Autonomous Animal Training System with Video-Based Protocol Selection

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Solution Overview

Problem

Current animal training methods lack autonomy and adaptability, failing to effectively select and execute training protocols that cater to individual animal behavior and anxiety levels, leading to inefficient training sessions and potential stress for the animals.

Innovation Solution

A method involving a training apparatus and computer system that autonomously selects and executes training protocols based on animal behavior, calculates training scores, and adjusts protocols to reduce anxiety, incorporating video feeds, reinforcers, and manual training sessions to tailor training to each animal's progress and comfort.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If traditional manual training methods are used, then the trainer can directly observe and adjust training protocols, but the training process lacks autonomy and cannot continuously adapt to individual animal behavior patterns

Engineering Contradiction:

Improveautonomy in selecting and executing training protocolsVSAvoidcomplexity of training system

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The training system autonomously selects and executes training protocols without continuous human intervention. The system services itself by automatically analyzing animal behavior data, calculating training scores, and adjusting protocols based on individual animal progress and anxiety levels, thereby resolving the contradiction between automation and complexity through self-managing capabilities.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system continuously monitors animal behavior during training sessions, calculates training scores based on performance metrics, and uses this feedback to automatically adjust subsequent training protocols. This closed-loop feedback mechanism enables autonomous adaptation while managing system complexity through algorithmic decision-making rather than human intervention.

Inventive Principle:

Principle #23Feedback

2Adaptability or versatility

If standardized training protocols are used, then training can be efficiently delivered, but the training cannot be personalized to individual animal behavior and anxiety levels

Engineering Contradiction:

Improvepersonalization of training to individual animalVSAvoidtraining efficiency

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system applies different training protocol parameters tailored to each individual animal's behavior patterns, anxiety levels, and progress. By customizing training intensity, duration, and type based on local (individual) characteristics rather than applying uniform standardized protocols, the system achieves personalization while maintaining efficiency through automated decision-making.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The training protocols dynamically adjust based on real-time animal performance data and calculated training scores. The system continuously modifies training parameters to match individual animal needs, transforming static standardized protocols into dynamic, adaptive training sequences that maintain efficiency through automated adjustment rather than manual customization.

Inventive Principle:

Principle #15Dynamics

3Productivity

If training intensity is increased to improve proficiency, then training outcomes improve, but animal anxiety and stress increase

Engineering Contradiction:

Improvetraining proficiency improvementVSAvoidanimal anxiety and stress

Core Design Contradiction:

ProductivityVSObject-affected harmful factors

Solution Approach 1:

The system monitors animal behavior indicators of anxiety and stress during training sessions, calculates training scores that reflect both performance and welfare, and uses this feedback to automatically adjust training intensity. When anxiety indicators exceed thresholds, the system reduces intensity while maintaining proficiency improvement through optimized protocol selection, thereby resolving the contradiction between productivity and animal welfare.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system changes training parameters such as duration, intensity, and frequency based on calculated training scores and observed animal behavior. By dynamically adjusting these parameters to optimize the balance between proficiency improvement and anxiety reduction, the system achieves productive training outcomes while minimizing harmful stress effects through data-driven parameter optimization.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11547088B2System and method for selecting and executing training protocols for autonomously training an animal

Publication Date: 2023.01.10 COMPANION LABS INC

AI summary

One variation of a method for autonomously training an animal includes: loading an autonomous training protocol for the animal onto a training apparatus configured to dispense units of a primary reinforcer responsive to behaviors performed by the animal; during an autonomous training session for the animal, accessing a video feed of a working field near the training apparatus, detecting the animal in the video feed, and executing the first autonomous training protocol; calculating a training score for the autonomous training session based on behaviors performed by the animal during the autonomous training session; selecting a manual training protocol, from a set of manual training protocols, based on the training score; generating a prompt to execute the first manual training protocol with the animal during a first manual training session; and transmitting the prompt to a user associated with the animal.