Automated Email Task and Calendar Extraction
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Solution Overview
Problem
Users often struggle to manage and prioritize incoming email messages that contain tasks or calendar-related information, leading to missed deadlines and overlooked appointments due to the embedded nature of these items within email communications.
Innovation Solution
An automated system that extracts and classifies text content from electronic communications to identify tasks and calendar items, allowing users to verify and populate them into task and calendar applications, ensuring timely reminders and scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually review and extract tasks from email messages, then task accuracy is improved, but time consumption and productivity deteriorate
Solution Approach 1:
The system automatically extracts tasks from email messages without requiring manual user intervention. The task extraction module autonomously identifies task-related content, parses it into structured format, and populates task management systems, enabling the system to serve itself rather than requiring continuous human oversight for each extraction operation.
Solution Approach 2:
The patent replaces the manual mechanical process of reading and extracting tasks from emails with an automated text processing system. The system uses keyword matching, pattern recognition, and natural language processing algorithms to automatically identify and extract task information, substituting human cognitive effort with computational processes.
2Measurement precision
If users manually create calendar entries from email communications, then scheduling accuracy is improved, but time loss increases
Solution Approach 1:
The system performs preliminary extraction and structuring of calendar information from email messages before final calendar entry creation. By pre-identifying meeting details, dates, and times during the initial task extraction phase, the system prepares calendar data in advance, reducing the time needed for final calendar population while maintaining accuracy through automated validation.
Solution Approach 2:
The task extraction module serves multiple functions: it extracts both task information and calendar information from the same email messages. This multi-functional approach allows the system to simultaneously populate both task management systems and calendar applications, eliminating the need for separate manual processes and reducing overall time loss.
3Productivity
If automated extraction is implemented, then productivity is improved, but reliability of task identification deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms where extraction results are validated against established criteria and patterns. The validation module checks extracted tasks against keyword lists, format requirements, and contextual rules, providing feedback loops that ensure only reliably identified tasks are populated, thereby maintaining high identification accuracy while preserving automated processing speed.
Solution Approach 2:
The patent introduces an intermediary validation layer between the automated extraction process and final task population. This intermediary module acts as a mediator that filters and verifies extracted information against predefined criteria, ensuring that only reliably identified tasks proceed to the task management system, thus maintaining both productivity and reliability.
Data Source
AI summary
Automatically detected and identified tasks and calendar items from electronic communications may be populated into one or more tasks applications and calendaring applications. Text content retrieved from one or more electronic communications may be extracted and parsed for determining whether keywords or terms contained in the parsed text may lead to a classification of the text content or part of the text content as a task. Identified tasks may be automatically populated into a tasks application. Similarly, text content from such sources may be parsed for keywords and terms that may be identified as indicating calendar items, for example, meeting requests. Identified calendar items may be automatically populated into a calendar application as a calendar entry.