Reply Content Determination Using Corpus Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face challenges in efficiently formulating replies to electronic communications, as existing methods lack automated suggestions based on the content of the original messages, leading to unnecessary typing and potential misresponses.
Innovation Solution
A method and system that analyze a corpus of electronic communications to determine relationships between original message features and reply content, using machine learning to provide suggested reply text based on these relationships, allowing for auto-population or presentation as options without requiring user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually type replies to electronic communications, then they can control the exact wording, but it increases time consumption and typing effort
Solution Approach 1:
The system performs preliminary action by analyzing the original communication and automatically generating suggested reply content before the user needs to compose the response. The machine learning model processes the incoming electronic communication, identifies key information, and pre-composes suggested replies that can be instantly inserted, eliminating the need for users to type responses from scratch and significantly reducing composition time.
Solution Approach 2:
The system enables self-service by automatically generating reply content based on the original communication without requiring extensive user input. The machine learning model independently analyzes the communication context, determines appropriate response content, and presents suggestions that users can accept with minimal modification, allowing the system to serve itself rather than requiring manual composition for each detail.
2Reliability
If users manually type replies, then they can ensure accuracy, but it increases the risk of misresponses and errors
Solution Approach 1:
The system implements feedback by presenting machine-generated suggested replies to the user for review and selection. The user receives feedback in the form of automatically generated response options that reflect accurate understanding of the original communication, allowing them to verify the content before sending. This feedback mechanism reduces misresponses by providing pre-vetted options while maintaining user control over the final output.
3Reliability
If the system analyzes a large corpus of communications to determine reply patterns, then it can improve reply accuracy, but it increases system complexity
Solution Approach 1:
The system replaces manual mechanical processes with automated machine learning algorithms. Instead of requiring complex manual analysis of communication patterns, the patent employs trained machine learning models that automatically identify relationships between original communications and appropriate replies. The system substitutes human analytical effort with computational patterns recognition, reducing the operational complexity burden on users while maintaining high accuracy through sophisticated algorithmic analysis.
Data Source
AI summary
Methods and apparatus related to determining reply content for a reply to an electronic communication. Some implementations are directed generally toward analyzing a corpus of electronic communications to determine relationships between one or more original message features of “original” messages of electronic communications and reply content that is included in “reply” messages of those electronic communications. Some implementations are directed generally toward providing reply text to include in a reply to a communication based on determined relationships between one or more message features of the communication and the reply text.