Task Identification from Electronic User Communications
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Solution Overview
Problem
Existing technologies fail to accurately identify and track tasks within multi-party conversations, as they cannot effectively integrate personal digital assistants into such contexts, leading to inefficiencies in completing discussed tasks.
Innovation Solution
The system processes multiple communications within a conversation to identify content elements and generate a shared conversational context, enabling accurate task identification and tracking, with features like task assignment, notification, and reassignment based on the conversation context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If personal digital assistants are integrated into multi-party conversations, then task identification accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the conversational context into discrete content elements (tasks, entities, attributes) that can be independently identified, tracked, and managed. Each content element is extracted and associated with specific conversation participants, allowing the system to process complex multi-party conversations through structured decomposition without overwhelming system complexity
Solution Approach 2:
The patent introduces an intermediary processing layer that mediates between raw conversation data and task identification. This layer extracts content elements, establishes associations between them, and maintains conversational context, thereby improving task identification accuracy while managing system complexity through a dedicated intermediate processing stage
2Measurement precision
If multiple communications are processed to generate shared conversational context, then task tracking accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary extraction of content elements from communications as they arrive, building and maintaining a shared conversational context in advance. This preliminary processing of communications to identify and associate content elements enables faster task tracking decisions without requiring complete re-analysis of all previous communications
Solution Approach 2:
The patent applies local quality by focusing processing resources on identifying and tracking specific task-related content elements within the conversational context rather than uniformly processing all communication data. This selective processing approach improves task tracking accuracy while reducing overall processing time by concentrating computational effort where it is most needed
Data Source
AI summary
Systems and methods are disclosed for task identification and tracking using shared conversational context. In one implementation, a first communication from a first user is received within a communication session. The first communication is processed to identify a first content element within the first communication. A second communication is received within the communication session. The second communication is processed to identify a second content element within the second communication. The first content element is associated with the second content element. Based on an association between the first content element and the second content element, a task is identified. An action is initiated with respect to the task.