Non-Invasive Analyte Sensor Database Baseline

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Solution Overview

Problem

Current non-invasive analyte sensors using radio or microwave frequency bands struggle to establish accurate and reliable databases for analyte data, particularly in distinguishing between normal and abnormal conditions over time, due to temporary variations and natural fluctuations.

Innovation Solution

Establishing an analyte database using non-invasive analyte sensors that collect data over a sufficient period to minimize temporary variations, employing spectroscopic techniques across radio or microwave frequency ranges, and updating the database with new data for predictive analysis of conditions in human or animal subjects, materials, and objects.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of time

If analyte data is collected over a short period using non-invasive sensors, then data collection time is reduced, but measurement precision deteriorates due to temporary variations and natural fluctuations

Engineering Contradiction:

Improvedata collection timeVSAvoidanalyte data accuracy

Core Design Contradiction:

Loss of timeVSMeasurement precision

Solution Approach 1:

The system performs preliminary data collection over an extended period to establish a baseline database of analyte levels under normal conditions. This preliminary action creates a reference framework that enables future rapid assessments to be made more accurately by comparing against the established baseline, thus resolving the contradiction between quick data collection and measurement precision.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system changes the temporal parameter of data collection by gathering analyte data over extended periods (weeks or months) rather than short intervals. This parameter change allows the system to capture and average out temporary variations and natural fluctuations, thereby improving measurement precision while the system learns to distinguish between normal variability and actual abnormal conditions.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If analyte data is collected over an extended period to minimize temporary variations, then measurement precision improves, but loss of time increases

Engineering Contradiction:

Improveanalyte data accuracyVSAvoiddata collection time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates a digital copy or model of normal analyte patterns through the established database. Once this reference model is built, the system can use it to quickly assess new data points without requiring extended collection periods, effectively copying the precision benefits of long-term data collection into rapid assessment capabilities.

Inventive Principle:

Principle #26Copying

3Reliability

If a database is established using data from multiple subjects to improve reliability, then generalizability improves, but adaptability to individual targets deteriorates

Engineering Contradiction:

Improvedatabase accuracyVSAvoidtarget-specific precision

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system applies local quality by maintaining both population-level aggregate data and individual-subject-specific data structures. This allows the system to apply general patterns learned from multiple subjects while simultaneously adapting to the unique characteristics of each individual target, resolving the contradiction between generalizability and target-specific precision.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system dynamically adjusts between using population-level patterns and individual-specific patterns based on the amount of available data for each subject. As more data becomes available for a particular subject, the system dynamically shifts toward greater individualization, while maintaining the ability to fall back on population patterns when individual data is limited.

Inventive Principle:

Principle #15Dynamics

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

This approach enables accurate prediction of normal or abnormal conditions by reducing the impact of temporary variations and natural fluctuations, providing reliable insights into medical pathologies, material conditions, or contaminations through continuous data analysis.

Implementation Method 1

employing spectroscopic techniques across radio or microwave frequency ranges

Methodology Applied

Scientific EffectSpectroscopy: Absorption Spectroscopy

Data Source

PatentUS11284820B1Analyte database established using analyte data from a non-invasive analyte sensor

Publication Date: 2022.03.29 LIND GLOBAL FUND II LP

AI summary

Establishing an analyte database using analyte data that has been obtained using one or more non-invasive analyte sensors, and using the analyte database to analyze data obtained using a non-invasive analyte sensor. Once the analyte database is established, the analyte database can be updated with new analyte data, and the analyte database can be used to analyze the new analyte data to derive information from the new analyte data. For example, in the case of a human target, the new analyte data together with the analyte database can be used to predict an actual or possible abnormal medical pathology of the human target.