CMDB to Knowledge Base Translation for Natural Language Automation
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Solution Overview
Problem
Legacy configuration management databases (CMDBs) pose compatibility and operability issues with newer systems due to outdated terminology and processes, hindering automation and natural language-based reasoning.
Innovation Solution
A computer system transforms a CMDB into a knowledge base that dynamically discovers dependencies and builds context, enabling natural language-based commands to be translated into valid APIs and parameters, thus facilitating automation and compatibility with both legacy and newer systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If a legacy configuration management database is used, then historical data and existing configurations are preserved, but compatibility and operability with newer systems deteriorate due to outdated terminology and processes
Solution Approach 1:
The patent introduces a translation layer that acts as an intermediary between the legacy CMDB and newer systems. This translation layer converts outdated terminology and data structures into modern equivalents, enabling compatibility without altering the original legacy data storage. The intermediary layer handles the semantic gap between old and new systems, allowing both to coexist and communicate effectively.
Solution Approach 2:
The system dynamically changes parameters by transforming legacy CMDB data into a standardized format that newer systems can understand. This involves mapping legacy fields to modern equivalents, converting deprecated data structures into current standards, and adjusting terminology to match contemporary system requirements while preserving the original data integrity.
2Ease of operation
If manual processing of service requests is used, then flexibility in handling complex requests is maintained, but productivity and automation capability deteriorate
Solution Approach 1:
The system implements self-service automation where the translated CMDB data enables automatic processing of service requests. The system can autonomously interpret service requests, map them to appropriate configurations, and execute changes without manual intervention. This self-service capability maintains flexibility through intelligent routing while dramatically improving productivity by eliminating repetitive manual tasks.
Solution Approach 2:
The patent performs preliminary action by pre-translating and structuring CMDB data into a format ready for automated processing. Service requests are pre-mapped to configuration items and parameters, so when a request arrives, the system can immediately execute the pre-prepared automation workflow without requiring manual analysis or data preparation steps.
3Device complexity
If legacy CMDB systems are used, then existing infrastructure is maintained, but extent of automation and natural language-based reasoning deteriorate
Solution Approach 1:
The translation layer serves as an intermediary that bridges legacy CMDB systems with modern natural language processing capabilities. It translates legacy data structures into a format that NLP models can understand, enabling the system to interpret natural language service requests and map them to legacy configuration items without requiring replacement of the existing infrastructure.
4Extent of automation
If CMDB data is transformed to a knowledge base, then natural language-based reasoning and automation are enabled, but device complexity increases
Solution Approach 1:
The system segments the transformation process into distinct modular components: data extraction from CMDB, translation to knowledge base format, dependency relationship mapping, and API parameter generation. Each segment handles a specific aspect of the transformation, making the overall complex process manageable, maintainable, and scalable while enabling natural language automation capabilities.
Data Source
AI summary
A technique relates to natural language automation to implement service requests. An intent of a service request is determined by accessing a knowledge base, the knowledge base being configured for dynamic discovery of dependencies related to configuration items, the configuration item being among the configuration items, the configuration items being associated with concepts. An intent application programming interface (API) database comprising a specification is accessed, the specification describing parameters of APIs and associations that the APIs have with the concepts of the knowledge base. Associated parameters of an API associated with the intent of the service request are determined based on the intent API database. The API is caused to be executed to accomplish the service request.