Email Content Scanner for Request Metadata and Retrieval
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Solution Overview
Problem
Current computer productivity platforms are inefficient and error-prone in locating relevant content, as users manually search and browse through multiple sources to find information requested or committed, leading to incomplete and inaccurate results.
Innovation Solution
Implementing an intelligent content surfacing technique using a content scanner to detect requests and commitments in emails and insert metadata, and a content surface engine to automatically retrieve and surface relevant information based on user activity, reducing the need for manual searching and ensuring accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually search and browse through multiple sources to locate relevant content, then they can find information requested or committed, but the process is time-consuming and error-prone
Solution Approach 1:
The system performs preliminary actions by automatically scanning emails and inserting metadata (such as due dates, request types, and commitment information) before users need to locate content. This pre-processing enables the content surface engine to quickly retrieve relevant information without manual searching, resolving the contradiction between accuracy and time consumption.
Solution Approach 2:
The patent introduces metadata as an intermediary element that bridges emails and relevant content. By embedding structured metadata in emails and using it as a query interface, the system enables precise content location without manual browsing, improving both accuracy and efficiency.
2Reliability
If users manually track due dates and search repositories for requested information, then they can respond to requests, but errors occur such as missing due dates or incomplete information
Solution Approach 1:
The content surface engine provides feedback by automatically surfacing relevant content and due date information to users based on metadata in their emails. This closed-loop feedback mechanism ensures users receive complete information without manual tracking, improving reliability while reducing process complexity.
Solution Approach 2:
The system performs self-service by automatically detecting requests and commitments in emails, inserting appropriate metadata, and surfacing relevant content without user intervention. This automation eliminates manual tracking errors and reduces the complexity of the process for users.
3Productivity
If the system automatically scans emails and inserts metadata to enable intelligent content surfacing, then content location efficiency improves, but system complexity increases
Solution Approach 1:
The patent segments the content location system into distinct functional modules: an email scanning component that inserts metadata, and a content surface engine that retrieves content based on metadata. This segmentation improves productivity by enabling automated content surfacing while managing complexity through modular design.
4Ease of operation
If the content surface engine automatically surfaces relevant content based on user activity, then manual searching effort is reduced, but the system requires sophisticated detection and retrieval capabilities
Solution Approach 1:
The content surface engine operates autonomously by detecting user activities (such as opening emails) and automatically surfacing relevant content without requiring complex user interactions. This self-service approach improves ease of operation while the underlying complexity is managed through automated processes.
Data Source
AI summary
Techniques of relevant content surfacing in a computer productivity platform are disclosed herein. In one embodiment, a method includes receiving, at an email server of the computer productivity platform, an email having a message body containing content and determining whether the content of the email contains a request to or a commitment by the user. In response to determining that the content of the email contains a request to or a commitment, inserting metadata containing one or more properties of the request or commitment into the email, the metadata. Then, when viewing of the content of the email by the user is detected, the computer productivity platform is queried for additional content using the properties of the request or commitment as keywords. The additional content is then surfaced to the user as being related to the content in the message body of the email.