Task Assignment Identification via Message Parsing
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Solution Overview
Problem
Existing communication systems fail to efficiently identify and assign tasks from electronic messages, leading to delays and miscommunication in both professional and personal contexts, affecting individual and organizational productivity.
Innovation Solution
A task assignment identification technique that automatically identifies pending tasks and associates them with the appropriate individuals by parsing electronic messages using natural language processing and machine learning, computing confidence scores based on message factors to determine task assignments and responsibilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual task identification and assignment from electronic messages is performed, then task assignment accuracy can be maintained, but time consumption and productivity decrease significantly
Solution Approach 1:
The system enables self-service by automatically identifying tasks and assigning them to appropriate individuals without requiring manual intervention. The task assignment identification technique autonomously parses electronic messages, extracts task information, determines responsible parties, and updates task tracking systems, allowing the system to serve itself in the task management process.
Solution Approach 2:
The patent replaces the mechanical manual process of reading, understanding, and assigning tasks from electronic messages with an automated computational system. Natural language processing algorithms and machine learning models substitute human cognitive and manual operations, automatically analyzing message content, identifying task semantics, and determining assignments based on learned patterns and confidence scores.
2Productivity
If automated task identification is implemented, then productivity increases, but system complexity and difficulty of implementation increase
Solution Approach 1:
The task assignment identification technique is designed as a universal system that can handle multiple types of electronic messages (emails, instant messages, collaborative platform messages) and various task formats through a single integrated platform. The system performs multiple functions including message parsing, task identification, entity recognition, confidence scoring, and task tracking updates, consolidating what would otherwise require separate specialized tools into one multi-functional solution.
Solution Approach 2:
The patent introduces an intermediary task assignment identification system that bridges electronic message platforms and task tracking systems. This intermediary component parses messages, identifies tasks and assignments, and communicates with task tracking systems, thereby simplifying the integration complexity by providing a standardized interface layer between different systems rather than requiring direct complex connections.
3Measurement precision
If comprehensive analysis of message factors is performed to determine task assignments, then assignment accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-processing electronic messages to extract relevant features and compute confidence scores before final task assignment determination. The task assignment identification technique analyzes message factors in advance, identifies potential tasks and assignments, and prepares confidence scores that can be quickly evaluated, thereby reducing the computational burden during the actual assignment decision-making process.
Solution Approach 2:
The patent applies partial action by selectively analyzing only the most relevant message factors and features necessary for accurate task identification and assignment, rather than processing every possible attribute of each message. The system focuses computational resources on key indicators such as task keywords, mentioned individuals, temporal information, and action verbs, achieving high accuracy without the excessive computational cost of comprehensive analysis of all message elements.
Data Source
AI summary
Task assignments are identified. A dataset that includes one or more electronic messages is received. Then, one or more pending tasks in the dataset are identified, and each of a plurality of people who are mentioned in the dataset is also identified. Then, for each of the pending tasks, one or more of the identified people are identified as potentially being people who are assigned to complete the pending task, and the pending task is associated with these identified one or more of the identified people. For each of the pending tasks, one or more of the identified people are also identified as potentially being people for whom the pending task is to be completed, and the pending task is also associated with these identified one or more of the identified people.