Messaging System Response Generation Using Historical Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current messaging systems require users to manually type responses, which is time-consuming, and existing suggested response systems do not provide personalized or varied options, limiting user efficiency and convenience.
Innovation Solution
A messaging system that generates recommended responses based on historical message data using a statistical model, normalizing and grouping messages to provide personalized and varied response options, allowing users to quickly select responses rather than typing them out.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually type responses in messaging applications, then message personalization is maintained, but response time increases and user efficiency decreases
Solution Approach 1:
The system performs preliminary actions by analyzing historical message data and pre-generating a set of candidate responses before the user needs to reply. The statistical model processes past communication patterns, identifies common response types, and prepares personalized suggestions in advance, allowing users to quickly select from pre-computed options rather than typing from scratch.
Solution Approach 2:
The system creates simplified copies of previous messages by extracting essential information and generating response templates based on historical data. Instead of requiring users to recreate messages, the system copies relevant patterns from past communications and adapts them to current contexts, providing ready-to-use response suggestions that maintain personalization while reducing typing effort.
2Productivity
If suggested response systems provide generic responses, then user efficiency improves, but message personalization is lost
Solution Approach 1:
The system applies local quality by customizing response suggestions specifically for each user pair and conversation context. The statistical model analyzes historical data between specific users, identifying their unique communication patterns, preferred tone, and typical response styles. This allows the system to generate personalized candidate responses tailored to each local interaction rather than providing generic suggestions applicable to all users.
Solution Approach 2:
The system changes parameters by dynamically adjusting response characteristics based on historical message data. The statistical model varies parameters such as response length, formality level, emoji usage, and sentence structure according to the specific user pair's communication history. This allows the same response template to be adapted with different parameters to maintain personalization across various conversation contexts while still providing efficient suggestions.
3Adaptability or versatility
If the system processes extensive historical message data, then response personalization improves, but computational complexity increases
Solution Approach 1:
The system extracts only the essential and most relevant features from extensive historical message data using the statistical model. Instead of processing entire message histories, the model identifies and extracts key patterns such as common response types, typical reply times, preferred communication styles, and frequent topics. This extraction process reduces the data complexity while preserving the personalization information needed for generating accurate response suggestions.
Solution Approach 2:
The system applies partial action by focusing computational resources on processing only the most influential portions of historical data. The statistical model identifies and prioritizes key patterns from message history, processing only those elements that significantly impact response personalization. This selective processing approach achieves effective personalization without requiring exhaustive analysis of every historical message, thereby reducing overall computational complexity.
Data Source
AI summary
Disclosed are systems, methods, and non-transitory computer-readable media for generating recommended responses based on historical data. A messaging system receives a message as part of a communication session between a first client device and a second client device. The message originated from the first client device. The messaging system determines, using the message as input in a statistical model, a set of candidate responses for replying to the message. The statistical model was generated based on historical message data transmitted as part of previous communication sessions between a plurality of client devices. The set of candidate responses is a subset of a set of available candidate responses. The messaging system determines, based on a set of candidate selection rules, a subset of the candidate responses yielding a set of recommended responses to the message, and causes the set of recommended responses to be presented on the second client device.