AI Pet Collar Sensor Fusion for Behavior Inference and Training
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Solution Overview
Problem
Conventional smart pet collars lack the ability to incorporate sensor fusion and machine learning to effectively infer and automate pet behaviors, failing to provide comprehensive insights and training capabilities.
Innovation Solution
An inferential smart pet collar that utilizes sensor fusion and machine learning to infer pet behaviors by combining inertial measurement units with other sensors, leveraging artificial intelligence to build models of pet behaviors and refine them over time, and applying rules to automate training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional smart pet collars use basic sensors and simple tracking features, then device complexity is low, but the ability to infer and automate pet behaviors is insufficient
Solution Approach 1:
The patent combines multiple sensors (accelerometer, gyroscope, magnetometer, GPS, barometer) into a unified sensor fusion system that collects and processes data together to infer pet behaviors, rather than using sensors independently. This merging enables comprehensive behavior analysis while managing system complexity through integrated processing.
Solution Approach 2:
The patent introduces a machine learning behavior engine as an intermediary layer between raw sensor data and behavior inference. This ML engine processes sensor fusion outputs, learns from labeled behavior data, and generates behavior predictions, thereby bridging the gap between simple sensor readings and complex behavior understanding without requiring direct complex rule-based systems.
2Measurement precision
If the collar collects and processes multiple sensor inputs for behavior inference, then measurement precision of pet behaviors improves, but use of energy increases
Solution Approach 1:
The patent implements periodic sampling of sensor data rather than continuous monitoring, where the system collects sensor fusion outputs at defined intervals and processes them batch-wise through the ML engine. This periodic approach maintains behavior detection accuracy by capturing sufficient behavioral patterns while reducing overall power consumption compared to continuous real-time processing.
Solution Approach 2:
The patent performs preliminary processing of sensor data by fusing multiple sensor inputs into consolidated behavior features before feeding them to the ML engine. This preliminary action reduces the computational burden on the energy-constrained device by pre-processing and filtering data, thereby lowering power consumption while maintaining measurement precision.
3Productivity
If the collar uses machine learning models to infer behaviors, then productivity of training automation improves, but device complexity increases
Solution Approach 1:
The patent segments the behavior inference system into distinct functional modules: sensor data collection, sensor fusion processing, ML model inference, and behavior output generation. This segmentation allows each module to be optimized independently and simplifies the overall system architecture, making the ML processing more manageable while maintaining high training automation efficiency.
Solution Approach 2:
The patent implements a self-training mechanism where the system collects labeled behavior data from users and automatically retrains the ML model to improve its accuracy over time. This self-service capability enhances productivity by continuously improving training automation without requiring external intervention, while the modular architecture helps manage the increasing complexity through systematic organization.
Data Source
AI summary
Smart pet collars adopt electronic features, such as wireless fencing, behavior feedback, automatic pet door activation, location tracking, and so on. However, prior smart pet collars do not incorporate multiples of these features, let alone a sensor fusion of data to provide new and improved insights into pet behavior. A sensor fusion of outputs from an IMU combined with other sensor outputs to infer predictable peculiarities of pet behavior using AI. As pet behaviors are largely predictable and repeatable, the presently disclosed technology utilizes ML to build models of sensor outputs that correspond to specific pet behaviors and refine those models over time as additional data becomes available. A software tool backed by an AI model iteratively tunes a training data set of sensor outputs that correspond to pet behaviors to update and optimize the models of sensor outputs to better assess future pet behaviors.