Virtual Assistant User Classification via Grammar Slot Analysis
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Solution Overview
Problem
Virtual assistant systems face challenges in accurately classifying users based on their natural language expressions due to the limitations of existing classification algorithms and the need for efficient handling of large datasets, particularly in providing high-value classifications without compromising user privacy.
Innovation Solution
The implementation of machine learning algorithms that classify virtual assistant users based on grammar slot values, utilizing modular domain-specific grammars and client-server architecture to interpret user expressions, and providing training data for improved natural language understanding, while maintaining user privacy by not sharing personal information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning algorithms are used to classify users based on grammar slot values, then user classification accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the classification process into multiple components: grammar slot extraction, feature selection, training data generation, and classification. Each component handles a specific aspect of the overall classification task, making the complex system more manageable and maintainable while achieving high accuracy
Solution Approach 2:
The patent introduces intermediary components such as feature selection modules and training data generation systems that bridge the gap between raw grammar slot values and final user classifications. These intermediaries process and transform data in controlled stages, reducing overall system complexity
2Measurement precision
If large datasets are used for training classification algorithms, then classification accuracy is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing training data, selecting relevant features in advance, and generating training datasets before the actual classification task. This preparation work reduces the processing time during runtime while maintaining high classification accuracy
Solution Approach 2:
The patent applies feature selection to focus on the most relevant grammar slot values for classification, rather than processing all possible features. This partial action approach processes only the necessary subset of data, reducing processing time while maintaining or improving accuracy by eliminating noise
3Loss of information
If detailed user information is collected for classification, then classification value is improved, but user privacy is compromised
Solution Approach 1:
The system extracts only the necessary grammar slot values from user expressions that are relevant for classification purposes. By taking out only the essential features needed for classification rather than collecting all user information, the system maintains classification value while protecting user privacy
Solution Approach 2:
The patent applies different levels of data collection and processing to different aspects of user information. Sensitive personal information is handled differently from public expression data, with only the necessary local portions of user data being collected and processed for classification
Data Source
AI summary
A virtual assistant processes natural language expressions according to grammar rules created by domain providers. The virtual assistant uniquely identifies each of a multiplicity of users and stores values of grammar slots filled by natural language expressions from each user. The virtual assistant stores histories of slot values and computes statistics from the history. The virtual assistant provider, or a classification client, provides values of attributes of users as labels for a machine learning classification algorithm. The algorithm processes the grammar slot values and labels to compute probability distributions for unknown attribute values of users. A network effect of users and domain grammars make the virtual assistant useful and provides increasing amounts of data that improve classification accuracy and usefulness.