Machine Learning Task Classifier for Personal Assistants
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Solution Overview
Problem
Conventional personal assistant software applications are error-prone due to variability in user phrasing and lack language independence, making them difficult to accurately classify and execute user requests across different languages.
Innovation Solution
A machine learning-based system that uses a high-quality training database generated from diverse user requests to create a task classifier model, allowing for robust classification and execution of user requests in real-time, independent of language, by converting user inputs into feature vectors and employing algorithms like support vector machines to predict task categories and extract specific queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional personal assistant software applications are used to process user requests, then the system is simple to implement, but the accuracy of task classification deteriorates due to variability in user phrasing
Solution Approach 1:
The patent applies preliminary action by collecting and storing numerous example user requests for each task category in advance during an offline training phase. These pre-collected examples form a training database that captures the variability of human phrasing. During online operation, the system simply compares new requests against this pre-built database using machine learning algorithms, achieving high accuracy without complex real-time processing.
2Measurement precision
If a large training database with diverse user requests is collected to improve classification accuracy, then the measurement precision improves, but the quantity of data and processing complexity increases
Solution Approach 1:
The patent uses copying by creating a comprehensive training database that replicates the diverse ways users phrase requests for each task category. Instead of processing raw varied inputs directly, the system creates standardized example copies of user requests for each category (e.g., multiple examples of how users ask to send messages, make calls, etc.). The machine learning algorithm learns from these copied examples and can then accurately classify new requests by pattern matching.
3Reliability
If conventional ad hoc approaches are used for task classification, then the system is easy to implement, but the reliability of task execution deteriorates due to language-specific limitations
Solution Approach 1:
The patent applies universality by designing a machine learning-based classification system that is language-independent. The system collects training examples from multiple languages and cultures, and the machine learning algorithm learns universal patterns of request structure that transcend specific languages. This allows the same system to reliably classify user requests in any language without requiring language-specific programming rules, making the personal assistant universally applicable.
Data Source
AI summary
A system and method is provided that processes a training database of human-generated requests in each of a plurality of task categories with a machine learning algorithm to develop a task classifier model that may be applied to subsequent user requests to determine the most likely one of the task categories for the subsequent user request.