Isolated AI Agent Containers for Private Grounding Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users are concerned about data privacy and lack of visibility into how AI models generate responses due to their remote hosting, which can lead to data retention and inaccurate responses.
Innovation Solution
Implementing localized AI agents that operate on user devices, using isolated containers for grounding data, allowing users to control and view the information sources, and ensuring data does not leave the device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If AI models are hosted in the cloud, then computational demands are met, but data privacy concerns arise and users lack visibility into data usage
Solution Approach 1:
The system segments AI functionality into localized agents that run on user devices rather than centralized cloud models. Each device hosts its own AI agent that processes data locally, dividing the computational task from data storage and processing to maintain privacy while providing adequate computational capacity for local operations.
Solution Approach 2:
The patent introduces an intermediary layer of localized AI agents that act as mediators between users and remote AI services. These agents handle data processing locally and only interact with cloud services when necessary, protecting user data while maintaining access to advanced AI capabilities through controlled communication protocols.
2Adaptability or versatility
If remote AI models are used, then advanced AI capabilities are accessed, but users cannot control or view the information sources used by the model
Solution Approach 1:
The system enables self-service through localized AI agents that autonomously manage their own data processing and grounding information retrieval. Users can configure their local agents to access specific data sources, and the agents independently handle the complexity of information gathering and processing while maintaining user-defined constraints and preferences.
Solution Approach 2:
The patent segments the AI system into modular components including local agents, grounding information sources, and optional cloud services. This segmentation allows users to selectively enable or disable specific data sources and AI capabilities, providing granular control over information flow while maintaining access to advanced AI functions through modular architecture.
3Power
If cloud-based AI models are deployed, then computational power is sufficient, but data retention and security cannot be guaranteed
Solution Approach 1:
The patent extracts sensitive data processing operations from centralized cloud systems and relocates them to local devices. By taking out the data processing function and placing it locally, the system eliminates the risk of cloud-based data retention while maintaining computational power through local execution of AI agents and their associated data processing pipelines.
Solution Approach 2:
The system transitions from a single-dimensional cloud-based architecture to a multi-dimensional distributed architecture where AI agents operate locally on user devices. This dimensional shift distributes computational power across multiple local nodes rather than concentrating it in the cloud, thereby reducing data retention risks while maintaining aggregate computational capability through the network of local agents.
Data Source
AI summary
A first AI agent is configured to obtain grounding information (e.g., information that will be used, by the first AI agent, to formulate a response to a query) from a first set of files and/or embeddings in a container associated with the AI agent. A second AI agent is configured to obtain grounding information from a second set of files and/or embeddings in a second container. Neither AI agent has access to files that are outside of the container associated with the AI agent. The container may be a folder into which a user can drag and drop files, and therefore the user may have visibility into (and control over) the information that is used as grounding information for each of the AI agents. An AI agent can be nested within a container for another AI agent to provide additional grounding information.