Method and system for predicting response and behavior on chats
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Solution Overview
Problem
Users face challenges in multi-user and multi-window chat environments, including difficulty in finding relevant messages to respond to, fear of missing urgent responses, and the risk of sending messages to the wrong chat window due to the high volume of incoming messages.
Innovation Solution
A method and system that predict potential query messages in group chats and correctly identify the intended chat window for responses, utilizing a user interface for inputting messages, generating lists of correlated candidate messages, predicting relevant messages, and displaying priority lists to assist users in responding accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually search for messages to respond to in continuous chats, then they can find relevant messages, but it consumes significant time and effort especially when bulk messages are sent by other users
Solution Approach 1:
The system performs preliminary analysis of chat messages to identify potential reply targets before the user needs to respond. It pre-processes incoming messages, analyzes their content and context, and prepares suggested reply candidates in advance, so when the user needs to respond, the relevant messages are already identified and presented.
Solution Approach 2:
The manual mechanical process of scrolling through and searching for messages is replaced with an automated intelligent system. The system uses natural language processing and machine learning algorithms to automatically analyze message content, determine relevance, and generate suggested replies, substituting the user's manual search effort with automated computational analysis.
2Adaptability or versatility
If users have multiple open chat windows with many messages, then they can access different conversations, but it becomes difficult to find topics of importance and determine where to reply first
Solution Approach 1:
The system applies different analysis and prioritization criteria to different chat windows and messages within them. It evaluates each message's importance, urgency, and relevance locally, then presents customized suggested replies for each context, allowing the user to quickly identify which topics require attention without manually scanning all windows.
Solution Approach 2:
The system acts as an intermediary between the user and multiple chat windows. It monitors all open conversations, analyzes their content and context, and provides a unified interface with suggested replies that indicates priority and relevance, mediating the complexity of multi-window management and helping the user focus on important topics.
3Productivity
If messages are sent rapidly in group chats, then communication efficiency increases, but the risk of sending messages to the wrong chat window increases
Solution Approach 1:
The system provides feedback to the user before message sending by analyzing the current context and suggesting the most appropriate chat window and recipient. It monitors the user's typing behavior, analyzes the message content, and provides real-time suggestions or confirmations to ensure the message is sent to the intended destination, creating a feedback loop that prevents errors.
Solution Approach 2:
The system performs preliminary verification of the intended message destination before the user sends the message. It analyzes the message content, determines the most appropriate chat window and recipients based on context and history, and presents this information to the user in advance, allowing correction of potential errors before final sending.
Data Source
AI summary
Provided is a system for predicting a response of a user in a multi-user environment. The system includes a display engine configured to display a user interface (UI) on a display screen for inputting a message corresponding to received messages from one or more contacts, a first processing engine configured to generate a list of correlated candidate messages associated with an input message using received messages, a second processing engine configured to predict a set of correlated candidate messages relevant to the input message from the list of correlated candidate messages, and a third processing engine configured to generate a priority list of candidate messages including the predicted set of correlated candidate messages. The display engine displays, on the display screen, at least one of one or more appropriate candidate messages based on a priority associated with the appropriate candidate messages in the generated priority list of candidate messages.