Pet Bowl Load Sensing for Subtle Feeding Behavior Changes
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Solution Overview
Problem
Existing pet feeding monitoring systems fail to provide adequate information for early detection of health issues, as they typically only track basic consumption data and lack the sensitivity to identify subtle changes in eating and drinking behaviors.
Innovation Solution
A smart pet bowl equipped with a load sensor and processor that can detect feeding behaviors with high sensitivity and sample rates, identifying count-based and duration-based behaviors, and optionally using secondary sensors to differentiate between pets in multi-pet households, providing real-time health insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If basic monitoring devices (cameras, scales) are used to track pet feeding behaviors, then some basic information (amount of food or water consumed, time of consumption) can be obtained, but these devices provide inadequate information to assess small changes in eating and drinking behavior that may give clues to animal health
Solution Approach 1:
The patent replaces traditional mechanical monitoring devices (cameras, scales) with a load sensor system that measures forces applied to the bowl during feeding. This substitution enables detection of subtle behavioral changes through precise force measurements, including duration, frequency, and intensity of feeding actions, thereby resolving the contradiction between measurement precision and information completeness.
2Loss of time
If visual indicators are used to monitor animal eating or drinking behavior, then information about animal health can be provided, but these indicators typically become visually noticeable only at mid- to late-stages of disease and do not provide enough information for correct intervention
Solution Approach 1:
The system continuously monitors feeding behavior parameters (duration, frequency, force) and provides real-time feedback to pet owners through notifications. This feedback mechanism enables early detection of health issues by tracking deviations from normal feeding patterns, allowing timely intervention before visual symptoms appear, thus resolving the time loss contradiction.
3Loss of information
If traditional monitoring methods are used, then basic consumption data can be tracked, but custodians of animals often lack the animal behavioral knowledge to associate eating or drinking behaviors with health issues
Solution Approach 1:
The system automatically analyzes feeding behavior data and generates health insights without requiring expert interpretation. The processor evaluates monitored parameters against established behavioral patterns and provides ready-to-interpret notifications to pet owners, enabling them to understand health implications without specialized knowledge, thus resolving the information loss contradiction.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides early indicators of potential health conditions, including physical, behavioral, and mental health issues, allowing for proactive notifications to pet owners and veterinarians.
Implementation Method 1
obtain load data from a load sensor of a pet bowl while the pet is interacting with contents of the pet bowl
Data Source
AI summary
The present disclosure relates to the monitoring feeding behavior(s) of a pet(s), under the control of at least one processor. An example system includes a pet bowl comprising a load sensor configured to obtain load data while the pet is interacting with contents of the pet bowl. The load sensor can have a sensitivity of +/â50 grams or less and the load data can occur at a sample rate from 10 samples to 150 samples per second. The system also includes a processor configured to sequentially group the load data in 0.01 second to 5 second time increments. The individual time increments can include multiple samples. The processor can also identify a feeding behavior occurring within one or more of the time increments based on the load data resulting from the pet interacting with the pet bowl or the contents of the pet bowl.