Natural Language Understanding System for Complex Task Automation
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Solution Overview
Problem
Current personal assistant applications are limited in their ability to interpret complex natural language commands and automate tasks across multiple domains, requiring users to manually specify services and undergo lengthy configuration processes, which is burdensome and requires programming knowledge.
Innovation Solution
A natural language understanding (NLU) system that analyzes user inputs to determine trigger and action domains, identifies appropriate APIs, extracts API parameters, and automatically fulfills API calls, allowing users to perform complex tasks through verbal or voice instructions without extensive programming knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional services are used to automate tasks, then task automation capability is improved, but user configuration time and complexity increase significantly
Solution Approach 1:
The system performs self-service by automatically analyzing natural language inputs, identifying intents and entities, selecting appropriate services and APIs, and configuring tasks without requiring user intervention in the configuration process. The system serves itself by converting user-friendly natural language directly into executable task configurations.
Solution Approach 2:
The natural language processing system acts as an intermediary between the user's simple verbal command and the complex service configuration system. It translates human language into structured service parameters and API calls, eliminating the need for users to directly configure services.
2Extent of automation
If conventional services are used to automate tasks, then task automation capability is improved, but ease of operation deteriorates due to programming knowledge requirements
Solution Approach 1:
The system replaces the mechanical process of manual service configuration and API programming with natural language processing. Instead of requiring users to mechanically configure services through technical interfaces, the system uses NLP to interpret and execute user intents directly.
Solution Approach 2:
The natural language processing system serves as an intermediary that shields users from the complexity of service configurations and programming requirements. Users interact only with natural language, while the intermediary handles all technical translation and configuration automatically.
3Ease of operation
If personal assistant applications provide simple command interfaces, then ease of operation is improved, but functionality and automation ability deteriorate
Solution Approach 1:
The system achieves multi-functionality by enabling a single natural language interface to perform diverse complex tasks across multiple domains. It can handle notifications, media control, messaging, and other automated tasks through the same simple interface, making the system both easy to use and highly capable.
Solution Approach 2:
The system performs preliminary action by pre-configuring service connections and API integrations in the background. This allows users to simply issue natural language commands without needing to understand or configure the underlying complex automation mechanisms, achieving both simplicity and advanced functionality.
4Adaptability or versatility
If systems interpret complex natural language commands, then functionality is improved, but device complexity increases
Solution Approach 1:
The system segments the complex natural language processing task into distinct components: intent identification, entity extraction, service selection, and API parameter mapping. This segmentation allows each component to be handled independently and systematically, managing overall system complexity through modular processing.
Data Source
AI summary
Embodiments of the invention provide a natural language understanding (NLU) system that performs relatively complex task automations via verbal or voice instructions. Normally, task automations have a trigger condition, and a series of one or more actions that would require a user's selection of an option via manual input. This is because the underlying machine logic requires knowledge and classification capabilities from multiple domains that conventional personal assistants are not capable of. Embodiments of the inventive system and methods provide a solution to this complex task by analyzing trigger and action domains, pinpointing appropriate APIs, extracting corresponding API parameters, and automatically fulfilling API calls for the user. The system is configured to parse natural language commands into API calls. It analyzes both the trigger and action APIs and prompts a user for any missing information if necessary.