Virtual Assistant Development System for Intent Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Natural-language systems are difficult to develop due to the need for extensive knowledge in speech recognition and text processing, limiting their ubiquity and demand across various devices.
Innovation Solution
A computing device provides a user interface that allows users to associate intents with request strings and concept tags, enabling the development of natural-language systems by categorizing sentences into intents and applying concept tags to words, with features like speech recognition, transcription correction, and grammar generation for intent and concept matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural-language systems are developed using conventional methods requiring extensive knowledge of speech recognition and text processing, then the system can achieve accurate intent recognition and concept understanding, but the development process becomes complex and difficult
Solution Approach 1:
The patent introduces an intermediary development system that mediates between the developer and the complex speech recognition/text processing infrastructure. This development system provides abstraction layers including intent templates, concept schemas, and automated annotation tools that hide the underlying complexity while maintaining recognition accuracy. The intermediary layer translates simple developer inputs into complex system configurations automatically.
Solution Approach 2:
The development process is segmented into distinct, manageable components: intent definition, concept tagging, request string association, and grammar generation. Each component can be developed and configured independently through separate interface modules, allowing developers to work on specific aspects without being overwhelmed by the entire system's complexity. This modular segmentation reduces the perceived and actual development burden.
2Ease of operation
If natural-language systems are simplified for easier development, then the development process becomes more accessible, but the system's ability to accurately recognize intents and concepts may be compromised
Solution Approach 1:
The system performs preliminary actions by pre-defining intent templates, concept hierarchies, and grammar structures before the actual development work begins. These pre-configured frameworks provide accurate semantic foundations that guide the simplification process. Developers work within these pre-established accurate frameworks rather than building from scratch, ensuring precision is maintained while easing the development process.
Solution Approach 2:
The development system incorporates self-service capabilities including automated concept suggestion, automatic grammar generation from annotated examples, and intelligent recommendation of intent associations. These self-service features reduce manual configuration requirements while maintaining or improving accuracy through algorithmic analysis of the annotated data, allowing simplified development without sacrificing precision.
3Reliability
If extensive manual annotation and configuration is performed to achieve accurate natural-language understanding, then the system's intent and concept recognition improves, but the development time and resources increase
Solution Approach 1:
The system implements feedback mechanisms where annotated examples automatically improve the system's understanding through iterative learning. As developers annotate request strings with concepts and intents, the system provides feedback by suggesting patterns, identifying inconsistencies, and recommending improvements. This feedback loop accelerates the annotation process while ensuring high reliability, as the system learns from each annotation and reduces future manual effort required for similar cases.
Solution Approach 2:
The system performs preliminary analysis and preparation by pre-processing input data, suggesting concept tags based on contextual analysis, and preparing annotated examples for review. This preliminary action reduces the time required for manual annotation by pre-configuring accurate suggestions that developers only need to verify or adjust, rather than creating from scratch. The preliminary processing maintains reliability by ensuring thorough analysis before final annotation decisions are made.
Data Source
AI summary
In accordance with aspects of the disclosure, a computing device may provide a user interface for developing an interactive natural-language response system, which may include a virtual assistant. A user may interact with a system using spoken, written (e.g., text), or other input methods. The user interface may allow a user to associate sentences with intents, tag words within the sentences with concepts, and construct a grammar using the associated intents and tagged concepts. The system may use the grammar for automatically predictively associating sentences with intents and words with concepts. The system may display in the foam of a chat transcript a single branch of a tree of a discussion between the virtual assistant and a user. The user interface may graphically display variable values to assist a user to test system responses under different simulated conditions.