AI Biosketch Analysis for Biological Outcome Prediction
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Solution Overview
Problem
The complexity and variability of biological data make it challenging to consistently apply analytical techniques, as numerous subtle yet crucial factors vary significantly between subjects, complicating the analysis of biological data.
Innovation Solution
A system and method using artificial intelligence and machine learning processes to receive and analyze multiple dimensions of biological extraction data, generating a dimensional history of a user, which is then used to determine biological outcomes by training machine learning models with training data and correlating biosketch measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional analytical techniques are applied to biological data, then analysis can be performed, but the complexity and variability of biological data between subjects frustrates consistent application
Solution Approach 1:
The patent transforms biological data into standardized numerical parameters through machine learning models. Multiple dimensions of biological data (genomics, proteomics, metabolomics, etc.) are converted into quantifiable measurements that can be consistently processed, changing the state of raw biological data into standardized parameters suitable for analytical techniques.
Solution Approach 2:
The patent introduces machine learning models as intermediary systems between raw biological data and analytical techniques. These models act as mediators that process the complex, variable biological data and output standardized results, enabling consistent application of analytical methods across different subjects despite inherent biological variability.
2Measurement precision
If multiple dimensions of biological data are collected to improve analysis accuracy, then measurement precision improves, but data complexity and processing difficulty increase
Solution Approach 1:
The patent segments the complex biological data analysis into distinct processing stages handled by different machine learning models. The first model processes multiple dimensions of biological data separately, the second model integrates these processed dimensions, and the third model generates final outcomes. This segmentation reduces processing difficulty while maintaining measurement precision.
Solution Approach 2:
The patent adds a temporal dimension to biological data analysis by generating a 'dimensional history' that tracks changes in biological parameters over time. This transforms static cross-sectional data into dynamic longitudinal data, improving measurement precision by capturing biological variability across time points while using machine learning to manage the increased data dimensionality.
3Reliability
If machine learning processes are used to handle biological data variability, then analysis reliability improves, but computational complexity increases
Solution Approach 1:
The patent employs universal machine learning models that can process multiple types of biological data across different subjects and time points. These models are trained on diverse training data to learn general patterns of biological variability, enabling them to handle various data dimensions (genomics, proteomics, metabolomics) and different subject characteristics with a single system architecture, improving reliability while managing complexity.
Data Source
AI summary
A system for determining a plurality of biological outcomes using a plurality of dimensions of biological extraction user data and artificial intelligence, the system including a computing device configured to receive, a plurality of dimensions of biological extraction data, generate, a first machine learning process comprising a plurality of biosektch measurements wherein the first machine learning process is trained as a function of training data to output a dimensional history of a user as a function of biological extraction data, output as a function of the plurality of dimensions of biological extraction data and the first machine learning process, the dimensional history of the user; and determine, using a second machine learning process and the dimensional history of the user, at least a biological outcome associated with the user.