Adaptive Animal Training System Using Machine Learning
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Solution Overview
Problem
Existing animal training methods are inefficient and lack consistency, requiring significant time and human intervention, and are limited in their ability to adapt to changing animal behaviors.
Innovation Solution
A computer-implemented method and system using machine learning (ML) to automatically and adaptively train animal behavior, by receiving user configuration and feedback, sensor inputs, and determining a mapping between inputs and training actions to apply.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional animal training methods are used, then training can be performed with simple methods, but training efficiency is low and requires significant time and human intervention
Solution Approach 1:
The system enables automated animal training by having the animal interact with sensors and receive feedback automatically through a computing device, eliminating the need for continuous human intervention. The machine learning model autonomously processes sensor data and determines appropriate training actions, allowing the training process to serve itself without constant human oversight.
Solution Approach 2:
The patent replaces traditional mechanical/human-based training methods with an automated computational system. Sensors capture animal behavior data, which is processed by a machine learning model on a computing device that automatically determines and delivers training feedback, substituting human judgment and manual intervention with algorithmic decision-making.
2Reliability
If traditional animal training methods are used, then training can be performed with simple approaches, but consistency in training is lacking
Solution Approach 1:
The system continuously monitors animal behavior through sensors and provides immediate feedback based on machine learning model predictions. The system tracks training progress and adjusts feedback delivery consistently according to predefined criteria and model predictions, ensuring uniform application of training principles across different sessions and conditions.
Solution Approach 2:
The computing device serves multiple functions: it collects data from various sensors, processes information through the machine learning model, delivers training feedback through multiple output modalities, and maintains training records. This multi-functional system ensures consistent training delivery while reducing the need for multiple separate training tools.
3Adaptability or versatility
If traditional animal training methods are used, then training can be performed without advanced technology, but the ability to adapt to changing animal behaviors is limited
Solution Approach 1:
The machine learning model dynamically adapts to changing animal behaviors by continuously learning from new sensor data and updating its predictions accordingly. The system adjusts its understanding of animal behavior patterns in real-time, allowing training feedback to remain relevant and effective as the animal develops and changes over time.
Solution Approach 2:
The system performs preliminary analysis of sensor data through the machine learning model to predict upcoming animal behaviors or needs. By anticipating behavioral changes before they fully manifest, the system can prepare and deliver appropriate training feedback proactively, adapting to the animal's evolving patterns ahead of time.
Data Source
AI summary
A computer-implemented system and method operate an automatic and adaptive animal behavioral system. The system receives user configuration and feedback, and generates a training set based on online animal behavior patterns and received user feedback. The system trains one or multiple models based on the training set, if the training set is sufficiently large. It then validates the models, and provides system generated classifications and action types and levels for animal's behavior patterns, if the models' accuracy rate is above a threshold.