ML Workflow Generation via NLP Sentiment Analysis
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Solution Overview
Problem
Current workflow generation techniques require manual intervention or reliance on external systems, leading to wastage of computing resources and delays in communication processes due to the lack of automated identification and generation of approval chains based on communication content.
Innovation Solution
A workflow generation system that utilizes a machine learning model to automatically generate proposed workflows by processing historical communication data, performing natural language processing, and sentiment analysis to determine the need for and structure of approval chains, thereby eliminating the need for manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual intervention is used for workflow generation, then workflow accuracy can be maintained, but computing resources are wasted and delays occur
Solution Approach 1:
The system enables self-service by allowing the workflow generation system to automatically analyze communication data and generate workflows without requiring manual intervention. The machine learning model processes communication content, identifies relevant entities and relationships, and constructs workflows autonomously, eliminating the need for human operators to manually create workflows while optimizing computing resource utilization.
2Reliability
If external systems are relied upon for workflow generation, then workflow creation can occur, but delays in communication processes increase
Solution Approach 1:
The system merges the workflow generation functionality directly into the communication platform by integrating the machine learning model with the communication system. This integration allows the system to analyze communication data and generate workflows within the same ecosystem, eliminating the need to rely on external systems and reducing communication delays while maintaining reliable workflow generation capability.
3Productivity
If automated workflow generation is implemented, then computing resource efficiency improves, but system complexity increases
Solution Approach 1:
The system replaces manual mechanical workflow creation processes with an automated machine learning-based system. The machine learning model automatically analyzes communication data, extracts relevant information, and generates workflows without human intervention, thereby improving computing resource efficiency while managing system complexity through automation rather than manual procedures.
4Ease of operation
If manual workflow creation is used, then system simplicity is maintained, but productivity and speed decrease
Solution Approach 1:
The system enables self-service by allowing the machine learning model to automatically generate workflows based on communication data without requiring manual intervention. This automation maintains ease of operation as users simply need to initiate the process, while the system handles the complex workflow generation autonomously, thereby improving productivity and speed without significantly complicating the user experience.
Data Source
AI summary
A workflow generation system may receive communication data identifying a communication created by a user of a client device, and may process the communication data, with a machine learning model, to determine whether a workflow is needed and particular recipients to be included in the workflow. The machine learning model may be trained based on historical communication data, historical workflow data based on natural language processing, and historical response data based on a sentiment analysis. The workflow generation system may generate a proposed workflow when the workflow is determined to be needed and based on the particular recipients, and may provide data identifying the proposed workflow to the client device. The workflow generation system may receive an approval of the proposed workflow from the client device, and cause the communication to be provided to the particular recipients, in a particular order, based on receiving the approval of the proposed workflow.