AI Task Flow Interface for Subtask Visibility and Progress Tracking

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Solution Overview

Problem

Conventional AI agent interfaces lack intuitive visualizations of training and capabilities, leading to difficulty in understanding how the AI agent has been trained and its proficiency for specific tasks, resulting in unpredictable responses and poor user experiences.

Innovation Solution

Systems and methods provide interfaces for generating and displaying user instructions, splitting tasks into subtasks, and visually presenting the processing flow, along with dynamic updates, to facilitate management and interaction with AI agents.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If conventional AI agent interfaces are used, then the AI agent can perform tasks, but the user cannot intuitively understand the AI agent's training and capabilities

Engineering Contradiction:

Improveinformation about AI agent training and capabilitiesVSAvoiduser understanding and interaction with AI agent

Core Design Contradiction:

Loss of informationVSEase of operation

Solution Approach 1:

The patent segments the AI agent's training information and capabilities into distinct visualizable components. The interface divides complex training data into manageable sections showing different training stages, data samples, and capability assessments, making previously invisible information accessible and understandable to users.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces an intermediary visualization layer between the user and the AI agent's internal training state. This intermediary interface translates complex training metrics and capability data into intuitive visual representations, allowing users to understand AI agent proficiency without directly accessing raw training data.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If the AI agent is trained for specific tasks, then it performs well on those tasks, but it cannot respond predictably to generalized tasks

Engineering Contradiction:

ImproveAI agent performance on specific tasksVSAvoidAI agent response to generalized tasks

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The patent implements feedback mechanisms that allow users to observe the AI agent's training progress and capability development in real-time. By visualizing which tasks the AI agent is proficient at and which require additional training, users can provide targeted feedback and guidance to improve both specific task performance and general adaptability.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent creates a dynamic interface that shows the AI agent's evolving capabilities as training progresses. The visualization updates to reflect changing proficiency levels across different task types, allowing users to see how the AI agent transitions from specialized to more generalized capabilities through continuous training.

Inventive Principle:

Principle #15Dynamics

3Adaptability or versatility

If the AI agent is trained for generalized tasks, then it can respond to various tasks, but it becomes undertrained for specific tasks

Engineering Contradiction:

ImproveAI agent capability for generalized tasksVSAvoidAI agent performance on specific tasks

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent applies local quality by allowing users to identify specific task areas where the AI agent needs improved proficiency. The interface enables targeted training interventions in specific capability domains while maintaining overall generalized capabilities, ensuring high reliability for particular tasks without sacrificing versatility.

Inventive Principle:

Principle #3Local quality

4Device complexity

If no visualizations of training are provided, then the interface is simple, but the user experience is poor and utility is reduced

Engineering Contradiction:

Improveinterface complexityVSAvoiduser experience and utility

Core Design Contradiction:

Device complexityVSEase of operation

Solution Approach 1:

The patent adds a new dimensional layer to the interface by incorporating visualizations of AI agent training status and capabilities. This additional dimension presents training information in intuitive graphical formats such as progress indicators, capability heat maps, and training trajectory visualizations, enhancing user experience without fundamentally complicating the core interface.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Data Source

PatentUS20250355697A1Task management interfaces for end-to-end task processing and sub-task generation and modification

Publication Date: 2025.11.20 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

Systems and methods are provided for facilitating management of interactions and training for AI (artificial intelligence) agents. Systems generate and display interfaces for training AI agents and for receiving user instructions. The systems parse user instructions to identify tasks to be performed by the AI agents. The systems cause the tasks to be split into subtasks to be performed by the AI agent. The systems also display a dialog frame that presents the user instructions along with AI agent responses that identify the subtasks. The systems also display a graph that visually identifies a processing flow of the subtasks and that dynamically updates the processing flow to reflect a status of progress for the AI agent performing the subtasks.