Wearable Workout Type Generation via Cloud Offloading
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Solution Overview
Problem
Electronic devices, especially wearable devices with limited memory capacity, struggle to provide a variety of workout types that match user preferences due to their inherent characteristics and limitations, leading to difficulties in offering desired workout options when interworking with other devices.
Innovation Solution
An electronic device equipped with a processor that acquires a workout type name, recommends data types based on the name, generates a new workout type from selected data types, and acquires corresponding measurement information, allowing for the creation and execution of new workout types, even when synchronized with other devices, thus expanding the range of available workout options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If wearable devices provide workout functions with limited memory capacity, then device complexity is reduced, but the variety of workout types that can be provided is limited
Solution Approach 1:
The patent divides workout type data into two segments: basic workout type information is stored locally in the wearable device's limited memory, while detailed workout content and additional workout types are stored in a cloud server or paired electronic device with larger storage capacity. The wearable device segments its functionality to handle only essential local operations while offloading storage-intensive tasks.
Solution Approach 2:
The patent introduces a cloud server or paired electronic device as an intermediary between the user and the workout data repository. This intermediary handles the storage and management of extensive workout type databases, allowing the wearable device to access a virtually unlimited variety of workout types without increasing its own memory capacity.
2Adaptability or versatility
If electronic devices store more workout types to match user preferences, then user preference satisfaction improves, but memory capacity consumption increases
Solution Approach 1:
The patent makes the workout system universal by enabling it to access workout types from multiple sources: local storage, cloud servers, and paired electronic devices. This multi-functionality allows the system to provide a comprehensive variety of workout types that can satisfy diverse user preferences without requiring each individual device to store all workout types locally.
Solution Approach 2:
The patent transitions from a single-dimension storage model (local device memory only) to a multi-dimensional storage architecture that includes local memory, cloud storage, and external device storage. This dimensional expansion allows the system to access vast quantities of workout data without increasing the physical memory capacity of the wearable device.
3Adaptability or versatility
If wearable devices synchronize workout types with other electronic devices, then workout type variety increases, but synchronization complexity and data mismatch issues arise
Solution Approach 1:
The patent implements a feedback mechanism in the synchronization process where the wearable device receives information about available workout types from paired electronic devices or cloud servers, compares this with its local inventory, and selectively downloads or streams additional workout types based on user preferences and available capacity. This feedback-driven approach reduces unnecessary synchronization operations.
Solution Approach 2:
The patent performs preliminary actions by pre-synchronizing workout type metadata and basic information during initial device pairing or during low-activity periods, so that when the user wants to access workout types, the basic framework is already in place and only specific workout content needs to be loaded, reducing real-time synchronization complexity.
Data Source
AI summary
Disclosed are an electronic device for generating workout types and a method of operating the electronic device. According to an embodiment, an electronic device includes: a memory and at least one processor connected to the memory, wherein the at least one processor is configured to: acquire a workout type name in response to a request for adding a workout type, recommend at least one data type based on the workout type name, generate a new workout type corresponding to the workout type name based on a data type selected from among the at least one recommended data type, and acquire workout measurement information corresponding to the selected data type in response to execution of the new workout type.