Living Documents for Cloud Knowledge Management
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Solution Overview
Problem
Companies face inefficiencies and costs due to the time-consuming and disorganized process of extracting and utilizing information from various sources, leading to rework on similar projects, especially in complex and rapidly evolving fields like cloud services, where consultants lack both business expertise and technical acumen to navigate changing technology landscapes.
Innovation Solution
A computer-implemented method using trained machine learning models to generate and update living documents by processing user input and codified knowledge management information, including engine data from intelligent cloud, domain expertise, and AI-driven experimentation engines, to provide automated knowledge management and decision-making support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual information extraction and organization methods are used, then information can be collected from various sources, but the process becomes time-consuming and disorganized
Solution Approach 1:
The patent replaces manual mechanical information extraction processes with automated machine learning models and natural language processing systems. These systems automatically ingest, extract, and organize information from diverse sources including documents, videos, and websites, eliminating the need for manual collection and reducing both time loss and information loss.
Solution Approach 2:
The knowledge management system performs self-updating through automated machine learning models that continuously learn from new data sources and automatically organize information without human intervention. The system self-maintains the knowledge base by automatically processing incoming information and updating the living documents.
2Adaptability or versatility
If consultants rely on leading documents for cloud services, then solutions can be provided, but the documents fall out of date quickly in fast-evolving technology spaces
Solution Approach 1:
The patent transforms static documents into dynamic 'living documents' that continuously update themselves. Machine learning models automatically ingest new information from technology sources, process it, and update the knowledge base in real-time, ensuring the documents remain current with fast-evolving technology without requiring manual revision.
Solution Approach 2:
The system implements continuous information processing and document updating through automated machine learning pipelines. The knowledge management system operates continuously to ingest new technology information, process it through ML models, and update living documents, ensuring constant currency and adaptability to changing technology landscapes.
3Quantity of substance
If internal and external information is collected from complex formats, then comprehensive knowledge can be gathered, but the collection and organization process becomes time-consuming
Solution Approach 1:
The patent implements a universal information processing system that handles multiple data formats (documents, videos, websites, structured and unstructured data) through a single automated pipeline. The machine learning models are designed to process diverse information types simultaneously, gathering comprehensive knowledge from all sources while maintaining efficient organization without manual intervention.
4Reliability
If more knowledge management information is maintained to support consulting solutions, then solution quality improves, but the complexity of managing and updating this information increases
Solution Approach 1:
The patent replaces complex manual knowledge management processes with automated machine learning systems. The ML models automatically ingest, process, validate, and organize large volumes of information from multiple sources, maintaining high solution accuracy through systematic processing while eliminating the operational complexity of manual knowledge management.
Data Source
AI summary
A computer-implemented method includes receiving user input including codified knowledge management information and/or engine data; and processing the input using one or more trained machine learning models to generate one or more living documents. A computing system includes one or more processors; and a memory having stored thereon instructions that, when executed, cause the computing system to receive user input including codified knowledge management information and/or engine data; and process the user input using one or more trained machine learning models to generate one or more living documents. A non-transitory computer-readable storage medium includes executable instructions that, when executed by a processor, cause a computer to receive user input including codified knowledge management information and/or engine data; and process the user input using one or more trained machine learning models to generate one or more living documents.