ML Workflow Vectors for Communication Task Prediction
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Solution Overview
Problem
Evaluating the prospects of communication workflows at a big-data scale is computationally burdensome due to the complexity of workflows, and determining which tasks to include in a workflow is often inefficient and manually performed without leveraging previous workflows.
Innovation Solution
Utilizing machine-learning techniques to generate composite feature vectors representing communication workflows, which are inputted into trained models to predict task outcomes and recommend additional tasks for completing partial workflows, employing methods like Graph2Vec for vector representation and neural networks to enhance computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to determine which tasks to include in a workflow, then flexibility and control are maintained, but efficiency and productivity deteriorate due to the time-consuming nature of manual evaluation
Solution Approach 1:
The patent replaces manual mechanical evaluation processes with machine learning-based automated systems. The ML models analyze workflow characteristics, task outcomes, and performance metrics to automatically recommend and select tasks for inclusion in workflows, eliminating the need for manual task-by-task evaluation and significantly improving efficiency.
Solution Approach 2:
The system enables workflows to self-optimize by automatically learning from historical performance data and task outcomes. The machine learning models continuously improve task selection and workflow configuration based on accumulated data, allowing the system to serve itself without constant manual intervention while maintaining high productivity.
2Productivity
If traditional methods are used to evaluate communication workflows, then simplicity is maintained, but computational efficiency deteriorates due to the complexity of workflows at big-data scale
Solution Approach 1:
The patent segments complex communication workflows into smaller, manageable components such as individual tasks, task outcomes, and performance metrics. The machine learning models process these segmented elements separately and aggregate the results, making it computationally feasible to evaluate complex workflows at big-data scale without overwhelming computational resources.
Solution Approach 2:
The system transforms workflow evaluation from a qualitative manual process to a quantitative automated process by introducing numerical parameters such as task outcome probabilities, performance scores, and predictive metrics. This parameter transformation enables efficient computational processing and comparison of complex workflows using standardized numerical representations.
3Productivity
If previous workflows are not leveraged in determining task selection, then each workflow is evaluated independently ensuring accuracy, but productivity deteriorates due to redundant evaluation of the same tasks
Solution Approach 1:
The patent implements feedback loops where historical workflow data and task outcomes are continuously fed back into the machine learning models. The models learn from past performance patterns and use this feedback to improve future task selections and workflow configurations, preventing redundant evaluations while maintaining accuracy through data-driven insights.
Solution Approach 2:
The system performs preliminary analysis of historical workflow data before creating new workflows. The machine learning models pre-process and store valuable insights from previous workflows, enabling rapid task selection and workflow configuration without starting from scratch, thus improving productivity while preserving historical information.
Data Source
AI summary
The present disclosure generally relates to evaluating communication workflows comprised of tasks using machine-learning techniques. More particularly, the present disclosure relates to systems and methods for generating a prediction of a task outcome of a communication workflow, generating a recommendation of one or more tasks to add to a partial communication workflow to complete the communication workflow, and generating a vector representation of a communication workflow.