Packet-Based Food Selection UI Using ML User Classification

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Solution Overview

Problem

Existing solutions for selecting alimentary provisioning based on physiological data often limit sources or possible selections, leading to frustration and under-utilization of available options.

Innovation Solution

A system and method using machine learning algorithms to classify user data and set non-alimentary ordering criteria, enabling the generation of alimentary combinations in a packet-based graphical user interface that considers multiple sources and user-specific nutritional requirements.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If existing solutions limit sources or possible selections to simplify selection, then device complexity is reduced, but user satisfaction and utilization decrease

Engineering Contradiction:

Improveease of selectionVSAvoiduser satisfaction

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The system dynamically adjusts the presentation of alimentary options based on user classifications and preferences. The graphical user interface adapts its content and organization according to the user's physiological data and stated preferences, providing a customized selection experience that evolves with user interactions and changing needs.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes multiple parameters simultaneously including user classification categories, preference weights, ordering criteria, and presentation formats. By adjusting these parameters based on machine learning classifications, the system optimizes both the ease of selection and user satisfaction without requiring users to manually configure complex settings.

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If multiple sources and selections are provided without classification, then adaptability increases, but information overload and user frustration increase

Engineering Contradiction:

Improveselection diversityVSAvoidinformation organization

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system segments the diverse alimentary options into organized categories based on user classifications derived from physiological data. By dividing the large set of possible selections into meaningful groups (e.g., by nutrient content, meal type, or user-specific categories), the system maintains selection diversity while making the information manageable and easy to navigate.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The machine learning-based user classification system acts as an intermediary between the diverse alimentary sources and the user. This intermediary processes and structures the information according to user-specific characteristics, transforming raw diverse data into organized, personalized recommendations that reduce cognitive load while preserving options.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Device complexity

If generic alimentary options are provided without personalization, then device complexity is reduced, but nutritional optimization decreases

Engineering Contradiction:

Improvesystem simplicityVSAvoidnutritional accuracy

Core Design Contradiction:

Device complexityVSManufacturing precision

Solution Approach 1:

The system performs preliminary classification of users based on their physiological data and nutritional needs before presenting alimentary options. By pre-processing user data through machine learning algorithms to determine user classifications and preferences, the system prepares personalized criteria in advance, ensuring nutritional accuracy is built into the selection process from the beginning rather than added as a post-processing step.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20260072712A1Methods and systems for providing alimentary combinations in a packet-based graphical user interface generated using distance metrics

Publication Date: 2026.03.12 KPN INNOVATIONS LLC

AI summary

A system for providing alimentary combinations in a packet-based graphical user interface generated using distance metrics is disclosed. The system includes a computing device designed and configured to receive user classification data, wherein the user classification training data includes a plurality of elements correlating user data entries to user set identifiers. The computing device is configured to receive at least an element of user data, train a user classifier as a function of the user classification training data using a machine learning algorithm. The computing device is configured to classify the at least an element of user data to a user set identifier, set a non-alimentary ordering criterion default parameter as a function of the user set identifier, and order an alimentary combination as a function of the non-alimentary ordering criterion. A method of providing alimentary combinations in a packet-based graphical user interface generated using distance metrics is also disclosed.