Email Client Content Pre-loading via NLP Action Importance
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Solution Overview
Problem
Current electronic messaging systems require users to leave their email client application and expend additional computational resources to retrieve content referenced by URLs, leading to unnecessary user time and resource expenditure when dealing with multiple messages over time.
Innovation Solution
A system that determines metadata for electronic messages, detects URLs, performs natural language processing to determine content type and action importance, and pre-fetches relevant content based on these factors, providing it alongside the message to the user's client device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content is pre-fetched from remote data processing systems, then user time and productivity are improved, but use of energy and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-fetching content referenced in electronic messages before the user actually requests it. The server proactively retrieves content from remote data processing systems and stores it in cache memory, so that when the user opens an email, the content is already available locally, eliminating the need for users to wait for content to load and reducing their device's computational burden.
2Speed
If content is pre-loaded into cache memory, then speed of content delivery is improved, but volume of data stored increases
Solution Approach 1:
The system applies local quality by selectively caching only certain types of content based on their characteristics and likelihood of being accessed. Different content referenced in emails is treated differently - some content types are cached while others are not, based on their properties. This selective approach optimizes cache utilization by storing only the most beneficial content locally, balancing storage volume with delivery speed improvements.
3Measurement precision
If natural language processing is performed on electronic messages, then accuracy of content selection is improved, but device complexity and processing time increase
Solution Approach 1:
The system introduces an intermediary by performing natural language processing on the text of electronic messages to understand the context and determine what content should be pre-fetched. The NLP analysis examines the message body to identify relevant content that the user is likely to want, enabling intelligent selection of what to cache without requiring complex user input or manual configuration.
Data Source
AI summary
Cognitive pre-loading of referenced content in electronic mail (e-mail) messages includes determining, using computer hardware, metadata for an electronic message directed to a user, detecting, using the computer hardware, a universal resource locator (URL) within a body portion of the electronic message, determining, using the computer hardware, a content type for the URL, and performing, using the computer hardware, natural language processing on the electronic message to determine an action importance corresponding to the URL. At least a portion of content specified by the URL can be pre-fetched from a data processing system based on the metadata, the content type of the URL, and the action importance. The electronic message and at least a portion of the content can be provided to a client device of the user.