Machine Learning Model for Animal Age Prediction
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Solution Overview
Problem
Conventional computers are unable to efficiently analyze the complex oral microbiome of animals due to their computationally intensive nature, leading to resource constraints that limit system performance and throughput.
Innovation Solution
A machine learning model is trained to predict an animal's age based on input data including oral microbiome information, health, and physical attributes, offloading complexity and improving resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional computers are used to analyze the oral microbiome, then the analysis can be performed, but the system consumes excessive computational resources and has reduced throughput
Solution Approach 1:
The patent extracts the complex computational analysis task from the conventional computer system and transfers it to a specialized machine learning model. The machine learning model is pre-trained on extensive microbiome data, allowing it to perform age prediction efficiently without consuming excessive computational resources during inference.
Solution Approach 2:
The machine learning model serves as an intermediary between the raw microbiome data and the age prediction output. It processes the complex microbial composition data and transforms it into meaningful age estimates, thereby mediating the computational burden and enabling efficient analysis.
2Reliability
If conventional computers perform computationally intensive tasks, then the tasks can be completed, but the number of available resources is reduced and task completion time increases
Solution Approach 1:
The machine learning model is pre-trained in advance using extensive microbiome data from animals of known ages. This preliminary training allows the model to capture complex patterns and relationships between microbial composition and age, so that when new data is input, the model can make rapid predictions without requiring time-consuming computational analysis.
Solution Approach 2:
The patent creates a computational copy of the complex analysis capability through the machine learning model. Instead of using conventional computers to perform the full analysis in real-time, the trained model serves as a copy that can make predictions quickly and efficiently, thereby reducing task completion time while maintaining accuracy.
3Productivity
If the oral microbiome analysis is performed using conventional methods, then the analysis can be conducted, but the system complexity increases and performance decreases
Solution Approach 1:
The patent extracts the complex pattern recognition and age prediction functionality from the overall system and encapsulates it in a dedicated machine learning model. This separation allows the rest of the system to remain simpler while the model handles the computational complexity internally through its trained parameters and architecture.
Data Source
AI summary
A device that is configured to obtain input data for an animal that is a member of the canid family is provided herein. The input data includes a first array having a first plurality of entries, where each entry within the first plurality of entries contains a numerical value that indicates an amount of a type of bacteria that is present within a sample from the animal. The device is further configured to input the input data for the animal into a machine learning model that is configured to receive the input data for the animal and to output an animal age value based at least in part on the input data for the animal. The animal age value identifies a predicted age for the animal. The device is further configured to obtain the animal age value from the machine learning model and to output the animal age value.