Social Agent Personalization via Client-Filled Dialogue Templates
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Solution Overview
Problem
Existing systems for personalizing interactions with non-human social agents face challenges in ensuring user data security and privacy, leading to concerns about data breaches and liability.
Innovation Solution
The system enables automated determination of naturalistic expressions by social agents while allowing users to selectively sequester their personal data, using automated methods to create personalized dialogues without exposing sensitive information to the social agent system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If user data is acquired and stored to personalize social agent interactions, then personalization quality is improved, but data security and privacy risks increase
Solution Approach 1:
The system segments user data into multiple categories (demographic data, interaction history, preferences) and applies different privacy protection mechanisms to each segment. The social agent receives only the minimum necessary data segments for personalization, while sensitive segments remain protected through federated learning or differential privacy techniques.
Solution Approach 2:
An intermediary privacy protection layer is introduced between the user data and the social agent system. This intermediary uses federated learning to train models on user data without exposing the raw data, or applies differential privacy to add noise to the data before processing, thereby enabling personalization while maintaining security.
2Adaptability or versatility
If comprehensive user data is collected for personalization, then interaction realism is improved, but liability for data breaches increases
Solution Approach 1:
The system extracts only the essential features and patterns from comprehensive user data that are necessary for realistic interaction, while removing or anonymizing the actual sensitive information. For example, instead of storing and processing full interaction histories, the system extracts behavioral patterns and preferences that achieve the same personalization effect with reduced liability.
Solution Approach 2:
The system creates synthetic copies or anonymized versions of user data that preserve the statistical properties and interaction patterns needed for realistic personalization, while eliminating personally identifiable information. These synthetic datasets enable training and operation without exposing real user data.
3Object-affected harmful factors
If user data access is restricted for privacy protection, then data security is improved, but personalization capability deteriorates
Solution Approach 1:
The system performs preliminary processing of user data through federated learning or differential privacy mechanisms before the social agent needs to access it. This preliminary action embeds the personalization information into the model parameters or anonymized data structures, allowing the social agent to function with restricted direct access to raw user data.
Solution Approach 2:
The system changes the parameter representation of user data from raw personal information to transformed features that preserve personalization capability while enhancing security. For example, converting demographic data into aggregated statistical parameters or using homomorphic encryption to enable computations on encrypted data without decryption.
Data Source
AI summary
A social agent system includes a computing platform having processing hardware and a system memory storing a social agent software code. The processing hardware is configured to execute the social agent software code to receive, from a client system, input data provided by a user of the client system when the user is interacting with the social agent system using the client system, and to select, using the input data, a dialogue template for responding to the user. The dialogue template includes one or more placeholder fields to be filled by the client system to create a personalized dialogue for responding to the user. The processing hardware is further configured to execute the social agent software code to deliver, to the client system, the dialogue template including the one or more placeholder fields to be filled by the client system to create the personalized dialogue for responding to the user.