process in the form of visual image representations and trained classification models. This copied representation allows consumers to perform simplified spectral analysis at home using portable devices, capturing the essence of laboratory-grade chemical analysis without the complexity and cost of actual chemical instrumentation.
Solution Approach 2:
The patent introduces visual image representations as an intermediary that bridges the gap between complex chemical analysis and consumer-friendly operation. The system converts intricate spectral data into visual formats that can be processed by simple computer vision models, enabling consumers to conduct analysis without needing to understand or handle complex chemical instrumentation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for efficient and accurate classification and recommendation of consumable items, overcoming limitations of traditional methods by utilizing the full spectra data for identification and providing personalized recommendations to consumers.
Implementation Method 1
obtain spectral data representing the consumable item using an infrared spectroscopy system
Data Source
AI summary
Some embodiments of the present disclosure relate to systems and methods including acquiring an image including an indication of at least one wine; identifying, by optical character recognition, the at least one wine in the acquired image; correlating each of the identified at least one wines to a visual image representation of each wine; and executing a trained computer vision classification system using the visual image representation of each of the identified wines and labeled visual image representations of at least one wine associated with a user flavor profile including at least one classification as inputs to generate an output including, for each of the identified wines, an indication of whether the identified wine corresponds to the at least one classification associated with the user flavor profile.