Text Processing Method for Workflow Generation
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Solution Overview
Problem
Current natural language processing (NLP) technologies face challenges in generating human-understandable descriptions from text, particularly in transforming text into workflows that are easily comprehensible by humans.
Innovation Solution
A text processing method that involves receiving text describing a process, decomposing it into sub-sentences, filtering and reordering them to form a sub-sentence group with workflow components, determining workflow labels, and generating a visual representation of the workflow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional NLP models (transformers with encoders and decoders) are used to process text, then the model can generate human-like responses, but the output lacks structural clarity and is not easily comprehensible as a workflow
Solution Approach 1:
The patent segments the input text into multiple sub-sentences, each containing at most one workflow component. This segmentation allows the system to process and label each sub-sentence independently, transforming unstructured text into a structured format that clearly represents workflow steps, thereby improving comprehensibility while maintaining manageable processing complexity
Solution Approach 2:
The patent extracts workflow components from the input text and separates them into distinct sub-sentences. By taking out the essential workflow information from the original text and presenting it in isolated, labeled sub-sentences, the system creates a clear, human-readable workflow representation that is easier to understand and operate
2Manufacturing precision
If the text is decomposed into sub-sentences and processed through multiple filtering and reordering steps, then the workflow structure becomes clearer, but the processing time and computational steps increase
Solution Approach 1:
The patent performs preliminary actions by pre-defining the set of possible workflow components and their corresponding labels before processing the input text. This preparation allows the system to efficiently match sub-sentences to labels without complex real-time analysis, improving accuracy while minimizing processing time through pre-computed reference data
Solution Approach 2:
The patent creates a simplified representation of the workflow by copying essential information from the input text into standardized sub-sentence formats. Each sub-sentence is a simplified copy containing only the necessary workflow component information, which reduces processing complexity while maintaining accurate workflow representation
3Ease of operation
If the system automatically determines workflow labels and generates visual representations, then the skill level required for users is reduced, but the system complexity and automation requirements increase
Solution Approach 1:
The patent implements self-service by enabling the system to automatically determine workflow labels and generate visual representations without requiring user intervention. The system processes input text autonomously, decomposing it into sub-sentences, determining appropriate labels, and generating the final workflow visualization, thereby reducing the skill level required from users while increasing the extent of automation
Data Source
AI summary
A text processing method includes: receiving text for describing a process of a task; obtaining a first sub-sentence group from the text for describing the process of the task, the first sub-sentence group including a plurality of sub-sentences arranged in an order in which the process occurs, and each of the plurality of sub-sentences including a workflow component; determining a workflow label corresponding to each sub-sentence in the first sub-sentence group; generating the workflow of the task based on the workflow labels corresponding to the plurality of sub-sentences in the first sub-sentence group; and generating a visual representation of the generated workflow for display by the workflow generation application.