Pet Eating Pattern Detection Using Personalized Data Thresholds
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Solution Overview
Problem
Conventional methods for detecting pet eating habits are inadequate as they rely on pet parents' manual tracking, fail to account for subtle eating trends, and do not adapt to individual pet behaviors, making it difficult to detect potential health issues.
Innovation Solution
A system and method that analyzes historical pet eating data to determine an expected distribution with thresholds, compares current eating data to these thresholds, and outputs notifications for changes in eating behavior, using sensors to collect data and adapt to individual pet habits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tracking by pet parents is used, then simplicity and low cost are maintained, but measurement precision and reliability of eating data are insufficient
Solution Approach 1:
The patent replaces manual mechanical tracking methods with electronic sensor-based detection systems. Sensors detect eating events automatically, converting physical eating actions into digital signals for accurate measurement and analysis, thereby improving measurement precision while reducing human error.
Solution Approach 2:
The system enables self-service monitoring where the pet wearing the sensor automatically tracks and reports its own eating behavior without requiring parental intervention. The sensor device independently collects, stores, and transmits eating data, improving reliability while simplifying the user experience.
2Adaptability or versatility
If conventional tracking methods are used, then ease of operation is maintained, but adaptability to individual pet behaviors is poor
Solution Approach 1:
The system dynamically adapts to each pet's unique eating patterns by continuously learning from historical data. The algorithm adjusts baseline expectations and anomaly detection thresholds based on individual pet behavior, enabling personalized monitoring that evolves with the pet's changing habits while requiring minimal user configuration.
Solution Approach 2:
The system implements feedback loops where eating data is continuously collected, analyzed, and used to refine future detection parameters. Pet parents receive notifications about unusual eating patterns, enabling them to observe and adjust to their pet's individual behaviors over time, thereby improving adaptability without increasing operational complexity.
3Loss of information
If manual tracking is used, then device complexity is low, but loss of information about subtle eating trends occurs
Solution Approach 1:
The sensor system continuously monitors eating behavior without interruption, capturing every eating event throughout the day. This continuous data collection preserves complete information about eating patterns, including subtle trends and anomalies that would be missed in manual tracking, while the automated nature maintains low operational burden.
Solution Approach 2:
The system pre-processes and stores detailed eating data in the cloud before parental review. Historical eating records are maintained with full granularity, enabling comprehensive analysis of subtle trends and patterns without requiring parents to manually record or remember details, thereby preserving information completeness while minimizing user effort.
Data Source
AI summary
A computer-implemented method for using historical pet eating data to determine changes in pet eating behavior is disclosed. The method includes receiving a plurality of historical pet eating data records from a database, determining a subset of the plurality of historical pet eating data records, determining an expected distribution based on the subset of the plurality of historical pet eating data records wherein the expected distribution includes a baseline, an upper threshold, and a lower threshold, receiving current pet data from a pet sensor wherein the current pet data includes a total meal event value, analyzing whether the total meal event value exceeds the upper threshold or the lower threshold, and outputting a notification indicating a result that is responsive to the analyzing.