Natural Language Processing for OS Task Automation
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Solution Overview
Problem
Existing systems require pre-registration of a large number of rules to accurately support context-dependent meaning differences and inconsistencies in written forms for the same word, making them inefficient in analyzing natural language inputs.
Innovation Solution
An information processing apparatus that includes an acceptance unit for process requests, a specifying unit to identify operation tasks, an extraction unit for text analysis to extract answer items from input natural language, and an execution unit to execute operation tasks based on the extracted answer items, without the need for pre-registration of rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of rules are pre-registered to support context-dependent meaning differences and inconsistencies in written forms, then the accuracy of natural language analysis is improved, but the system complexity and maintenance burden increase significantly
Solution Approach 1:
The patent replaces the mechanical rule-based system with a machine learning-based natural language processing system. Instead of manually registering rules to handle context-dependent meanings and written form inconsistencies, the system uses trained models that automatically learn and adapt to these variations from data, thereby maintaining high accuracy while eliminating the complexity of rule management
Solution Approach 2:
The system enables self-service by allowing the natural language processing model to automatically adapt to new contexts, meanings, and written form variations through continuous learning from user interactions and data, without requiring manual rule updates or expert intervention for each new scenario
2Reliability
If pre-registration of rules is performed to handle context-dependent meanings, then the reliability of information extraction is improved, but the time and resources required for system setup increase
Solution Approach 1:
The patent applies preliminary action by pre-training the machine learning model on comprehensive datasets that cover various contexts, meanings, and written form variations before deployment. This initial training phase establishes the reliability foundation, and the model then maintains this reliability through automatic adaptation during operation without requiring additional setup time for specific scenarios
3Adaptability or versatility
If manual rule registration is required to support inconsistencies in written forms for the same word, then the adaptability of the system is improved, but the ease of operation deteriorates
Solution Approach 1:
The patent replaces the manual rule-registration mechanism with an automated machine learning system that naturally handles written form variations. The model learns to recognize and adapt to different written forms of the same word through training on diverse data, providing high adaptability while maintaining ease of operation since no manual intervention is needed
Solution Approach 2:
The system implements dynamics by enabling the natural language processing model to continuously adapt and evolve its understanding of written form variations through ongoing learning from user interactions and new data, allowing the system to become more adaptable over time without increasing operational complexity
Data Source
AI summary
An information processing apparatus according to the present invention includes: an acceptance unit that accepts a process request to an operation system; a specifying unit that, based on the process request, specifies an operation task to be executed in the operation system; an extraction unit that performs text analysis on the process request and extracts an answer item corresponding to an input item required at execution of the operation task from the process request; and an execution unit that executes the operation task based on the answer item.