AI Schedule Planner Automating Task Inference from Workspace Data
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Solution Overview
Problem
Existing AI-assisted schedule planning systems rely on manual user input, which is unreliable and time-consuming, especially for long-term planning, and lack integration with workspace applications for comprehensive task management.
Innovation Solution
A system that automatically retrieves and infers user tasks from various data sources, constructs prompts for a generative model like GPT-4, and schedules tasks based on context and user preferences, providing interactive schedule generation and refinement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual user input is used for schedule planning, then the system is simple to implement, but the input data quality is poor and the process is time-consuming
Solution Approach 1:
The system automatically retrieves tasks from multiple data sources (emails, calendars, task management applications) without requiring manual user input. The AI agent autonomously processes these tasks, infers priorities, and generates schedules, allowing the system to serve itself rather than relying on user memory or manual entry.
Solution Approach 2:
An AI agent acts as an intermediary between the user's scattered task information and the final schedule. This intermediary automatically collects data from various sources, processes it through reasoning, and produces a coherent schedule, bridging the gap between raw data and useful output without direct user intervention.
2Productivity
If manual task entry is used, then the system requires fewer computational resources, but the time needed for long-term planning increases significantly
Solution Approach 1:
The system performs preliminary actions by automatically retrieving and processing task information from multiple data sources before schedule generation. It pre-processes emails, calendars, and task lists to extract relevant information, so that when the user requests a schedule, the data is already prepared and ready for rapid AI processing.
Solution Approach 2:
The patent replaces manual mechanical task entry with automated AI-based processing. Instead of users manually typing tasks, the system uses AI agents to automatically parse, infer, and process task information from digital sources, significantly reducing the time and computational effort required compared to manual methods.
3Adaptability or versatility
If the system integrates with multiple workspace applications, then the schedule becomes comprehensive and context-aware, but the device complexity increases
Solution Approach 1:
The system is designed with universal connectivity to integrate with multiple workspace applications (emails, calendars, task management tools). A single AI agent can access and process information from various data sources, making the system versatile and adaptable to different user needs without requiring separate specialized tools for each function.
Solution Approach 2:
The patent merges multiple data sources and processing functions into a unified AI agent that handles all task management operations. Instead of having separate systems for email processing, calendar management, and task tracking, the AI agent consolidates these functions, processing all information through a single intelligent framework that generates a comprehensive schedule.
Data Source
AI summary
A data processing system implements receiving, via a first software application on a client device, a call requesting a schedule to be generated for a user by a generative model. The system further implements identifying online and/or offline data source(s) indicating activities specific to the user, the online and/or offline data source(s) including software application(s) within a workspace; constructing a first prompt by a prompt construction unit as an input to the generative model, the prompt construction unit constructing the first prompt by appending the activities and context data to an instruction string, the instruction string comprising instructions to the generative model to schedule the activities based on the context data, and to assign the scheduled activities into the schedule, the context data being associated with the user and/or the activities; providing the schedule to the client device; and causing a user interface of the client device to present the schedule.