3D-Printed Energy Formulation for Personalized Rebalancing
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Solution Overview
Problem
Existing manufacturing formulations using rapid prototyping are inadequate in producing complex and effective energy balance formulations for patients.
Innovation Solution
A printer equipped with a processor and memory, capable of receiving an energy quantifier, identifying body areas with insufficient life energy, generating an energy rebalancing plan, and printing an energy balance formulation using additive manufacturing, which includes identifying ingredients, determining side effects, and transmitting the formulation to specific body areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional rapid prototyping manufacturing formulations are used, then manufacturing complexity is reduced, but the effectiveness and precision of energy balance formulations for patients deteriorates
Solution Approach 1:
The manufacturing system is segmented into distinct functional modules: energy quantifier reception module, body area identification module, energy rebalancing plan generation module, ingredient identification module, side effect determination module, and additive manufacturing module. This segmentation allows each module to specialize in specific tasks, improving overall manufacturing precision while managing complexity through modular architecture.
Solution Approach 2:
The system performs self-assessment by receiving energy quantifiers directly from patient data, automatically identifying body areas with insufficient life energy, and generating personalized energy rebalancing plans without requiring external manual formulation, thereby achieving high precision through automated self-service manufacturing.
2Reliability
If personalized energy balance formulations are created for each patient, then treatment effectiveness improves, but manufacturing time and complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-establishing the energy quantifier reception framework, pre-programming the body area identification algorithms, and pre-configuring the energy rebalancing plan generation logic. These preliminary preparations enable rapid personalized formulation creation when patient data is received, reducing actual manufacturing time while maintaining high effectiveness.
Solution Approach 2:
The system achieves personalized formulations by dynamically changing parameters such as energy quantifier values, body area locations, ingredient quantities, and formulation concentrations based on individual patient data, allowing rapid adaptation to each patient's specific needs without requiring complete reformulation of the manufacturing process.
3Reliability
If comprehensive ingredient analysis and side effect determination are performed, then formulation safety improves, but processing complexity increases
Solution Approach 1:
The ingredient identification and side effect determination functions are merged into a single integrated processing module within the energy rebalancing plan generation system. This merging allows simultaneous execution of both analyses, improving formulation safety through comprehensive evaluation while reducing overall processing complexity by eliminating the need for separate independent systems.
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 contains 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 contains instructions further configuring the additive manufacturing device to print the energy balance formulation based on the energy rebalancing plan.