Automated Goal Management Data Structures
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Solution Overview
Problem
Users face challenges in compiling and organizing content across various sources to achieve goals, as existing technologies lack the ability to automatically identify, organize, and track goals and objectives.
Innovation Solution
A system that monitors user interactions to automatically identify, organize, and track goals and objectives by non-intrusively capturing content associated with user interactions, processing it to determine goals, and building a data structure (plan) for tasks and subtasks related to the goals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If the system continuously monitors and processes user interactions to automatically identify and organize goals, then the automation level and goal tracking accuracy are improved, but the computational resources and system complexity increase
Solution Approach 1:
The system segments the goal management process into distinct modular components: interaction monitoring module, content extraction module, goal identification module, task generation module, and plan updating module. Each module performs a specific function and processes data in a structured pipeline, reducing overall system complexity while maintaining high automation levels.
Solution Approach 2:
The system performs preliminary processing of user interactions by continuously monitoring and capturing content in advance before formal goal identification is needed. This pre-processing creates a ready pool of structured data that can be quickly analyzed for goal detection, improving automation efficiency without proportionally increasing system complexity.
2Productivity
If the system automatically determines tasks and subtasks for each goal, then productivity and time management are improved, but the device complexity and processing requirements increase
Solution Approach 1:
The task determination process is divided into hierarchical levels: goals are segmented into major tasks, which are further segmented into subtasks. This hierarchical segmentation allows the system to manage complexity by processing tasks at appropriate granularities, improving productivity without overwhelming system resources.
Solution Approach 2:
The system generates task structures that may include more detailed subtasks than immediately necessary, allowing users to work with high-level tasks initially while the system maintains readiness to expand into more detailed breakdowns as needed. This partial action approach improves productivity by providing structure without requiring full processing of all possible task details upfront.
3Adaptability or versatility
If the system updates and revises plans based on ongoing user interactions, then the goal tracking accuracy and adaptability are improved, but the loss of time for processing and the computational overhead increase
Solution Approach 1:
The system implements continuous feedback loops where user interactions are monitored, processed, and used to update goal plans in real-time. The feedback mechanism compares actual user behavior against planned tasks, automatically revising plans when deviations are detected, thereby improving adaptability while managing processing time through efficient comparison algorithms.
Solution Approach 2:
The system maintains continuous monitoring and updating operations rather than periodic batch processing. This continuity allows the system to detect and respond to goal-related activities as they occur, improving adaptability and goal tracking accuracy while distributing computational load over time to minimize processing overhead at any single moment.
Data Source
AI summary
Users create, view, and interact with massive amounts of content every day, including browsing websites, engaging with social platforms, collaborating electronically with friends and colleagues, transacting with apps and/or plugins, creating and editing documents, and the like. Traditionally, the content associated with various user interactions is siloed based on the entity with which the user interacts. Accordingly, to achieve a goal, it is up to the user to compile content across various sources, identify and organize tasks and subtasks, and track progress and completion of the goal. The present application determines a goal for a user based on monitoring user interactions. A data structure is created for the goal, including determining applicable tasks and subtasks. The data structure becomes a living entity for storing and tracking the goal by continuing to monitor user interactions and determine the user's progress towards completion of each task and, ultimately, the goal.