Automated Workout Schedule Generation with Discrete Optimization
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Solution Overview
Problem
Current electronic devices lack an efficient method to generate personalized workout schedules that balance workout types and content preferences, leading to suboptimal fitness plans and user engagement.
Innovation Solution
A discrete optimization approach using a greedy algorithm and rollout algorithm to generate candidate daily and weekly schedules based on user preferences, combined with content filling that selects suitable workout content items based on predefined criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated schedule generation is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The schedule generation process is divided into distinct phases: candidate schedule generation, scoring/evaluation, and selection. The greedy algorithm handles initial schedule construction while the rollout algorithm performs optimization, separating concerns and improving manageability of the complex generation task.
Solution Approach 2:
The system performs preliminary actions by generating multiple candidate schedules before final selection, using the greedy algorithm to create initial feasible schedules and the rollout algorithm to evaluate and refine them. This preliminary generation and evaluation phase enables more efficient final schedule selection.
2Adaptability or versatility
If personalized workout schedules are generated, then adaptability is improved, but computation time increases
Solution Approach 1:
The system incorporates multiple parameters including user preferences, fitness goals, available equipment, and workout constraints to generate personalized schedules. By adjusting and optimizing these parameters through the greedy and rollout algorithms, the system achieves high personalization while managing computation time through efficient parameter evaluation.
Solution Approach 2:
The scoring function evaluates different aspects of schedule quality locally (individual workout sessions, time slots, and constraints) rather than requiring global optimization of the entire schedule at once. This localized evaluation approach enables personalized scheduling while reducing overall computation time.
3Manufacturing precision
If multiple candidate schedules are generated, then manufacturing precision is improved, but loss of information increases
Solution Approach 1:
The system extracts and evaluates only the most relevant features and constraints for each candidate schedule using the scoring function, rather than processing all possible information. This selective extraction maintains optimization accuracy by focusing on critical schedule attributes while reducing data processing overhead.
Solution Approach 2:
The rollout algorithm generates a sufficient number of candidate schedules to achieve high optimization accuracy, using partial evaluation of all possible schedules. This approach achieves near-optimal results by evaluating a carefully selected subset of candidates rather than exhaustively processing all possibilities.
Data Source
AI summary
The subject system may be implemented by a processor circuit configured to receive a request for generating a workout schedule that includes one or more workout sessions and one or more workout content items associated with the one or more workout sessions. The request includes one or more workout session preferences and one or more workout content preferences. The processor circuit is configured to generate a schedule framework based on the one or more workout session preferences, the schedule framework comprising one or more scheduled workout sessions on one or more days of a week, generate the workout schedule by selecting, for each respective scheduled workout session in the schedule framework, a workout content item from a set of workout content items based at least in part on the one or more workout content preferences, and provide an indication of at least a portion of the workout schedule.