Annotating Collaborative Content for Automated Runbook Generation
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Solution Overview
Problem
Creating runbooks that address complex system problems spanning multiple domains is challenging due to the scarcity of expert time and the difficulty in coordinating multiple experts simultaneously, leading to a bottleneck in generating sufficient runbooks.
Innovation Solution
A system and method for annotating collaborative content in a collaborative effort system, allowing users to tag key elements within the content, which are then used to generate a runbook, facilitating the extraction and aggregation of key content from discussions among subject matter experts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If runbooks are created manually by experts, then the quality and accuracy of runbooks are improved, but the productivity and volume of runbooks generated deteriorate due to expert time scarcity
Solution Approach 1:
The system creates runbooks by copying and structuring information from existing collaborative content (chat logs, emails, documents) rather than requiring experts to manually create each runbook from scratch. This allows automated generation of multiple runbooks while preserving the quality of expert-contributed source material.
Solution Approach 2:
The system enables runbook generation to serve itself by automatically extracting and organizing content from collaborative discussions. The collaborative content inherently contains the runbook information, and the system autonomously processes this content into structured runbooks without requiring continuous expert intervention.
2Manufacturing precision
If multiple experts are coordinated to create runbooks spanning multiple domains, then the comprehensiveness and accuracy of runbooks are improved, but the time and complexity of creation process deteriorate
Solution Approach 1:
The system performs preliminary action by capturing and storing collaborative content during the problem-solving process itself. This content is then later mined and structured into runbooks, eliminating the need for separate coordination sessions to gather expert knowledge after the fact.
Solution Approach 2:
The system acts as an intermediary that automatically processes and structures information from multiple expert contributions. Instead of requiring experts to manually coordinate and integrate their knowledge, the system mediates by extracting relevant information from their collaborative content and organizing it into comprehensive runbooks.
3Loss of information
If experts spend time creating runbooks, then the knowledge capture and reusability are improved, but the time available for solving current problems deteriorates
Solution Approach 1:
The system enables knowledge capture to serve itself by automatically generating runbooks from collaborative content that experts create while solving problems. This eliminates the need for separate knowledge capture sessions, allowing experts to focus on problem-solving while the system autonomously captures and structures the knowledge they generate.
Solution Approach 2:
The system performs preliminary knowledge capture by storing collaborative content during the problem-solving process. This content is then later processed into reusable runbooks, capturing knowledge at the moment it is created rather than requiring separate post-processing sessions that would consume expert time.
Data Source
AI summary
Aspects include methods, systems, and computer programs to tag collaborative content to facilitate mining key content as a runbook. The method includes providing a user interface allowing a user to annotate portions of content in a collaborative effort system, the content comprising one or more log elements and responsive to a user utilizing the user interface and selecting a log element in the content, tagging the selected log element with an annotation. The tagged log elements may be used to generate a runbook.