Document Template Instantiation Using Context-Based ML Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing document management systems face delays in preparing documents for workflows due to the inefficiencies in creating and adapting templates, which slow down the overall execution of document-based processes.
Innovation Solution
A document management system that utilizes machine learning to store, manage, and instantiate templates by determining variable values based on context, allowing for efficient generation and execution of documents through template matching and automatic generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If experts manually prepare documents for workflow steps, then document accuracy and quality are improved, but workflow execution time increases and productivity decreases
Solution Approach 1:
The system creates document templates from previously executed documents of the same type. These templates capture the structure, content, and variable assignments, allowing rapid reproduction of high-quality documents without manual expert intervention for each new document instance.
Solution Approach 2:
The system performs preliminary document preparation by pre-defining templates with all necessary structures, clauses, and variable placeholders before workflow execution. This advance preparation eliminates the need for manual document creation during workflow steps, significantly reducing execution time while maintaining quality through the inherited template structure.
2Adaptability or versatility
If document templates are manually created and adapted for each workflow, then document specificity and relevance are improved, but time consumption and resource usage increase
Solution Approach 1:
The system creates universal document templates that can serve multiple workflow steps and different document types. A single template can be adapted to generate various documents by changing variable assignments, eliminating the need to create separate templates for each specific workflow instance while maintaining document relevance through contextual variable substitution.
Solution Approach 2:
The system copies proven document structures and content from previously successful document instances to create new templates. This copying approach ensures that templates inherit the adaptability and relevance of historically effective documents while eliminating the time-consuming process of manual template creation and adaptation.
3Adaptability or versatility
If multiple versions of document templates are maintained for different contexts, then document context-appropriateness is improved, but system complexity increases
Solution Approach 1:
The system implements dynamic template selection and generation based on workflow context, document type, and execution step. Instead of maintaining static multiple versions, the system dynamically determines which template version or how to adapt a base template based on real-time contextual parameters, reducing management complexity while maintaining context appropriateness.
Solution Approach 2:
The system manages template versions by parameterizing them with contextual metadata (workflow step, document type, participant roles). This allows the system to select or generate appropriate template versions by changing parameters rather than manually managing multiple discrete versions, significantly reducing the complexity of template version control while maintaining context-specific appropriateness.
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.