Host Device Process Management via Inference Model
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Solution Overview
Problem
Existing data processing systems face challenges in managing processes such as startup and software installation, as they often lack comprehensive management interfaces, providing only limited information through graphical user interfaces, which restricts remote monitoring and anomaly detection.
Innovation Solution
The system employs a management controller to obtain screenshots of the graphical user interface at different points in time, using these images along with telemetry and hardware data to infer the current and future states of the process. An inference model predicts the progression status of the process, enabling remote management to ensure completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a management controller obtains and analyzes screenshots and telemetry data to infer process states, then remote monitoring capability and anomaly detection are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The management controller serves as an intermediary between the host device and remote management systems. It captures screenshots and telemetry data locally, processes this information through inference models, and then communicates with remote systems. This intermediary approach enables reliable remote monitoring without requiring direct complex interactions between the host device and remote management, thus improving reliability while controlling system complexity.
Solution Approach 2:
The system performs preliminary actions by capturing screenshots and collecting telemetry data at predetermined time intervals during process execution. This proactive data collection enables early detection of anomalies and allows the inference model to predict potential failures before they occur, improving process completion reliability without requiring complex real-time analysis.
2Measurement precision
If screenshots and telemetry data are collected at multiple time points, then process state inference accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The system applies partial action by collecting screenshots and telemetry data at strategic time points rather than continuously. Screenshots are captured at predetermined intervals and when specific events occur, providing sufficient information for accurate process state inference without the computational overhead of continuous capture and analysis, thus balancing accuracy with processing time.
Solution Approach 2:
The data collection and processing is segmented into discrete time intervals and event-triggered captures. Rather than analyzing all data continuously, the system divides the monitoring process into segments where screenshots and telemetry data are collected at specific moments, then processed separately through the inference model. This segmentation reduces overall computational burden while maintaining inference accuracy.
3Ease of operation
If the system provides limited information through graphical user interfaces, then ease of operation is improved, but remote monitoring capability deteriorates
Solution Approach 1:
The management controller acts as an intermediary that captures comprehensive process information (screenshots and telemetry data) locally, then selectively transmits relevant data to remote management systems. This allows the local graphical user interface to remain simple and easy to operate, while simultaneously enabling remote monitoring without exposing users to complex information displays.
Solution Approach 2:
The system creates copies of the process state information through screenshots and telemetry data collection. These copies are then transmitted to remote management systems, allowing remote monitoring of the complete process state without requiring the local graphical user interface to display all information. This separates the simplicity of local operation from the comprehensiveness of remote monitoring.
Data Source
AI summary
Methods and systems for managing processes performed by a host device are disclosed. To manage the processes performed by the host device, the future state of operation of the host device performing the process may be identified. To identify the future state of operation, the actual state of operation at various points in time during performance of the process may be obtained. The actual state of operation may be obtained using information presented by a graphical user interface managed by a process performed by the host device at different points in time. By doing so, the identified future operating state may be used to manage the continued performance of the process to facilitate completion of the process.