Document Template Instantiation Using Context-Aware ML Prediction
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Solution Overview
Problem
Existing document management systems face delays in preparing documents for workflows due to the inefficiencies in creating and selecting appropriate templates, leading to slowed workflow execution.
Innovation Solution
A document management system that stores and manages templates, including document and document component templates, uses machine learning to select and instantiate documents based on context, and generates new templates as needed, utilizing a repository with indexed contexts and machine learning models to predict variable values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If experts manually prepare documents for workflow steps, then document accuracy and appropriateness are improved, but workflow execution speed deteriorates
Solution Approach 1:
The system pre-generates multiple versions of document templates with different contexts stored in a repository before they are needed. When a document is required for a workflow step, the system quickly retrieves and selects from pre-prepared templates rather than creating documents from scratch, thus maintaining accuracy while improving speed
Solution Approach 2:
The machine learning model automatically selects the most appropriate document template version based on the current workflow context without requiring expert intervention. The system serves itself by autonomously determining which template version best matches the current situation, reducing manual preparation time while maintaining document appropriateness
2Adaptability or versatility
If multiple versions of document templates are stored and managed, then adaptability to different contexts is improved, but system complexity deteriorates
Solution Approach 1:
The system introduces a machine learning model as an intermediary between the template repository and the document generation process. The ML model automatically compares current context with stored template contexts and selects the best matching version, simplifying the management of multiple template versions by automating the selection process rather than requiring manual management
Solution Approach 2:
The system represents document contexts as feature vectors with specific parameters that can be computationally compared. By transforming contextual information into standardized parameter representations, the system enables efficient automated comparison and selection among multiple template versions without increasing operational complexity
3Productivity
If machine learning models are used to determine variable values, then document generation speed is improved, but computational resource usage deteriorates
Solution Approach 1:
The system applies machine learning selectively only to determine critical variable values that require contextual understanding, rather than using ML for all document generation tasks. For routine variables with deterministic values, traditional methods are used, reducing overall computational resource usage while maintaining fast generation speed for complex elements
Data Source
AI summary
A system, for example, a document management system stores documents and manages workflows associated with documents. The document management system allows discovery of templates based on explicit searches performed by users or automatic searches performed based on a context. The document management system allows generating new document templates based on selected versions of document component templates. The generated document template may be stored as a new version. The document management system instantiates documents based on templates by predicting values of variables used in the template based on various factors that describe the context in which the template is being used. The values used for instantiating variables may be generated using machine learning models that may be trained using historical data stored in the document management system.