3D-Printed Energy Balance Formulations Using ML Personalization
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Solution Overview
Problem
Current rapid prototyping methods are inadequate in producing complex and effective energy balance formulations for improving energy balance in individuals.
Innovation Solution
A printer system comprising a processor and memory that uses machine-learning to generate an energy rebalancing plan based on user-specific energy quantifiers, which is then used to create an energy balance formulation through additive manufacturing, incorporating ingredients and instructions for energy rebalancing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional rapid prototyping methods are used to produce energy balance formulations, then the manufacturing process is simple, but the formulations lack complexity and effectiveness for personalized energy rebalancing
Solution Approach 1:
The system segments the formulation development process into distinct modules: machine learning model training, energy quantifier analysis, formulation generation, and additive manufacturing. This segmentation allows each component to be optimized independently while maintaining overall system effectiveness for personalized energy rebalancing
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between user energy quantifiers and formulation generation. This intermediary processes complex energy data and translates it into effective formulation parameters, bridging the gap between simple input data and complex effective formulations
2Adaptability or versatility
If machine-learning processes are implemented to generate personalized energy rebalancing plans, then formulation effectiveness is improved, but manufacturing complexity increases
Solution Approach 1:
The additive manufacturing device is designed to perform multiple functions: it can manufacture various types of formulations (energy balancers, weight loss aids, muscle gain promoters) by receiving different digital formulations from the machine learning model, making the system universally applicable to diverse energy rebalancing needs without requiring multiple specialized manufacturing systems
Solution Approach 2:
The system utilizes parameter changes in the machine learning model to adapt formulations based on user-specific energy quantifiers. By adjusting model parameters and formulation compositions dynamically, the system achieves high personalization capability while maintaining a single manufacturing platform
Data Source
AI summary
An apparatus for printing an energy balance formulation, wherein the apparatus includes at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive an energy quantifier related to a user and generate an energy rebalancing plan wherein the energy rebalancing plan identifies an energy balance formulation includes training a machine-learning process using energy training data, wherein the energy training data contains a plurality of inputs containing energy quantifiers correlated to a plurality of outputs containing energy rebalancing plans. The memory containing instructions further configuring the processor to generate the energy rebalancing plan as a function of the machine-learning process and the energy quantifier. The memory containing instructions further configuring the additive manufacturing device to print the energy balance formulation based on the energy rebalancing plan.