Contextual Email Recommendation System
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Solution Overview
Problem
Email systems fail to automatically provide users with contextually relevant messages, requiring explicit user input and search queries, which is cumbersome on mobile devices with limited input capabilities.
Innovation Solution
A processor-executable method that identifies and displays contextually relevant messages by extracting keywords from a received email message, clustering similar messages, and ranking them for display with minimal user input, using a user interface element that facilitates retrieval of relevant messages with gestures like taps or clicks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually search for emails using search boxes and keywords, then they can find specific emails, but the operation becomes cumbersome especially on mobile devices with limited input capabilities
Solution Approach 1:
The system automatically performs email search and recommendation without requiring user input. The processor-executable instructions autonomously analyze the currently displayed email, extract relevant keywords, and retrieve similar emails, making the search service self-acting rather than user-initiated.
Solution Approach 2:
The system pre-processes emails by extracting keywords and computing similarity metrics in advance. When an email is displayed, the system has already prepared the data structures and similarity calculations needed for rapid retrieval, eliminating the need for manual search operations at the moment of use.
2Adaptability or versatility
If the system displays contextually relevant messages automatically, then user experience is enriched, but the device complexity increases due to keyword extraction and message clustering algorithms
Solution Approach 1:
The system extracts only the essential keywords from emails rather than analyzing the complete message content. This selective extraction of salient features reduces the complexity of subsequent similarity computations while maintaining the ability to identify contextually relevant emails.
Solution Approach 2:
The system transforms email content into simplified parameter representations (keywords and similarity scores) that are easier to process. By changing the representation from full text to extracted parameters, the system reduces computational complexity while preserving the ability to perform contextual matching.
Data Source
AI summary
An email system that enables users to request for and receive recommended messages which are relevant and contextually related to a received message via activation of a user interface element is disclosed. When a message is received, it is analyzed and keywords relevant to the received message along with named entities comprised in the received message are retrieved and stored. Upon activation of a user interface element, the relevant keywords are employed to retrieve other similar messages which are grouped into clusters. The clusters are further ranked based on their relevance to the received message and the top ranked messages from the most relevant cluster are selected for display to the user as the messages that are contextually relevant to the received message. The email system also facilitates execution of batch operations on the contextually relevant messages.