Dynamic Model Pipelines Across Network Nodes for Wearables
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Solution Overview
Problem
Existing models, whether specific or general, face challenges when applied to tasks outside their training scope, leading to high noise or error rates, and there is a need for efficient data processing in resource-constrained devices like wearable computing devices.
Innovation Solution
A dynamic processing pipeline is implemented across nodes in a network, combining multiple models configured for specific tasks, with alert thresholds and user-defined filters in a GUI, and utilizing a synchronization protocol to manage node communication and resource sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized models are used for specific tasks, then task accuracy is improved, but model versatility deteriorates
Solution Approach 1:
The patent combines multiple specialized models into a unified pipeline system where each model handles a specific task. The pipeline architecture merges the capabilities of individual models while maintaining their specialized functionality, allowing the system to achieve high accuracy for specific tasks while providing versatility through the ability to process different types of data through different models in the pipeline.
Solution Approach 2:
The pipeline system provides universal functionality by enabling a single system to perform multiple specialized tasks through the coordinated operation of different models. The system can process various types of input data (images, audio, text) through different specialized models while maintaining a unified architecture, thus achieving both accuracy for specific tasks and versatility across different task types.
2Adaptability or versatility
If general models are used for various tasks, then model versatility is improved, but error rates increase
Solution Approach 1:
The patent segments the processing system into distinct specialized models, each dedicated to handling specific task types. This segmentation allows each model to be optimized for its specific domain, reducing error rates while maintaining overall system versatility. The pipeline architecture enables the system to switch between different segmented models based on the input data type, thus avoiding the high error rates associated with using general models for specialized tasks.
3Measurement precision
If multiple models are combined in a pipeline, then task accuracy is improved, but computational complexity increases
Solution Approach 1:
The pipeline system implements dynamic characteristics by enabling flexible configuration and execution of models based on input data types and system resources. The system can dynamically select which models to activate, adjust processing priorities, and adapt the pipeline flow in real-time, thus managing computational complexity while maintaining high accuracy through specialized model processing.
4Weight of moving object
If resource-constrained devices are used, then device portability is improved, but processing capability deteriorates
Solution Approach 1:
The patent extracts computational tasks from the device and distributes them across a network of specialized models. By taking out processing requirements from the resource-constrained device and utilizing distributed computing resources through the pipeline system, the device maintains portability while achieving enhanced processing capability through networked collaboration.
Data Source
AI summary
Embodiments may relate to dynamic processing pipelines that include multiple models communicatively coupled together (e.g., in series) to accomplish a task. Each model in a pipeline may be configured (e.g., trained or machine learned) for a specific task. For example, a first model in a pipeline may be trained to identify humans in images and a second model in the pipeline may be trained to detect furniture in images. The models may be stored on nodes of a network (e.g., an ad-hoc, peer-to-peer, and/or mesh network). The pipeline may be implemented by nodes of the pipeline applying received data to their corresponding models and then transmitting output data to the next node in the pipeline. Embodiments may relate to a graphical user interface (GUI) for viewing or filtering data of the pipeline according to one or more user-defined alert filters.