Pet Emotion Detection via Breed-Specific Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for detecting pet emotions are challenging for pet owners, as they lack expertise in evaluating pet characteristics and do not consider breed-specific traits, leading to inaccurate assessments and potential negative impacts on pet welfare.
Innovation Solution
A system and method using machine-learning models to detect pet emotions from video data, which includes receiving image data, detecting pet outlines, analyzing pet attributes, and displaying detected emotions on a user interface, while also considering breed-specific characteristics for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pet owners use conventional methods to evaluate pet characteristics, then they can attempt to determine pet emotions, but the accuracy is poor due to lack of expertise and breed-specific knowledge
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between the pet owner and the pet emotion assessment. The model processes visual data of the pet and automatically determines emotions, eliminating the need for the owner to have expertise in evaluating pet characteristics. This mediator handles the complex analysis while the owner simply provides input data.
Solution Approach 2:
The patent replaces the manual mechanical process of evaluating pet characteristics with an automated machine learning system. Instead of requiring the owner to visually assess and interpret pet body language and physical traits, the system uses computational algorithms to analyze images and determine emotions automatically.
2Reliability
If pet owners manually evaluate pet characteristics without breed-specific knowledge, then they can assess pet emotions, but the reliability is reduced due to lack of expertise
Solution Approach 1:
The patent changes the parameters used for emotion detection from general physical characteristics to breed-specific traits. The machine learning model is trained to recognize emotions through characteristics specific to different dog breeds, such as ear position, tail carriage, and facial expressions that are breed-specific. This parameter change improves reliability by using appropriate evaluation criteria for each breed.
Solution Approach 2:
The patent performs preliminary training of the machine learning model on extensive datasets of pet images with labeled emotions and breed information. This preliminary action prepares the model to accurately recognize breed-specific emotional expressions before it is used for actual emotion detection, ensuring high reliability without requiring the user to have breed knowledge.
3Loss of time
If conventional evaluation methods are used, then pet owners can assess emotions, but time consumption increases due to lack of expertise requiring thorough evaluation
Solution Approach 1:
The patent substitutes the time-consuming manual evaluation process with automated image processing. The machine learning model quickly analyzes pet images and determines emotions in seconds, eliminating the need for the owner to spend time carefully observing and interpreting various physical characteristics and behaviors.
Solution Approach 2:
The system performs self-service by automatically analyzing pet emotions without requiring the owner's expertise or time investment. The machine learning model independently processes the evaluation, making the system self-sufficient and eliminating the trade-off between time investment and accuracy that plagues manual methods.
Data Source
AI summary
A computer-implemented method for detecting one or more emotions of one or more pets is disclosed. The method receiving, by one or more processors, image data from at least one user device, wherein the image data includes one or more frames, detecting, by the one or more processors, at least one pet outline that includes at least one pet in the one or more frames, detecting, by the one or more processors, one or more emotions of the at least one pet based on the at least one pet outline; and displaying, by the one or more processors, the one or more emotions on at least one user interface of a user device.