Private AI User Model for Context-Aware Personal Companions
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Solution Overview
Problem
Existing robotic companions lack the ability to provide personalized and contextually relevant assistance due to limited processing power and restricted form factors, limiting their interaction capabilities and adaptability to user needs.
Innovation Solution
An autonomous personal companion utilizing a deep learning engine to build a personalized AI model that integrates with mobile platforms, allowing it to interact with various digital assets, move autonomously, and process data locally or through a back-end server to provide contextually relevant assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If early robotic pets were equipped with basic computer capabilities and sensors, then they could provide limited companionship and interact with owners, but their processing power and form factors remained restricted, limiting their intelligence and adaptability
Solution Approach 1:
The system divides processing between the robotic companion device and remote servers. The robot performs local sensing and basic processing, while complex AI model training and personalized assistance generation occur on remote servers, allowing high adaptability without requiring full processing power in the robot itself
Solution Approach 2:
A communication system acts as an intermediary between the robotic companion and users, transmitting data and commands between the device and remote servers. This enables the robot to access sophisticated processing capabilities externally while maintaining a simple form factor
2Ease of operation
If the robotic companion uses a personalized AI model for contextually relevant assistance, then interaction quality improves, but data processing requirements and computational resources increase
Solution Approach 1:
The system pre-generates personalized AI models using user data collected over time, and stores these models for later use. This allows the robot to provide contextually relevant assistance without performing complex real-time processing during user interactions
Solution Approach 2:
Instead of running complex AI processing in the resource-constrained robot, the system creates simplified copies or representations of user behavior patterns and stores them locally. These copied patterns enable contextual assistance with minimal computational resources
3Adaptability or versatility
If the companion collects and processes extensive user data for personalization, then service tailoring improves, but privacy concerns and data security requirements increase
Solution Approach 1:
The system extracts only the essential personalization parameters needed for effective assistance and removes unnecessary sensitive data. By taking out only what is needed for personalization while leaving out sensitive information, the system achieves personalization capability while reducing privacy risks
Data Source
AI summary
A method for building an artificial intelligence (AI) model. The method includes accessing data related to monitored behavior of a user. The data is classified, wherein the classes include an objective data class identifying data relevant to a group of users including the user, and a subjective data class identifying data that is specific to the user. Objective data is accessed and relates to monitored behavior of a plurality of users including the user. The method includes providing as a first set of inputs into a deep learning engine performing AI the objective data and the subjective data of the user, and a plurality of objective data of the plurality of users. The method includes determining a plurality of learned patterns predicting user behavior when responding to the first set of inputs. The method includes building a local AI model of the user including the plurality of learned patterns.