Agent Runtime Architecture for Multimodal Interface 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, limiting their performance and scalability.
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 for automating multimodal interface workflows with AI agents that can understand and act on user intents.
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 annotation cost and time consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by using synthetic data generation and pre-training on unlabeled data before fine-tuning with limited labeled data. This prepares the model in advance to reduce the amount of labeled data needed for achieving high performance.
Solution Approach 2:
The patent introduces synthetic data as an intermediary between unlabeled data and labeled data. Synthetic samples generated from pre-trained models serve as a bridge, allowing the system to leverage large amounts of unlabeled data while reducing dependency on expensive labeled data.
2Reliability
If more model parameters are added to increase model capacity, then description capability is improved, but data growth rate cannot keep pace with parameter growth rate
Solution Approach 1:
The system applies partial action by using only a small subset of labeled data (core set) for fine-tuning large models. Instead of requiring labeled data proportional to model size, the method achieves good performance with minimal labeled examples through pre-training on abundant unlabeled data.
Solution Approach 2:
The model is pre-trained on large amounts of unlabeled data before being fine-tuned on a small labeled set. This preliminary action on unlabeled data allows the model to learn general representations that transfer well to downstream tasks, reducing the data-to-parameter ratio requirement.
3Productivity
If human-in-the-loop methods are used to incorporate human knowledge, then training efficiency with minimal labeled data is improved, but system complexity increases
Solution Approach 1:
The system implements self-service through automated synthetic data generation and automatic core set selection algorithms. Human experts only need to provide minimal labeled examples, while the system automatically handles data synthesis, selection, and model training, reducing the burden of manual annotation and system management.
4Ease of manufacture
If core set construction and active learning are used to select key samples, then annotation cost is reduced, but the coupling between tasks and data remains a challenge
Solution Approach 1:
The system achieves universality by training models on diverse unlabeled data from multiple sources and domains during pre-training. This multi-functional pre-training enables the model to adapt to various downstream tasks with minimal task-specific labeled data, reducing annotation costs while maintaining versatility.
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.