Personalized Restaurant Menu System Using Machine Learning Translation
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Solution Overview
Problem
Existing technologies fail to effectively personalize restaurant menus based on user preferences, particularly for individuals who do not understand the language or currency of the menu, and they pose privacy risks by requiring personal information to determine user preferences.
Innovation Solution
A method and system that use machine learning models, including convolutional neural networks (CNN) and recurrent neural networks (RNN), to translate and personalize restaurant menus based on user preferences received from devices, without requiring personal information, and to convert currencies and languages dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to translate menu content, then language translation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components between the menu content and the user. These models act as translators that convert text from one language to another, improving translation accuracy while managing system complexity through modular integration of pre-trained models.
Solution Approach 2:
The system uses pre-trained machine learning models that have been copied from external sources or previously trained environments. This allows the system to leverage existing translation capabilities without having to train models from scratch, thereby improving translation accuracy while reducing the computational burden and complexity of the current system.
2Measurement precision
If personal information is collected to determine user preferences, then personalization accuracy is improved, but privacy risk increases
Solution Approach 1:
The patent extracts only the necessary preference information from user data while leaving out sensitive personal information. The system determines user preferences through indirect methods such as analyzing device characteristics, browsing behavior, and contextual information, thereby improving personalization accuracy without collecting unnecessary personal data that would increase privacy risks.
Solution Approach 2:
The system enables users to self-configure their privacy settings and preference parameters without requiring the system to collect and process sensitive personal information. Users can manually input their preferences or allow the system to infer them from non-sensitive behavioral data, thus achieving personalization while maintaining privacy.
3Adaptability or versatility
If menu content is converted to graphical content for device rendering, then user interface adaptability is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary conversion of menu content into multiple graphical formats and resolutions in advance. The system pre-processes the menu data into various formats that can be directly rendered on different devices, improving device compatibility while reducing the time required for real-time conversion during actual menu display.
Solution Approach 2:
The system creates multiple copies of the menu content in different graphical formats and resolutions. These pre-generated copies are stored and selectively delivered to devices based on their specific rendering capabilities, thereby improving device compatibility without requiring time-consuming real-time conversion for each device.
4Ease of operation
If currency conversion is performed dynamically, then user experience is improved, but computational resources increase
Solution Approach 1:
The patent implements localized currency conversion by detecting the user's location and applying the appropriate currency conversion rates only to relevant menu items in that specific context. The system converts currencies dynamically based on local conditions such as geographic location and device settings, improving user experience while minimizing unnecessary computational resource consumption by avoiding global or unnecessary conversions.
Data Source
AI summary
Disclosed herein is a method for personalizing menus of restaurants based on preferences, in accordance with some embodiments. Accordingly, the method includes receiving at least one request associated with at least one user from at least one device, obtaining at least one menu associated with at least one restaurant based on the at least one request, receiving at least one preference from at least one device, translating the at least one content associated with the at least one dish based on the at least one preference by implementing at least one machine learning model, generating at least one personalized menu of the at least one restaurant for the at least one user based on the translating, transmitting the at least one personalized menu to the at least one device, and storing the at least one machine learning model.