Therapeutic Remedy Calculation via Machine Learning

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Solution Overview

Problem

Locating effective therapeutic remedies for individuals with shared medical conditions is challenging due to the overwhelming quantity of literature and the constant creation of new remedies and conditions.

Innovation Solution

A device and method that utilize a sensor and computing device to record user vibrancy data, identify a therapeutic remedy instruction set through machine learning processes, and calculate a therapeutic remedy result by associating user data with a therapy response curve, while also considering nutritional impacts.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If therapeutic professionals manually evaluate literature to locate effective remedies, then they can identify suitable treatments, but the process becomes overwhelming and time-consuming due to the quantity of literature

Engineering Contradiction:

Improveaccuracy of remedy identificationVSAvoidtime to evaluate literature

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces the manual mechanical evaluation process with an automated machine learning system. The computing device uses trained machine learning models to automatically analyze user data, compare it against therapeutic remedy databases, and identify effective remedies without requiring manual literature evaluation by professionals.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service by allowing the machine learning algorithm to autonomously perform the remedy identification task. The computing device automatically processes user data, retrieves relevant information from databases, and generates treatment recommendations without requiring continuous human intervention in the evaluation process.

Inventive Principle:

Principle #25Self-service

2Adaptability or versatility

If therapeutic professionals manually locate and evaluate therapeutic remedies, then they can provide treatment recommendations, but the process is complex and difficult due to the constant creation of new remedies and conditions

Engineering Contradiction:

Improveability to handle new remedies and conditionsVSAvoidcomplexity of remedy evaluation process

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements a dynamic system where the machine learning models are continuously trained and updated with new therapeutic remedy data and medical conditions. The computing device adapts to new information by retraining algorithms, ensuring the system remains current with constantly evolving medical knowledge without increasing operational complexity for users.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The machine learning system serves multiple functions: it identifies therapeutic remedies, analyzes user data, compares nutritional responses, and provides treatment recommendations. This multi-functional approach consolidates what would otherwise require multiple separate evaluation processes into a single unified system.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Reliability

If a comprehensive analysis of user data and nutritional responses is performed to calculate therapeutic remedy results, then treatment effectiveness can be improved, but the computational process becomes more complex

Engineering Contradiction:

Improveaccuracy of therapeutic remedy resultVSAvoidcomplexity of calculation process

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent segments the comprehensive analysis into distinct computational modules: user data processing, therapeutic remedy matching, nutritional response analysis, and result calculation. Each module handles a specific aspect of the analysis independently, then integrates results to provide the final therapeutic recommendation, reducing overall system complexity.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The machine learning algorithm acts as an intermediary that manages the complex calculations between raw user data and therapeutic recommendations. The computing device uses the machine learning model to bridge the gap between comprehensive data analysis and simplified treatment outcomes, handling computational complexity internally while presenting clear results to users.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20230197244A1Device and methods of calculating a therapeutic remedy result

Publication Date: 2023.06.22 KPN INNOVATIONS LLC

AI summary

A device for calculating a therapeutic remedy result, the device including a display; a sensor; and a computing device in communication with the display and the sensor, wherein the computing device is configured to record a user vibrancy datum; identify a therapeutic remedy instruction set as a function of the user vibrancy datum, wherein the therapeutic remedy instruction set comprises a therapeutic remedy; and calculate a therapeutic remedy result that associates the user vibrancy datum and the therapeutic remedy with a therapy response curve.