Context-Aware Shorthand Sentence Generation Model
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Solution Overview
Problem
Current AI systems lack the ability to generate sentences that are contextually relevant to user inputs, particularly when users input shorthand words, as they do not adequately consider context information such as user profiles, locations, or application types.
Innovation Solution
An apparatus and method that utilize a data recognition model to analyze shorthand words and context information, generating optimal sentences by learning correlations between shorthand words and context data, including user input patterns, application characteristics, and environmental factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If statistical values of past inputs are used to assist user input, then input efficiency is improved, but the system cannot generate contextually relevant sentences
Solution Approach 1:
The system pre-collects and stores various types of context information (user profiles, location data, application types, time information) in advance through the context information acquisition unit. This preliminary action enables the system to have context data ready before sentence generation is needed, allowing it to go beyond simple statistical matching and provide contextually relevant sentence suggestions that adapt to the current situation.
2Speed
If AI systems process user inputs without context information, then processing speed is maintained, but sentence relevance to user context deteriorates
Solution Approach 1:
The system divides the sentence generation process into distinct functional modules: a context information acquisition unit that collects relevant data, a data recognition model that processes the input, and a sentence generation unit that produces output. This segmentation allows each module to specialize in its task, maintaining processing efficiency while improving overall sentence relevance through dedicated context analysis.
Solution Approach 2:
The patent introduces context information as an intermediary element between user input and sentence generation. This intermediary layer processes and integrates additional contextual data (user profiles, location, application type) before generating the final sentence suggestions, thereby improving relevance without significantly impacting processing speed due to the modular architecture.
3Measurement precision
If multiple context factors are considered for sentence generation, then sentence accuracy is improved, but system complexity increases
Solution Approach 1:
The data recognition model is designed as a universal component that handles multiple types of context information (user profiles, location data, application types, time information) through a single integrated processing framework. This multi-functionality allows the system to consider various context factors for improved sentence accuracy without proportionally increasing system complexity, as the same core model adapts to different input types.
Data Source
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AI summary
Disclosed are an apparatus and/or method for providing a sentence based on a user input. When a sentence corresponding to a shorthand word input from a user is generated and provided to the user, a sentence most suitable for a current context is provided to the user by further considering context information. At least a portion of the method for providing a sentence based on a user input may be performed using a rule-based model and/or an artificial intelligence model learned according to at least one of neural network or deep learning algorithms. The rule-based model and/or artificial intelligence model may provide a sentence most suitable for a current context to the user by using the input shorthand word and the context information as input values.