Task Identification in Messages via NLP Grammar Analysis
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Solution Overview
Problem
Users face difficulties in identifying and managing tasks embedded in various messages such as emails, texts, and voicemails, as these messages often require careful reading and re-reading to ensure task fulfillment, especially when not organized into to-do lists.
Innovation Solution
A method involving natural language processing to analyze messages, classify tasks, and automatically launch suitable user interfaces for task fulfillment, populating data entry fields with relevant information, using a grammar with rule paths to generate candidate tasks and scores, and identifying task entities and actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually read and re-read messages to identify tasks, then task identification accuracy may improve, but time consumption and user burden increase significantly
Solution Approach 1:
The patent replaces the mechanical manual reading process with an automated natural language processing system. The NLP engine analyzes message content, extracts task information, and identifies task entities automatically, eliminating the need for users to manually read and re-read messages while maintaining high accuracy through computational analysis.
Solution Approach 2:
The system enables messages to self-analyze and self-identify task content through integrated NLP capabilities. The message processing system automatically extracts task information, classifies task entities, and structures task data without requiring user intervention, allowing the system to serve itself in identifying tasks within messages.
2Adaptability or versatility
If tasks are embedded in various message formats without organization, then message flexibility is maintained, but task retrieval and management difficulty increases
Solution Approach 1:
The patent introduces an intermediary NLP processing layer between diverse message formats and task management systems. This intermediary automatically extracts task information from various message types (emails, texts, voicemails), standardizes the data structure, and presents organized task information to users, bridging the gap between format flexibility and retrieval ease.
Solution Approach 2:
The system creates a universal task extraction framework that handles multiple message formats through a single NLP processing pipeline. The grammar rules and entity recognition mechanisms work across different message types, providing consistent task identification and organization regardless of the source format, thereby achieving both versatility and ease of operation.
3Productivity
If automated task identification is implemented, then time efficiency improves, but system complexity and processing requirements increase
Solution Approach 1:
The patent segments the NLP system into distinct functional modules: message preprocessing, grammar rule application, entity recognition, task extraction, and result presentation. Each module handles a specific aspect of task identification, reducing overall system complexity by breaking down the complex NLP process into manageable, independent components that can be developed and maintained separately.
Data Source
AI summary
Methods and apparatus are described herein for identifying tasks in messages. In various implementations, natural language processing may be performed on a received message to generate an annotated message. The annotated message may be analyzed pursuant to a grammar. A portion of the message may be classified as a user task entry based on the analysis of the annotated message.