Automated Response Generation for Communication Messages
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Solution Overview
Problem
Users face inefficiencies in responding to large volumes of communication messages across various platforms, as reading or listening to each message to draft a response is time-consuming and inefficient, especially in a time-sensitive and configurable manner.
Innovation Solution
A database server implements natural language processing (NLP) and machine-learning techniques to analyze communication messages, classify them, and suggest actionable responses, allowing users to automatically generate and send responses based on insights and metadata extraction, with user-specific configurations and cross-device action tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users read or listen to each communication message to draft a response, then response quality can be maintained, but time consumption increases significantly
Solution Approach 1:
The system enables self-service by automatically analyzing communication messages and generating draft responses without requiring user intervention. The NLP model processes incoming messages, extracts key information, and produces suggested responses that users can review and send with minimal effort, thus resolving the contradiction between maintaining response quality and reducing time consumption.
Solution Approach 2:
The patent replaces the mechanical process of manually reading and drafting responses with an automated NLP-based system. The machine learning model performs text analysis, information extraction, and response generation, substituting human cognitive labor with computational processes that operate faster and handle larger volumes of messages simultaneously.
2Productivity
If users manually process each message, then customization and configurability can be maintained, but efficiency decreases
Solution Approach 1:
The system incorporates feedback mechanisms where users can review, modify, and provide feedback on generated responses. This feedback loop allows users to maintain control and customization over the response generation process while the system learns from user preferences and adjustments, balancing automated efficiency with user oversight and configurability.
Data Source
AI summary
Methods, systems, and devices for analyzing communication messages (e.g., emails) and selecting corresponding actions are described. In some database systems, a user may receive multiple messages at a user device. To efficiently determine responses to these messages, the user device may send the messages to a backend server for analysis. The server may perform natural language processing (NLP) to classify the message with one or more binary classifications and may extract metadata from each message. Based on the classifications and the metadata, the server may determine one or more actions the user device may perform to respond to each message. The server may send instructions to the user device indicating the suggested actions, and the user device may display these actions as options to a user. Additionally, the user device may use the classifications and metadata to automatically generate one or more communication templates in response to the message.