Expert System Meal Suggestion Personalization

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing systems for maintaining healthy lifestyle habits, particularly diet and exercise regimes, are often restrictive, expensive, and inaccessible, leading to user frustration and dropout, as they fail to provide personalized guidance and variety, making it difficult for individuals to sustain healthy habits.

Innovation Solution

A system and method using a computer-based expert system that generates and pushes tailored meal suggestions to individual users via cellular technologies, based on their personal preferences, allowing for interactive feedback and adaptation to user behavior, providing personalized guidance and motivation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If a computer-based expert system generates personalized meal suggestions using user preferences and feedback, then user satisfaction and adherence to diet goals improve, but system complexity and data processing requirements increase

Engineering Contradiction:

Improveuser adherence to diet goalsVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system segments the complex task of meal planning into distinct components: user profile creation, preference selection, meal suggestion generation, feedback collection, and recommendation adjustment. Each component handles a specific aspect of the diet guidance process, making the overall system more manageable and reliable

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system implements continuous feedback loops where user responses to meal suggestions (acceptance, rejection, modifications) are collected and used to refine future recommendations. This feedback mechanism progressively improves personalization accuracy and user adherence without requiring complete system redesign

Inventive Principle:

Principle #23Feedback

Solution Approach 3:

The system performs preliminary actions by collecting user preferences, dietary restrictions, and personal information during initial setup and ongoing interactions. This pre-collected data is stored and used to generate personalized meal suggestions, reducing the need for complex real-time decision-making

Inventive Principle:

Principle #10Preliminary action

2Adaptability or versatility

If the system provides interactive feedback and adapts to user behavior, then personalization and user satisfaction increase, but information processing requirements and system resources increase

Engineering Contradiction:

ImprovepersonalizationVSAvoidinformation processing requirements

Core Design Contradiction:

Adaptability or versatilityVSLoss of information

Solution Approach 1:

The system applies local quality by focusing information processing on specific user attributes and preferences rather than analyzing all possible data. It collects and processes only the most relevant information for meal suggestions, such as dietary restrictions, preferred cuisines, and feedback patterns, reducing overall information processing requirements while maintaining high personalization

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system uses partial action by implementing personalization progressively rather than all at once. It starts with basic user preferences and gradually incorporates additional feedback and behavioral data as users interact with the system, allowing adaptation without overwhelming information processing demands

Inventive Principle:

Principle #16Partial or excessive action

3Duration of action of stationary object

If meal suggestions are tailored to individual preferences and behaviors, then user frustration decreases and regime sustainability improves, but system complexity and development costs increase

Engineering Contradiction:

Improveregime sustainabilityVSAvoidsystem complexity

Core Design Contradiction:

Duration of action of stationary objectVSDevice complexity

Solution Approach 1:

The system implements dynamics by making meal suggestions adaptive and evolving rather than static. Recommendations change based on user feedback, behavioral patterns, and preference updates, allowing the system to sustain user engagement over time without requiring complete redesign or high complexity

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system applies self-service by enabling users to provide their own feedback and preference information, which the system then uses to automatically generate and adjust recommendations. This reduces the need for complex external intervention or manual customization while improving regime sustainability

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS8706525B2Method and system for suggesting meals based on tastes and preferences of individual users

Publication Date: 2014.04.22 HUMANA INNOVATIONS ENTERPRISES

AI summary

A system and method for generating meal suggestion messages using an expert system and then pushing those suggestions to users. Meal suggestions are tailored to users based on their tastes and preferences. Users specify preferences related to a diet plan, food preferences, meal time preferences, and meal preparation preferences. An expert system considers each user's preferences and nutritional data to generate meal suggestion messages consistent with the user's preferences and dietary goals. Meal suggestions are pushed to the user according to the user's preferred time for eating each meal. The user can accept or reject the suggested meal or one or more foods within a suggested meal. For rejected meal suggestions, a meal substitution message is generated and sent. The expert system uses accepted and rejected meal suggestions to determine if certain foods or entire meals should no longer be recommended to individual users or to system users as a whole.