Calendar Event Document Linking Using Content Connection Analysis
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Solution Overview
Problem
Conventional digital documentation systems are inflexible, inaccurate, and inefficient in providing access to digital documents, often disseminating irrelevant content and wasting resources on unnecessary searches and communications during calendar events.
Innovation Solution
An event document management system that utilizes a trained machine learning model to analyze document and calendar event characteristics, automatically associating relevant digital documents with calendar events based on content connections, and providing suggestions, transformations, and summaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional digital documentation systems provide access to digital documents based on specific search requests, then document accessibility is improved, but system flexibility deteriorates
Solution Approach 1:
The system automatically performs document search, retrieval, and association with calendar events without requiring explicit user search requests. The system self-services by proactively identifying relevant documents based on calendar event context and automatically making them accessible to participants.
Solution Approach 2:
The system performs document retrieval and association actions in advance of user requests by pre-processing calendar events and pre-identifying relevant documents. This preliminary action allows documents to be ready for immediate access when needed, improving both accessibility and flexibility.
2Ease of operation
If conventional systems require manual user operations to access and utilize digital content, then user control is maintained, but operational efficiency deteriorates
Solution Approach 1:
The system performs preliminary actions by automatically searching for, retrieving, and associating relevant documents with calendar events before users need them. This eliminates the need for users to manually search and access documents, significantly improving operational efficiency while maintaining user control through automatic suggestions.
Solution Approach 2:
The system provides feedback by automatically suggesting relevant documents to users based on calendar event context. This feedback loop allows the system to learn from user interactions and continuously improve its document recommendations, enhancing both efficiency and user control.
3Loss of information
If conventional digital documentation systems disseminate digital documents to users, then information sharing is achieved, but accuracy deteriorates
Solution Approach 1:
The system introduces an intermediary layer of automated document analysis and relevance assessment between the document repository and users. This intermediary uses machine learning models to evaluate document-content connections and filter relevant documents, ensuring accurate information sharing while maintaining high relevance accuracy.
Solution Approach 2:
The system replaces manual user judgment of document relevance with an automated machine learning-based relevance assessment mechanism. This substitution uses trained models to objectively evaluate document-content connections, improving accuracy while enabling scalable information sharing.
4Quantity of substance
If conventional systems conduct unnecessary digital document search queries and create unnecessary communications, then comprehensive document coverage is achieved, but resource efficiency deteriorates
Solution Approach 1:
The system extracts and focuses only on the essential document retrieval tasks related to specific calendar events, eliminating unnecessary search queries and communications. By extracting the core relevant information needs and addressing them directly, the system achieves comprehensive coverage of necessary documents while minimizing resource consumption.
Solution Approach 2:
The system performs preliminary analysis of calendar events to pre-identify relevant documents and pre-filter out unnecessary search queries. This preliminary action ensures that only essential document retrieval and communication operations are performed, optimizing resource efficiency while maintaining comprehensive document coverage.
Data Source
AI summary
The present disclosure relates to generating and utilizing digital associations between digital documents and digital calendar events based on determined content connections. For example, one or more embodiments suggest a digital document to a user for association with a digital calendar event based on content connections between the digital document and the digital calendar event. In particular, one or more embodiments determine a content connection based on document characteristics (e.g., document content and access characteristics) and calendar event characteristics (e.g., calendar content and historical meeting information). For example, the system can train a machine learning model to determine content connections based on document characteristics and calendar event characteristics. By associating digital documents with digital calendar events, the system can efficiently manage digital documents for participants, automatically generate new digital documents for calendar events, generate snippets for insertion within digital documents, and generate calendar event summaries and action items.