Unified Task Aggregation for Role-Based Planning
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Solution Overview
Problem
Conventional methods for managing work and personal tasks require users to log into multiple systems, manually organize and prioritize tasks, leading to decreased productivity and missed activities due to the fragmentation of task management across different tools and sources.
Innovation Solution
A system that collects task-related data from multiple sources, including work-specific and personal calendars, and uses crowd-sourced data to optimize task planning, prioritization, and alerting, providing a unified interface for users to manage their tasks based on role-based data and task-tracking information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually organize and prioritize tasks across multiple systems, then task management flexibility is improved, but time consumption and productivity decrease
Solution Approach 1:
The system automatically collects task data from multiple sources, determines task lists, and prioritizes tasks without requiring manual user input. The task manager autonomously aggregates information from calendars, emails, and other sources, eliminating the need for users to manually organize tasks while maintaining flexible task management.
2Loss of information
If users check multiple systems and applications, then comprehensive task visibility is improved, but operational complexity and time consumption increase
Solution Approach 1:
The system merges task information from multiple separate sources (calendars, emails, task lists) into a single unified task list. This consolidation provides comprehensive task visibility while simplifying the user interface, allowing users to access all task information through one application rather than multiple separate systems.
Solution Approach 2:
The task manager acts as an intermediary layer between multiple data sources and the user. It collects and processes information from various systems, then presents it in a simplified format, reducing the complexity of accessing multiple systems while maintaining comprehensive information visibility.
3Measurement precision
If users manually prioritize tasks, then task relevance accuracy is improved, but productivity and time efficiency decrease
Solution Approach 1:
The system uses feedback from multiple sources including role-based task data, task-tracking information, and user patterns to automatically determine task priorities. This feedback mechanism enables the system to learn from historical data and improve its prioritization accuracy over time while maintaining high productivity.
Solution Approach 2:
The system changes the parameter of priority determination from manual user judgment to automated algorithmic assessment based on multiple factors including role-based data, task tracking information, and temporal patterns. This parameter change maintains measurement precision while significantly improving productivity.
Data Source
AI summary
According to a general aspect, a system for personalized planning based on crowd-sourcing includes a data collector configured to collect task-related data specific to a user from multiple different data sources, and a planning optimizer configured to determine a task list providing upcoming tasks to complete based on an analysis of the task-related data in view of role-based task data and task-tracking information. The planning optimizer obtains a task from other users having a same role as the user based on the role-based task data, determines a suggested activity for completing the task based on the task-tracking information, and provides the task list to the user via a user interface.