Document Template Instantiation via Context-Aware Variable Prediction
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Solution Overview
Problem
Delays in preparing documents for document-based workflows in online document management systems slow down the execution of workflows, as they often require expert preparation and are not efficiently adaptable to different contexts and participants.
Innovation Solution
A document management system that stores and manages templates with variables, uses machine learning to determine variable values based on context, and generates or selects appropriate templates and document components for specific workflows, allowing for efficient document instantiation and workflow execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If documents are prepared manually by experts for each workflow step, then document accuracy and context-appropriateness are improved, but workflow execution speed deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-defining document templates with placeholder variables before workflows execute. Templates are prepared in advance with all possible document structures, clauses, and formatting ready, so that during workflow execution only variable substitution is needed, dramatically reducing preparation time while maintaining expert-level document quality
Solution Approach 2:
The system enables self-service by allowing the document management system to automatically select and instantiate appropriate document templates based on workflow context without requiring expert intervention. The system autonomously matches workflow requirements with template characteristics and performs variable substitution using workflow data, eliminating the need for manual expert preparation while maintaining document accuracy
2Adaptability or versatility
If document templates are customized for each specific context and participant, then document adaptability is improved, but system complexity deteriorates
Solution Approach 1:
The system applies segmentation by breaking down complex document templates into modular components organized as a template hierarchy. Documents are divided into reusable sections, clauses, and elements that can be independently selected and combined. This modular structure allows the system to adapt to different contexts by assembling appropriate components without managing complexity of fully customized documents for each scenario
Solution Approach 2:
The system achieves universality through a hierarchical template structure where parent templates contain child templates that can be reused across multiple contexts. A single template hierarchy serves multiple document types and workflow scenarios, allowing the same template framework to adapt to different participants, contexts, and document requirements without requiring separate customization for each case
3Measurement precision
If multiple versions of document templates are stored for different contexts, then context-matching accuracy is improved, but storage and retrieval complexity deteriorates
Solution Approach 1:
The system implements the nested doll principle through a hierarchical template structure where parent templates contain child templates, which may themselves contain further nested templates. This nesting organizes multiple template versions in a structured hierarchy rather than as separate flat entities, allowing the system to manage context-specific variations through selective instantiation of nested elements based on workflow context matching
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.