Machine Learning Mailbox Presentation Reconfiguration
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Solution Overview
Problem
Organizations face challenges in efficiently managing and protecting growing volumes of data across multiple sources, with existing solutions often lacking interoperability and scalability, leading to difficulties in data protection and storage management.
Innovation Solution
An information management system utilizing machine learning processes to analyze and reconfigure the presentation of data objects, such as email messages, by identifying connections and prioritizing them, and grouping them into clusters for optimized display and storage, while also incorporating scalable and unified data storage management solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data storage and management systems are used, then data can be stored, but data accessibility and retrieval efficiency deteriorate as data volume grows
Solution Approach 1:
The patent segments mailboxes into multiple mail folders based on machine learning analysis of email connections and user behavior patterns. This segmentation organizes large volumes of email data into manageable, contextually relevant groups, improving retrieval efficiency without requiring users to manually navigate through entire mailbox contents.
Solution Approach 2:
The patent replaces manual email organization mechanisms with machine learning processes that automatically analyze email connections, user behavior, and content relationships. This substitution enables the system to dynamically organize and present email data based on learned patterns rather than rigid folder structures or manual user actions.
2Ease of operation
If machine learning processes are applied to analyze and organize data, then data accessibility improves, but system complexity increases
Solution Approach 1:
The machine learning system performs self-service by automatically analyzing email connections, learning user behavior patterns, and dynamically organizing mail folders without requiring user intervention. The system serves itself by continuously improving its organizational structure based on learned insights, reducing the operational burden on users while managing complexity internally.
Solution Approach 2:
The patent changes organizational parameters dynamically based on machine learning analysis. Instead of fixed folder structures, the system adjusts mail folder configurations, grouping criteria, and presentation formats based on learned patterns from email connections and user behavior, enabling adaptive organization that simplifies user interaction.
3Productivity
If data is organized into clusters based on connections, then data presentation efficiency improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-analyzing email connections and user behavior patterns to establish baseline organizational structures. Machine learning models are trained in advance on historical data, enabling the system to quickly present organized mail folders without performing intensive analysis at the moment of user interaction, thus reducing perceived processing time.
Data Source
AI summary
Systems and methods that enable an information management system to utilize machine learning processes to modify and/or reconfigure presentation of mailbox contents, such as mail objects (e.g., email messages), are described. The systems and methods may provide data associated with mail objects to a machine learning system, which runs various machine learning processes using the mail object data. The machine learning processes may discover and/or identify connections between mail objects, such as connections between objects based on two or more objects sharing an associated product or service, an object being associated with a trending topic or issue, an object being associated and/or identified as having a relative high (or, low) priority, and so on.