Workflow Graphs for CAD Task Optimization and Bandwidth Reduction
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Solution Overview
Problem
Conventional systems for analyzing and optimizing workflows in CAD applications face challenges in efficiently processing and displaying large amounts of recorded data, leading to high network bandwidth consumption and difficulty in comprehending user workflows, with users often relying on inefficient workflows due to limited exploration of alternative methods.
Innovation Solution
A computer-implemented method using machine learning functions to generate and display workflow graphs that summarize multiple user workflows, highlighting similarities and differences, and provide optimized workflows based on comparisons, reducing data transmission requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional systems record and transmit detailed command and state change data for workflow analysis, then comprehensive workflow data is available, but network bandwidth consumption increases and data transmission costs rise
Solution Approach 1:
The system extracts only the essential workflow information (sequence of commands and resulting state changes) from the complete raw data, transmitting this condensed representation instead of the full detailed data set. This extraction process retains the necessary workflow analysis capabilities while significantly reducing the volume of transmitted data.
Solution Approach 2:
Instead of transmitting raw data and processing it locally, the system inverts the approach by pre-processing data on the server to extract only the essential workflow sequence information, then transmitting this refined data for client-side analysis. This inversion shifts the processing burden from the client to the server.
2Loss of information
If conventional systems transmit and display large amounts of recorded workflow data, then complete workflow information is accessible, but network congestion increases and display effectiveness decreases
Solution Approach 1:
The system segments the workflow data into discrete command-state change pairs, organizing the information into a structured sequence that is easier to process and analyze. This segmentation transforms the monolithic raw data into manageable units that can be efficiently transmitted and displayed.
Solution Approach 2:
The system inverts the conventional approach by pre-processing and structuring the workflow data on the server before transmission, rather than transmitting raw data and processing it locally. This inversion delivers pre-organized, analysis-ready data to the client, reducing both network load and client-side processing requirements.
3Reliability
If users rely on tried and true workflows, then workflow reliability is maintained, but productivity improvement is limited due to lack of exploration of alternative workflows
Solution Approach 1:
The system implements feedback by analyzing recorded workflow data to identify patterns, variations, and alternative approaches used by different users. This feedback mechanism provides users with insights into other workflow methods, enabling them to compare and potentially adopt more efficient approaches while maintaining reliability through informed decision-making.
Solution Approach 2:
The system enables parameter changes by allowing users to modify their workflows based on analyzed patterns and alternatives. Users can adjust their workflow parameters (sequence of commands, state changes) to optimize for different criteria such as speed, efficiency, or personal preference, thereby improving productivity while maintaining reliability through controlled experimentation.
Data Source
AI summary
A W-graph system comprising a server connected with a plurality of clients via a network. Each client/user performs a design task via a design application while the server collects timestamped event data. The server generates a plurality of W-graphs for a plurality of tasks based on the collected event data. Each W-graph comprises one or more representative workflows, each representative workflow comprising at least one merged node representing nodes from different workflows for different users performing the same task. A W-graph for a task selected by the user may be viewed in a W-graph GUI. A user may also select a W-suggest function to have a current workflow for a task analyzed for optimization based on a W-graph generated for the same task. A modified current workflow is generated that highlights user techniques in the current workflow that are less efficient than user techniques in the W-graph.