Multimodal Interface Trajectory Capture for AI Task Automation
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Solution Overview
Problem
Existing deep learning models require large amounts of labeled data for training, which is time-consuming and costly, and there is a need for systems that can integrate human knowledge and experience to improve model performance and adapt to diverse tasks.
Innovation Solution
A system for generating training data and automating tasks using artificial intelligence agents that intercept user actions, translate them into machine-actuated commands, and construct prompts to automate multimodal interface workflows, leveraging a Transformer-based architecture for efficient data processing and integration of human knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large amounts of labeled data are used for training deep learning models, then model performance is improved, but training time and cost increase significantly
Solution Approach 1:
The system creates synthetic training data by capturing and replaying user interactions with the interface. Instead of requiring real labeled data for every possible scenario, the system copies actual user behavior patterns and transforms them into training examples, significantly reducing the time needed to accumulate sufficient training data while maintaining model performance.
Solution Approach 2:
The system enables itself to generate training data through automated recording of user interactions. The interface automatically captures user actions, interface states, and outcomes, then processes this information to create training examples without requiring manual annotation. This self-service approach eliminates the time-consuming manual labeling process while continuously improving the training dataset.
2Quantity of substance
If manual data annotation is performed to train AI models, then labeled training data is obtained, but the process becomes laborious and slow
Solution Approach 1:
The system automatically generates labeled training data by recording user interactions with the interface. Users naturally provide the labels through their own actions, and the system captures these interactions to create training examples. This eliminates the need for manual annotators and dramatically increases the speed at which labeled data can be accumulated.
Solution Approach 2:
The system uses feedback from actual user interactions to generate training data. By continuously monitoring and recording what users do and how they interact with the interface, the system creates a feedback loop where real usage patterns directly translate into training examples, enabling rapid accumulation of high-quality labeled data without manual intervention.
3Extent of automation
If existing deep learning models are used, then task automation is achieved, but adaptability to new tasks is limited without additional training data
Solution Approach 1:
The system creates a dynamic training data generation process that continuously adapts to new tasks and interface versions. Instead of using static pre-trained models, the system actively records user interactions with current interface versions and generates updated training data that reflects evolving workflows. This enables the model to adapt to new tasks and interface changes without requiring retraining from scratch.
Solution Approach 2:
The system performs preliminary recording of user interactions before they are needed for training. By continuously capturing interface usage patterns and states in advance, the system builds a reservoir of training examples that can be quickly processed into training data when new tasks emerge. This preliminary data collection enables rapid adaptation to new tasks without waiting for manual annotation of new scenarios.
Data Source
AI summary
A system for generating training data to train agents to automate tasks otherwise done by users includes an intermediary disposed between an interface and a user. The intermediary is configured to: intercept one or more user-actuated actions directed towards the interface by the user, the user-actuated actions, if received by the interface, execute a task on the interface; preserve a state of the interface prior to the execution of the task; translate the user-actuated actions into one or more actuation commands, the actuation commands configured to trigger one or more machine-actuated actions that replicate the user-actuated actions on the interface to cause automation of the task; and generate a training dataset to train an agent to automate the task, wherein the training dataset requires the agent to process, as input, the state of the interface prior to the execution of the task, and to generate, as output, the actuation commands.