Dialogue History Vector Encoding for Privacy-Preserving Response Control
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Solution Overview
Problem
Existing systems struggle to accurately interpret user intent and generate precise responses in dialogue systems due to challenges in recognizing non-standard language, ambiguity in user requests, and privacy concerns when using another user's dialogue history.
Innovation Solution
An electronic apparatus that vectorizes and stores user dialogue history using encoding algorithms like BERT or RoBERTa, allowing it to generate accurate responses by leveraging both current and encoded previous user dialogue history, while addressing privacy issues by encoding rather than storing raw data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user dialogue history is stored in raw text form to improve response accuracy, then the system can better understand user intent, but privacy breaches and data security risks increase
Solution Approach 1:
The patent creates vector representations as copies of the semantic meaning of dialogue history without storing the original text. These vector embeddings capture the essence of user intent while being privacy-preserving numerical representations that cannot be reverse-engineered to reveal sensitive information.
Solution Approach 2:
The patent transforms dialogue history from text domain to vector space domain by applying encoding algorithms. This parameter change converts readable text into numerical representations that preserve semantic relationships while eliminating privacy risks associated with storing raw text data.
2Measurement precision
If encoding algorithms like BERT or RoBERTa are used to vectorize dialogue history, then response accuracy improves through better semantic understanding, but computational complexity and processing time increase
Solution Approach 1:
The patent performs encoding of dialogue history in advance and stores the pre-computed vector representations. This preliminary action eliminates the need for real-time encoding during conversation, significantly reducing computational complexity and response latency while maintaining high response accuracy.
Solution Approach 2:
The patent extracts only the essential semantic features from dialogue history by creating compact vector representations. This extraction process removes redundant information while preserving the core meaning, reducing computational burden compared to processing full text during conversation.
3Manufacturing precision
If vectorized dialogue history is used to improve response precision, then the system can handle non-standard language better, but the system complexity increases due to encoding and decoding requirements
Solution Approach 1:
The patent implements a universal encoding scheme that handles diverse language inputs including non-standard language, slang, and ambiguous expressions through a single vectorization framework. This multi-functional approach manages various language types without requiring separate processing pipelines, controlling system complexity.
Data Source
AI summary
An electronic apparatus is provided. The electronic apparatus includes a communication device, a memory configured to store at least one instruction and one or more vector values corresponding to dialogue history information, and a processor, based on execution of the at least one instruction, configured to extract text from the dialogue content received through the communication device, calculate a vector value of the extracted text by using a predetermined encoding algorithm, and generate response information by using the calculated vector value and the stored one or more vector values.