CRM Email Triage via Machine Learning Analysis
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Solution Overview
Problem
Users working on multiple tasks in task database systems often face challenges in timely responding to emails due to the high volume of messages, making it time-consuming and inefficient to manually identify and reply to emails that require attention.
Innovation Solution
A cloud server-based system that retrieves task metadata, identifies source and target email domains, and uses machine learning models to analyze email content to automatically determine and notify users of emails that need a response, providing a graphical user interface for direct reply actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a user manually goes through each received email message in an inbox, then the user can identify emails that need a reply, but the process is time-consuming and inefficient
Solution Approach 1:
The patent replaces the manual mechanical process of reviewing emails with an automated machine learning-based system. The ML model analyzes email content, sender relationships, and task data to automatically identify and prioritize emails requiring responses, eliminating the need for users to manually sort through their inboxes while maintaining high identification accuracy.
Solution Approach 2:
The system enables self-service by automatically performing the email triage function that would otherwise require user intervention. The ML model autonomously evaluates incoming emails, determines which ones need replies, and presents them to users in a prioritized manner, allowing users to focus only on actionable emails without manual sorting.
2Adaptability or versatility
If a user works on multiple tasks simultaneously, then the user can handle diverse responsibilities, but the user may not be able to follow up and reply to email messages in a timely manner
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring email patterns, task progress, and user behavior. The ML model learns from this feedback to improve its predictions about which emails require responses, adapting to each user's specific workflow and communication patterns to maintain high response speed across multiple tasks.
Solution Approach 2:
The patent introduces an intermediary intelligent system that acts as a mediator between multiple tasks and email communications. This ML-based intermediary prioritizes and organizes emails according to task urgency and importance, allowing users to efficiently switch between tasks while ensuring timely email responses through the intermediary's continuous management.
3Extent of automation
If a cloud server uses machine learning models to analyze email content, then the system can automatically determine emails that need a reply, but the system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex email analysis system into distinct modular components: an ML model training module, an email analysis module, a task management module, and a user interface module. Each component handles a specific function independently, making the overall complex system manageable, maintainable, and scalable while achieving high automation.
Data Source
AI summary
The disclosure describes various embodiments for determining emails that each need a response based on data from a customer relationship (CRM) system. In one embodiment, a method of determining such emails includes the operations of retrieving open tasks assigned to a user from a task database; determining one or more source email domains for one or more source contacts, and one or more target email domains for one or more target contacts; and determining one or more threads emails exchanged between the source contacts and the target contacts based on the source email domains and the target email domains. The method further includes the operations of creating an email list from the threads of emails, including a latest email from a group that was sent by a target contact; and generating a subset of the list of emails by analyzing contents of each of the list of emails using a machine learning model.