Multimodal Interface Agents with Feedback-Driven Learning
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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 enhance model performance and adaptability across diverse tasks.
Innovation Solution
A system for automating artificial intelligence-based multimodal agentic workflows using a Transformer model that integrates human-in-the-loop (HITL) methods, active learning, and core set construction to reduce data annotation costs and improve model performance, enabling agents to understand and interact with software tools through multimodal interfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep learning models are trained with large amounts of labeled data, then model performance is improved, but data annotation cost and time consumption increase
Solution Approach 1:
The patent implements a feedback mechanism where users provide corrections and feedback on agent actions. This feedback is used to refine the agent's behavior through continuous learning, reducing the need for extensive pre-labeled data. The system learns from actual usage patterns and user corrections to improve performance over time.
Solution Approach 2:
The system enables self-service learning where the agent automatically learns from its own actions and user feedback without requiring manual annotation of training data. The agent can autonomously improve its skills by observing user corrections and incorporating them into its learning model, eliminating the need for time-consuming manual data preparation.
2Reliability
If deep learning models are trained with large amounts of labeled data, then model performance is improved, but annotation cost increases
Solution Approach 1:
The system uses feedback from user corrections and interactions to train the agent, replacing the need for expensive manual annotation processes. Users provide feedback on agent actions, and this feedback is used to refine the agent's model, significantly reducing annotation costs while maintaining high performance.
Solution Approach 2:
The agent performs self-service learning by automatically processing user feedback and incorporating it into its training model. This eliminates the need for costly professional annotators and reduces overall annotation expenses while achieving comparable or superior model performance.
3Extent of automation
If existing AI models are used, then basic tasks can be automated, but adaptability to diverse software tools and interfaces is limited
Solution Approach 1:
The system employs dynamic learning where the agent continuously adapts its behavior based on real-time feedback from users and interactions with different software tools. The agent's model is continuously updated and refined through feedback loops, enabling it to adapt to diverse interfaces and tools without requiring retraining for each specific application.
Solution Approach 2:
The agent is designed with universal capabilities to interact with multiple types of software tools and interfaces through a common feedback-driven learning framework. Rather than being specialized for single applications, the agent can generalize its skills across diverse domains by learning from varied user feedback and interactions.
Data Source
AI summary
A system for constructing prompts that cause an agent to automate multimodal interface workflows includes agent specification logic and agent calling logic. The agent specification logic is configured to construct agent specifications using prompts and agent functions, wherein the agent specifications are configured to automate a multimodal interface workflow. The agent calling logic is in communication with the agent specification logic and is configured to translate the agent specifications into agent calls that cause an agent to implement the agent functions to produce outputs that are responsive to the prompts.