Machine-Learning Menu Curation for Real-Time User Personalization
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Solution Overview
Problem
Conventional data analytics techniques fail to provide real-time, granular insights into consumer engagement, leading to skewed decisions and high computational and storage demands, and lack personalized menu recommendations.
Innovation Solution
A system utilizing a trained machine learning model to curate menus dynamically based on individual user data and preferences, reducing storage and computational demands by using localized data processing and real-time feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional data analytics techniques are used to accumulate large datasets for extended time periods, then the reliability of consumer engagement trends is improved, but the storage infrastructure requirements and computational capacity needs increase significantly
Solution Approach 1:
The patent extracts only the essential features and patterns from large datasets using machine learning models, rather than storing and processing the complete raw data. The system extracts consumer engagement patterns, preferences, and trends, storing only these condensed representations which significantly reduce storage requirements while maintaining analytical reliability.
Solution Approach 2:
The patent transforms raw consumer data into transformed parameters through machine learning processing. Instead of storing original transaction records and raw data, the system stores processed features, embeddings, and model parameters that capture the essential information, reducing data volume while preserving analytical value.
2Loss of information
If conventional data analytics techniques process large datasets, then comprehensive consumer insights are obtained, but the processing time increases and real-time analytics capability is reduced
Solution Approach 1:
The patent performs preliminary data processing, feature extraction, and model training in advance to create pre-processed data structures and trained models. This preliminary action enables rapid querying and real-time analytics by avoiding the need to process raw data from scratch during actual analysis requests.
Solution Approach 2:
The patent creates simplified representations and models of consumer data patterns rather than working with the complete original datasets. These copies or representations capture the essential insights while enabling much faster processing speeds suitable for real-time analytics applications.
3Productivity
If conventional data analytics techniques are used, then general consumer trends are identified, but real-time and granular insights into individual user preferences are insufficient
Solution Approach 1:
The patent applies machine learning models to process and analyze data at the individual user level, creating personalized profiles and recommendations. Instead of treating all consumers uniformly, the system processes and stores granular information about individual preferences, behaviors, and patterns, enabling both speed and high measurement precision for each user.
4Loss of information
If large datasets are stored and processed centrally, then comprehensive analytics are available, but data storage resources and computational capacity are consumed excessively
Solution Approach 1:
The patent extracts only the essential information and patterns from datasets using machine learning, storing and processing only these extracted representations rather than the complete raw data. This extraction process significantly reduces the computational capacity and storage resources needed while maintaining the completeness of analytical insights.
Data Source
AI summary
Systems and methods for dynamically curating a menu are disclosed herein. An example system includes one or more processors and a non-transitory computer-readable memory coupled to the processors. The memory may store a trained ML model and instructions thereon that, when executed by the one or more processors, cause the one or more processors to: receive categorical data associated with a user accessing a menu platform; generate, by the trained ML model using the categorical data as inputs, a curated menu for the user, wherein the trained ML model is trained using (i) a set of training categorical data from a plurality of users accessing the menu platform and (ii) a set of training menu items uploaded to the menu platform as inputs to output a set of training curated menus; and transmit a control instruction causing a user computing device to display the curated menu for the user.