Biofield Sensor Data Correlation with Phenotype Database
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Solution Overview
Problem
Current methods lack the ability to scientifically measure and quantify biofields effectively, and correlate these measurements with actual health conditions of living organisms.
Innovation Solution
A method for correlating biofield sensor data with phenotype and disease history of living organisms, using biofield sensors to measure electromagnetic signals, and storing this data in a database to analyze patterns and predict health outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If biofield measurements are taken using sensitive sensors, then the ability to detect biofield data is improved, but the complexity of the measurement system increases
Solution Approach 1:
The patent introduces a database as an intermediary component that receives, stores, and processes biofield measurement data. This mediator separates the complex sensing hardware from the data analysis functions, allowing the measurement system to focus on detection while the database handles correlation and pattern recognition with phenotype and disease history data.
Solution Approach 2:
The patent replaces manual analysis methods with automated computational algorithms that process biofield data against stored phenotype and disease databases. This substitution of manual interpretation with automated data processing reduces the operational complexity burden on the measurement system while maintaining high measurement precision.
2Measurement precision
If biofield data is correlated with phenotype and disease history, then the accuracy of health assessments is improved, but the complexity of data processing increases
Solution Approach 1:
The patent performs preliminary organization of phenotype and disease history data into a structured database before correlation analysis. By pre-processing and organizing the reference data, the system reduces the complexity of real-time correlation calculations, enabling accurate health assessments through efficient data matching algorithms.
Solution Approach 2:
The patent creates a digital copy of phenotype and disease history data in a database, allowing repeated correlation analyses without reprocessing the original complex data structures. This copying approach simplifies the correlation process by working with standardized database records rather than raw complex data.
3Measurement precision
If multiple biofield scans are stored and analyzed over time, then the precision of pattern recognition is improved, but the volume of data storage requirements increases
Solution Approach 1:
The patent extracts and stores only the essential biofield measurement data and key phenotype information in the database, rather than retaining all raw data. This extraction approach maintains the necessary information for pattern recognition while significantly reducing storage requirements by discarding redundant or non-essential data.
Solution Approach 2:
The patent segments the data into distinct categories (biofield measurements, phenotype data, disease history) and stores them in organized database structures. This segmentation allows efficient retrieval and processing of specific data types, reducing the effective storage volume needed while maintaining comprehensive data availability for analysis.
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 method allows for the identification of correlations between biofield scans and phenotype data, improving the precision and accuracy of health assessments over time.
Implementation Method 1
A biofield sensor, such as, for example, an electron tunneling putative energy analyzer, electron avalanche putative field analyzer, or any other type of sensor that can detect an organism's biofield, can measure biofield data
Data Source
AI summary
Systems and methods are provided for identifying characteristics of a subject using a biofield scan obtained from the subject. An embodiment can include a method for cross-correlating biofield scans to an enome database, and/or a genome database. A phenotype history and a biofield scan can be created from a user. A user's biofield scan can be created from measured amplitude and frequency. A database is created from a user's phenotype history, and biofield scan. The user's phenotype history and biofield scans are then correlated with known physical and biochemical characteristics. A biofield signature is created and compared to the user's phenotype history, and biofield scan.