Personalized Exercise Program Recommendations with Vector Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional exercise systems provide inflexible exercise programs that do not account for individual user preferences, lifestyles, or motivational styles, making it difficult for users to find relevant exercise programs in large libraries.
Innovation Solution
An exercise program recommendation system that utilizes a vector search model and machine learning (ML) personalization model to analyze exercise program videos, identifying vector features and generating personalized recommendations based on user history and queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If exercise programs are stored in a large exercise program library, then the variety and quantity of exercise programs increase, but the difficulty of identifying and searching for relevant exercise programs increases
Solution Approach 1:
The patent introduces an intermediary search system that includes a search query processor and recommendation engine. This intermediary layer between the user and the large exercise program library processes search queries, analyzes user profiles, and generates personalized recommendations, thereby reducing the difficulty of searching through the extensive library without requiring users to manually browse through all programs.
Solution Approach 2:
The patent replaces manual mechanical searching through the exercise program library with an automated electronic system. The system uses computer processors to execute search queries, analyze program characteristics, match them with user profiles, and generate recommendations automatically, eliminating the need for users to manually search through large numbers of programs.
2Ease of operation
If predetermined exercise programs are provided based on user requests, then the exercise programs are easily accessible, but the flexibility and adaptability to individual user lifestyles decrease
Solution Approach 1:
The patent implements a dynamic exercise program recommendation system that continuously adapts to changing user preferences, lifestyles, and goals. The system uses machine learning algorithms to analyze user profiles and search queries in real-time, generating personalized recommendations that evolve over time rather than providing static predetermined programs. This allows the system to maintain ease of access while significantly improving adaptability to individual users.
Solution Approach 2:
The patent changes the parameters of exercise program recommendations by analyzing multiple factors including user profiles, search queries, and program characteristics. Instead of providing fixed predetermined programs, the system dynamically adjusts recommendation parameters based on real-time data, modifying program selections to better match individual user preferences, lifestyles, and goals while maintaining easy accessibility.
3Device complexity
If conventional search methods are used to find exercise programs, then the system complexity is low, but the relevance and quality of exercise program recommendations decrease
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors user interactions with exercise programs, search queries, and program characteristics. This feedback is used to refine and improve the recommendation algorithm over time, enhancing the relevance and quality of recommendations. The feedback loop allows the system to learn from user behavior patterns and adjust its recommendations accordingly, improving measurement precision without requiring excessive initial system complexity.
Solution Approach 2:
The patent performs preliminary actions by pre-processing exercise program data, creating user profiles, and establishing recommendation models before actual search queries are executed. The system pre-analyzes program characteristics, categorizes them according to various criteria, and prepares recommendation templates in advance. This preliminary preparation enables the system to quickly generate relevant recommendations when users search, improving recommendation quality without requiring complex real-time processing during actual searches.
Data Source
AI summary
An exercise program recommendation system may apply a vector search model to a plurality of exercise program videos, the vector search model identifying vector features within the plurality of exercise program videos. An exercise program recommendation system may receive a search query for an exercise program, the search query including an exercise program feature. An exercise program recommendation system may be based on the exercise program feature, applying a machine learning (ML) personalization model to the plurality of exercise program videos. An exercise program recommendation system may provide a user with an exercise program recommendation from the plurality of exercise program videos.