Context-Aware Canned Response Generation via ML Feature Vectors
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Solution Overview
Problem
Current computing systems lack the ability to provide contextually relevant and personalized canned responses for user communications, requiring users to manually select responses, which can be time-consuming and inefficient.
Innovation Solution
A method and system that utilize machine-learning applications trained on user messaging history to generate feature vectors from conversations, allowing for the automatic selection and display of contextually relevant canned messages for incoming communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually select canned responses for communications, then the system provides basic response functionality, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system automatically generates and selects canned responses without requiring manual user input. The machine learning application analyzes incoming messages and autonomously generates contextually relevant responses, allowing the system to serve itself rather than requiring user intervention for each response selection
Solution Approach 2:
The system pre-generates multiple candidate responses before the user needs to reply. By analyzing the incoming message and generating potential responses in advance, the system prepares options that can be quickly selected or automatically sent, eliminating the need for real-time response composition
2Adaptability or versatility
If the system provides generic canned responses, then response generation is simple, but the responses lack contextual relevance and personalization
Solution Approach 1:
The system changes the parameters of response generation by using machine learning models that analyze multiple features of incoming messages (content, sender, context, timing). This transforms the response generation process from static template selection to dynamic, parameter-driven generation that adapts to specific communication contexts
Solution Approach 2:
The machine learning application acts as an intermediary between the incoming message and the canned responses. It analyzes the message content and selects or generates appropriate responses by mediating between the raw input and the response options, enabling contextual relevance without requiring complex user intervention
3Productivity
If no automated response system is used, then the system remains simple, but users must manually compose every response which is inefficient
Solution Approach 1:
The system performs partial automation by generating and presenting multiple canned response options rather than fully automating the selection process. This approach provides enough automation to improve efficiency while allowing user review and selection, avoiding the complexity and potential errors of complete automation
Data Source
AI summary
A method may involve, for each of one or more messages that are selected from a plurality of messages from an account: (a) extracting one or more phrases from a respective selected message; (b) determining that a conversation includes the respective selected message and one or more other messages from the plurality of messages; (c) generating a first feature vector based on the conversation, wherein the first feature vector includes one or more first features, wherein the one or more first features include one or more words from the conversation; and (d) generating, by a computing system, one or more training-data sets, wherein each training-data set comprises one of the phrases and the first feature vector. The method may further involve: training, by the computing system, a machine-learning application with at least a portion of the one or more training-data sets that are generated for the one or more selected messages; applying the trained machine-learning application to process an incoming message to the account; and responsive to applying the trained machine-learning application, determining one or more reply messages corresponding to the incoming message, wherein the one or more reply messages include at least one of the extracted one or more phrases.