PDM Generator for Multi-Guest Dietary Preference Planning
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Solution Overview
Problem
Existing tools fail to accommodate multiple guests' food allergies, dietary restrictions, and preferences in meal planning, complicating hosting events and failing to provide suitable spaces or locations for such events.
Innovation Solution
A preference differential menu (PDM) generator and preparation tool that aggregates host and guest preferences using preference differential analytics to generate a dish matrix array, which is used to create a personalized menu considering allergies, dietary restrictions, and preferences, and is supported by a building equipped with necessary equipment and accessories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing tools are used to create personal profiles for allergies or dietary restrictions, then individual restriction analysis is provided, but the ability to construct meals for multiple people accounting for both restrictions and preferences is lost
Solution Approach 1:
The system segments the meal planning process into distinct analytical phases: restriction analysis (identifying what must be excluded) and preference analysis (identifying what should be included). This segmentation allows the complex task of multi-person meal construction to be broken down into manageable steps, each handled by specialized analytical modules that work together to produce the final menu.
Solution Approach 2:
The system merges restriction-based filtering with preference-based selection into a unified meal construction process. By combining the subtraction analysis (removing restricted items) with addition analysis (adding preferred items) within a single integrated framework, the system achieves comprehensive multi-person meal planning that neither approach could accomplish alone.
2Reliability
If hosts manually determine and balance food restrictions for multiple guests, then dietary requirements are met, but the complexity and difficulty of meal planning increases
Solution Approach 1:
The system performs self-service analysis by automatically processing guest restriction and preference data to generate compliant meal recommendations. The analytical algorithms independently evaluate dietary requirements and generate appropriate menu items without requiring manual intervention, thereby maintaining reliability while dramatically reducing the operational burden on hosts.
Solution Approach 2:
The system incorporates feedback mechanisms where guest restriction and preference data serves as input that automatically adjusts meal recommendations. The analysis continuously refines menu selections based on the provided constraints and preferences, ensuring dietary compliance while simplifying the planning process through automated iterative optimization.
3Productivity
If hosts cook large quantities of food to accommodate all guests, then all guests are fed, but food waste increases and cooking difficulty increases
Solution Approach 1:
The system applies partial action by calculating and preparing only the necessary quantity of each dish based on the specific restriction and preference profile of each guest. Rather than preparing excessive amounts to cover all possibilities, the analysis determines the precise minimum requirements, reducing both food waste and cooking complexity while ensuring every guest receives appropriate meals.
Data Source
AI summary
The present invention is directed to methods and tools that offer a person the ability to aggregate the allergies and dietary restrictions, as well as the predilections and disinclinations in generating a menu for multiple people invited to a meal, or an event where food would be served. Moreover, the present invention is further directed to spaces or locations capable of hosting such methods and tools, equipped with the appropriate equipment and accessories.