Pet Wellness Platform for Tailored Food Recommendations
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Solution Overview
Problem
Current pet foods often fail to meet the unique nutritional requirements of different pet breeds and individuals due to inadequate data collection and inaccurate reporting of attributes such as activity levels and health conditions, leading to suboptimal dietary recommendations.
Innovation Solution
A pet wellness platform that collects data from wearable devices, video analysis, and IoT devices to generate accurate pet attributes, determining temperature classifications, and recommending tailored pet food quantities and types based on machine-learned recipe scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple pet attributes are collected to improve recommendation accuracy, then the nutritional appropriateness of pet food recommendations is improved, but the complexity of data collection and attribute identification increases
Solution Approach 1:
The platform employs multiple data collection mechanisms (owner input interfaces, wearable device integrations, video analysis systems) that can gather diverse pet attributes through unified processes. These multi-functional systems handle various attribute types (activity levels, health conditions, breed characteristics) through integrated workflows, reducing the operational complexity despite increased attribute coverage.
Solution Approach 2:
The patent introduces automated intermediary systems including machine learning models that process raw data into standardized attributes, API intermediaries that bridge wearable devices with the platform, and video analysis algorithms that extract behavioral attributes. These intermediaries simplify the complexity by automating the transformation from diverse data sources to structured pet attributes.
2Loss of information
If automated data collection from multiple sources is implemented, then the completeness of pet information is improved, but the potential for data errors and inconsistencies increases
Solution Approach 1:
The platform implements feedback mechanisms where collected pet attribute data is processed through validation rules and cross-referenced against known breed standards and health guidelines. Inconsistencies trigger alerts for owner verification, and the system continuously learns from correction patterns to improve future data validation, thereby maintaining reliability while collecting comprehensive information.
Solution Approach 2:
The system performs preliminary data validation and normalization before storing pet attributes. Automated checks verify data consistency against breed-specific requirements and health parameters upfront, preventing error propagation. This preliminary action ensures that comprehensive data collection does not compromise data quality.
Data Source
AI summary
According to some embodiments of the present disclosure, a method for recommending pet food for a pet is disclosed. The method includes receiving pet information corresponding to the pet from a client user device of a user associated with the pet and generating a set of attributes relating to the pet based on the pet information. The method further includes determining a temperature classification corresponding to the pet based on the set of attributes and determining a recipe score corresponding to the pet based upon the temperature classification and the set of attributes. The method further includes determining a pet food recommendation from a pet product database based on the temperature classification, and providing a diet recommendation indicating the pet food recommendation the user via a communication network.