Document Chronology Graph for Format Conversion Context
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Solution Overview
Problem
Existing systems fail to accurately track changes in electronic documents, particularly when converting formats, as contextual information and authorship details are often lost, leading to misattribution of changes.
Innovation Solution
A method and system utilizing a machine learning model to create a document chronology graph by identifying connecting events between entities, including electronic documents and their modifiers, through a supervised learning approach, which constructs a chronology of changes and maintains contextual information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If tracked changes are used in word processing software, then modification history can be recorded, but contextual information and authorship details are lost when converting to other formats
Solution Approach 1:
The patent transitions from traditional linear change tracking to a graph-based chronology model that adds dimensional context. The chronology graph structure introduces new dimensions for representing relationships between changes, entities, and documents, preserving contextual information that would otherwise be lost during format conversion.
Solution Approach 2:
The system combines multiple data types and information sources into a unified chronology graph structure. It integrates document metadata, entity information, change details, and contextual relationships into a composite representation that preserves all relevant information across format conversions.
2Measurement precision
If traditional change tracking is implemented, then modifications can be monitored, but accurate authorship attribution becomes difficult when multiple entities are involved
Solution Approach 1:
The patent segments the change tracking system into distinct entity nodes and connecting event nodes within a graph structure. This segmentation allows precise attribution of changes to specific entities while maintaining clear relationships between multiple stakeholders, improving authorship accuracy without requiring a monolithic complex system.
Solution Approach 2:
The chronology graph acts as an intermediary structure between raw change data and final authorship attribution. It introduces entity nodes and connecting events as mediators that bridge the gap between document modifications and meaningful authorship information, enabling accurate attribution even in multi-entity scenarios.
3Adaptability or versatility
If document format conversion is performed, then compatibility is improved, but tracked changes are lost
Solution Approach 1:
Instead of relying on format-specific change tracking that gets lost during conversion, the patent creates a format-independent chronology graph copy of the change history. This graph structure captures the essential change information in a universal representation that can be preserved across different document formats, effectively copying the change history in a sustainable manner.
Data Source
AI summary
A system and method for creating an electronic document chronology. The method includes applying a machine learning model to an application data set to determine a plurality of connecting events representing a plurality of electronic document changes, wherein each connecting event is between a first entity and a second entity of a plurality of entities, wherein the first entity of each connecting event is an electronic document, wherein the application data set includes first electronic document change data and a plurality of first entity identifiers of the plurality of entities; and creating a document chronology graph based on the plurality of connecting events, wherein the document chronology graph includes a plurality of nodes and a plurality of edges, wherein each node represents one of the plurality of entities, wherein each edge represents one of the plurality of connecting events.