Local Workout Recommendation Engine for Privacy and Bandwidth
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Modern computing devices face challenges in keeping users engaged with workout routines due to the lack of exposure to new workouts or content, which can lead to reduced user interest and increased network, processing, and power consumption.
Innovation Solution
A computing system configured to generate privacy-preserving personalized workout recommendations by analyzing historical workout data from both internal and external workouts, using a wearable device and a server, to suggest tailored content without exposing personal health information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If workout recommendations are generated using centralized server processing, then recommendation accuracy can be improved through comprehensive data analysis, but user privacy is compromised and network bandwidth consumption increases
Solution Approach 1:
The patent implements local processing on user devices to generate workout recommendations, allowing personalized recommendations to be created without transmitting sensitive health data to centralized servers. This distributes the computational task from a central server to individual user devices, maintaining privacy while delivering accurate recommendations based on local workout history and preferences
Solution Approach 2:
The patent introduces encrypted data transmission as an intermediary mechanism between user devices and servers. Sensitive health information is encrypted before transmission, allowing the system to leverage centralized server resources for recommendation generation while protecting user privacy through the encryption intermediary layer
2Adaptability or versatility
If comprehensive workout data is collected and processed centrally, then personalized recommendations can be generated, but network bandwidth and processing resources are consumed
Solution Approach 1:
The patent segments the recommendation system into distributed components running on individual user devices. Each device independently processes its own workout data locally, eliminating the need to transmit comprehensive workout data across the network. This segmentation maintains personalization capabilities while dramatically reducing network bandwidth consumption
Solution Approach 2:
The patent enables user devices to autonomously generate workout recommendations using local processing capabilities and stored workout history. This self-service approach allows devices to create personalized recommendations without requiring continuous centralized processing, reducing both network bandwidth and server processing resource consumption
Data Source
Figure 1
Figure 2
Figure 3
AI summary
In some implementations, a computing system can be configured for presenting privacy preserving personalized workout recommendations. In some implementations, a workout application of system 100 can generate workout recommendations based on workouts previously performed by a user within the workout application and/or external to the workout application. For example, a user can participate in an internal workout presented by the workout application or an external workout performed without the aid of the workout application and detected by one or more sensors of a user device carried by the user during the external workout. The various attributes of the workouts can be stored as historical workout data and used by the workout application to recommend to the user workouts available through the workout application, or corresponding workout service. The workout recommendations can be generated on the user device to preserve the privacy of the user's personal health information.