AI Chat Expression Recommendation via Message Analysis
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Solution Overview
Problem
Existing chat services require users to manually select emoticons or expression items to convey emotions, which is time-consuming and inefficient.
Innovation Solution
A chat service method and apparatus using a deep learning network to analyze user messages and recommend expression items matching the user's intention by converting between various forms of expression items, including images, videos, and text, through a message analyzer and artificial intelligence processor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually select emoticons or expression items in existing chat services, then users can express their emotions, but it takes a great deal of time and is inefficient
Solution Approach 1:
The system automatically analyzes user input messages and recommends appropriate expression items without requiring manual selection. The message analyzer processes the user's message content and the AI processor generates recommendations, enabling the system to serve itself in identifying and suggesting expression items that match the user's intended emotion or meaning.
Solution Approach 2:
The system pre-analyzes the user's message content using the message analyzer before the user needs to select an expression item. By performing the analysis and generating recommendations in advance based on the message content, the system prepares suitable expression items ready for immediate presentation to the user, reducing the time required for selection.
2Measurement precision
If the system analyzes user messages and recommends expression items using deep learning, then the accuracy of emotional expression improves, but the device complexity increases
Solution Approach 1:
The system divides the complex task of expression item recommendation into distinct functional modules: a message analyzer that processes input messages and extracts features, and an AI processor that generates recommendations based on the analyzed features. This segmentation allows each component to specialize in specific processing tasks, improving overall accuracy while managing complexity through modular design.
Solution Approach 2:
The message analyzer serves as an intermediary between the user's raw message input and the AI processor. It pre-processes and structures the message content, extracting relevant features and attributes that the AI processor then uses to generate accurate recommendations. This intermediary layer simplifies the AI processor's task and improves the overall system's ability to accurately understand user intent.
Data Source
AI summary
A chat service providing apparatus may include a message analyzer and an artificial intelligence processor. The message analyzer may identify a first expression item included in at least one chat message received from a user terminal, analyze an attribute allocated to the first expression item on the basis of an analysis medium frame, and determine a user's intention according to a result of analyzing the attribute. The artificial intelligence processor may recommend a second expression item from among a plurality of registered expression items on the basis of a conversion condition which is an algorithm for selecting an expression item having an attribute corresponding to the user's intention among the plurality of expression items. The analysis medium frame may include, as an attribute, at least one of a description of an expression item, a reference emoji, a representative intension, and a representative language.