Document Management System Context-Aware Template Instantiation
Find Innovative SolutionsGenerate Solutions
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 varying contexts and participants.
Innovation Solution
A document management system that stores and manages templates with variables, uses machine learning models 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 adaptation to different contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If expert preparation is used for document creation, then document quality and accuracy are improved, but workflow execution speed deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-defining document templates with all necessary structures, clauses, and variable placeholders before actual document instantiation. This allows documents to be quickly generated by simply filling in variable values rather than requiring expert preparation from scratch each time, thus maintaining quality while improving speed
Solution Approach 2:
The system uses template copying where a master template definition is created once and then instantiated multiple times by replacing variable placeholders with actual values. This copying mechanism preserves the quality of the original template structure while enabling rapid document generation without repeated expert intervention
2Reliability
If manual document preparation is used, then document accuracy is improved, but time consumption increases
Solution Approach 1:
The system implements self-service by automatically matching document templates to workflow contexts using machine learning models. The system autonomously selects appropriate templates and instantiates documents by replacing variables without requiring manual expert preparation, thereby reducing time consumption while maintaining accuracy through structured template designs
Solution Approach 2:
The system changes parameters by using variable placeholders in templates that can be dynamically replaced with context-specific values. This allows the same template structure to generate accurate documents for different contexts by simply changing the parameter values, eliminating the need for manual preparation while preserving accuracy
3Adaptability or versatility
If templates are stored for different contexts and participants, then document adaptability is improved, but system complexity increases
Solution Approach 1:
The system achieves universality by creating context-agnostic template definitions that can serve multiple workflows and participant types. Instead of maintaining separate templates for each context, a single template with variable placeholders can adapt to different contexts through parameter substitution, reducing system complexity while maintaining high adaptability
Solution Approach 2:
The system uses machine learning models as intermediaries to bridge the gap between diverse workflow contexts and template selections. The ML model automatically matches contexts to appropriate templates and performs variable substitution, shielding users from the complexity of managing multiple context-specific templates while maintaining adaptability
4Reliability
If context-based template selection is implemented, then document relevance is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on historical workflow and template data before actual document generation. This pre-training enables the models to quickly match new contexts to appropriate templates without time-consuming analysis during document creation, thus improving relevance while minimizing processing time
Solution Approach 2:
The system changes parameters by using vector representations of contexts that can be efficiently compared and matched. By transforming contextual information into comparable parameter formats, the system enables rapid relevance assessment and template selection without sacrificing document relevance
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.