Pet Check-In Kiosk Imaging for Reliable AI Identification
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Solution Overview
Problem
Conventional animal recognition systems are static and inaccurate, particularly in real-world settings, and individuals without veterinary expertise face challenges in selecting appropriate solutions for their animals due to the overwhelming number of alternatives.
Innovation Solution
A check-in kiosk equipped with a camera device having an arm to hold an object, such as a treat, ensures stable and oriented pet images for accurate facial recognition, coupled with machine learning to identify animals uniquely and generate personalized recommendations based on veterinary records, and augmented reality to navigate to item locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional animal recognition systems are used, then the system structure is simple, but the identification accuracy is poor and reliability is low
Solution Approach 1:
The system segments the animal recognition task into multiple components: image capture device, machine learning model for identification, and recommendation system. This segmentation allows each component to be optimized independently while working together to achieve high accuracy without overwhelming complexity.
Solution Approach 2:
The patent introduces an intermediary machine learning model that bridges the gap between simple image capture and complex recommendation generation. This intermediary layer processes visual data and translates it into actionable identification results and personalized recommendations, improving accuracy while managing system complexity.
2Reliability
If static animal recognition methods are used, then the system is easy to implement, but the identification is inaccurate in real-world settings
Solution Approach 1:
The system transitions from static recognition to dynamic interaction by using a camera device with an adjustable arm that can actively position treats to guide animal gaze. This dynamic approach ensures the animal faces the camera correctly, dramatically improving identification reliability in real-world settings while maintaining ease of use through automated positioning.
Solution Approach 2:
The system incorporates feedback loops where the camera captures images, the machine learning model processes them, and the system uses this information to generate recommendations. This continuous feedback mechanism ensures reliable identification by constantly refining the recognition process based on actual animal appearance and context.
3Adaptability or versatility
If individuals without veterinary expertise try to select appropriate solutions, then they can make selections, but they are overwhelmed by the number of alternatives
Solution Approach 1:
The system performs self-service by automatically generating personalized recommendations based on the animal's visual identification and characteristics. The machine learning model and recommendation engine work autonomously to analyze the animal's features and suggest appropriate products, eliminating the need for users to manually search through numerous alternatives while maintaining high adaptability and personalization.
Data Source
AI summary
The present disclosure provides techniques for improved animal recognition and recommendations. One or more images of an animal associated with a user are captured at a check-in kiosk of an enterprise. A unique identifier of the animal is determined by processing the one or more images using an animal recognition machine learning model. One or more records indicating characteristics of the animal are accessed based on the unique identifier, and one or more recommended items for the animal are generated based on processing the one or more records using an artificial intelligence system. One or more physical locations of the one or more recommended items in the enterprise are determined, and one or more indications of the one or more physical locations are output to facilitate user navigation to the one or more physical locations of the one or more recommended items in the enterprise.