Multimodal Interface Runtime for Agent-Based Workflow Automation
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Solution Overview
Problem
Existing deep learning models require large amounts of labeled data for training, which is laborious and time-consuming, and there is a high degree of coupling between tasks and data, making it challenging to achieve high-performance models quickly for new tasks.
Innovation Solution
Integrate human-in-the-loop (HITL) methods to incorporate human knowledge and experience, using core set construction and active learning to select key samples for training, and develop a system that automates multimodal interface workflows with AI agents trained on diverse datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large amounts of labeled data are used for training deep learning models, then model performance is improved, but data labeling becomes more laborious and time-consuming
Solution Approach 1:
The system enables AI agents to autonomously perform data labeling tasks by executing workflows that involve observing interface states, determining labeling requirements, and generating labels without human intervention. The agent monitors UI changes, identifies elements requiring labeling, and automatically creates training data, thereby eliminating the time-consuming manual labeling process while maintaining high model performance
Solution Approach 2:
The system implements a feedback loop where the AI agent continuously monitors interface state changes, evaluates whether labeling is needed, and adjusts its labeling strategy based on observed patterns. This feedback mechanism allows the agent to efficiently identify high-value labeling targets and optimize the labeling process, reducing overall time while maintaining data quality
2Productivity
If more human knowledge is incorporated through HITL methods, then training efficiency is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary layer between the AI agent and the interface that automatically captures interface state changes and translates them into labeling tasks. This intermediary mechanism simplifies the interaction between the agent and the complex interface, enabling efficient human-in-the-loop training by automatically managing the complexity of interface monitoring and state tracking
Solution Approach 2:
The system segments the complex training process into distinct phases: interface state monitoring, change detection, labeling decision-making, and data generation. By dividing the complex HITL process into manageable segments, the system reduces overall complexity while maintaining high training efficiency through automated coordination between phases
3Adaptability or versatility
If AI agents are trained on diverse datasets, then automation capability across software tools is improved, but data management complexity increases
Solution Approach 1:
The system employs a universal data collection framework that can capture interface state changes across multiple software tools and platforms using the same monitoring mechanisms. The agent uses a unified approach to identify, observe, and label elements regardless of the specific software tool, thereby achieving diverse automation capability while managing data through a single standardized process rather than tool-specific management
Data Source
AI summary
A system for client-side implementation of an interface automation language at runtime includes agent specification logic and runtime interpretation logic. The agent specification logic, running on client-side, is configured construct an agent specification, and to make the agent specification available for server-side translation into an intermediate representation, wherein the agent specification is configured to automate a multimodal interface workflow. The runtime interpretation logic, running on client-side, is configured to receive the intermediate representation, detect one or more agent functions in the intermediate representation, generate one or more agent calls based on the agent functions, issue the agent calls to an agent and, in response, receive at least one runtime actuation function from the agent, and translate the runtime actuation function into at least one runtime actuation command, wherein the runtime actuation command triggers at least one machine-actuated action as a runtime synthetic action that automates the multimodal interface workflow.