Adaptive Software Knowledge Model for Deployment Reliability
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Solution Overview
Problem
In complex enterprise computing environments, software deployment faces challenges due to limited resource validation for infinite environmental permutations, leading to installation deadlocks and difficulties in creating a knowledge base for customized operating systems and proprietary applications, with existing deployment rules being too restrictive or generic, and requiring manual updates for knowledge model expansion.
Innovation Solution
A Knowledge Generation Machine (KGM) that automatically collects and processes information from various sources, detects new information, and extends the knowledge model without manual intervention, enabling scalable generation of deployment rules and dependency models across different channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deployment rules are made specific to validate environmental permutations, then installation reliability improves, but the complexity of creating and maintaining the knowledge base increases
Solution Approach 1:
The patent segments the knowledge base into modular dependency rules that can be independently validated and maintained. Each software component has its own dependency declarations that are processed separately, allowing specific environmental validations without creating a monolithic complex rule set. This modular segmentation enables reliable deployment rules while managing knowledge base complexity through structured organization.
Solution Approach 2:
The patent implements preliminary action by requiring software components to declare their dependencies upfront during the packaging phase. This preliminary declaration of dependencies allows the deployment system to validate environmental permutations before installation occurs, ensuring reliability without requiring complex runtime decision-making. The knowledge base is populated with these pre-validated dependency rules, reducing the complexity of ad-hoc deployment rule creation.
2Adaptability or versatility
If deployment rules are made generic to cover more scenarios, then adaptability improves, but manufacturing precision of deployment validation decreases
Solution Approach 1:
The patent implements dynamics by making the knowledge base adaptive rather than static. The system dynamically generates deployment rules based on the specific software component being installed and its declared dependencies. This dynamic rule generation allows the system to adapt to different software packages and environmental configurations while maintaining validation precision through component-specific dependency declarations. The knowledge base evolves as new software components and their dependencies are added.
Solution Approach 2:
The patent applies parameter changes by allowing deployment rules to be customized based on the specific parameters of each software component. Instead of using fixed generic rules, the system adjusts deployment validation parameters according to the component's declared dependencies, version requirements, and environmental constraints. This parameter-based customization enables precise validation for each component while maintaining overall system adaptability.
3Manufacturing precision
If manual updates are used to expand the knowledge model, then manufacturing precision of knowledge accuracy improves, but productivity of knowledge base expansion decreases
Solution Approach 1:
The patent implements self-service by enabling the knowledge base to automatically expand through dependency declarations included with software components. When new software is packaged, its dependency information is automatically extracted and added to the knowledge base without requiring manual intervention. This self-service mechanism maintains knowledge accuracy through structured dependency data while dramatically increasing the productivity of knowledge base expansion compared to manual curation.
Solution Approach 2:
The patent applies feedback by using dependency declaration information from software components to automatically update and validate the knowledge base. The system processes dependency declarations as feedback input, automatically generating and validating deployment rules. This feedback loop maintains knowledge accuracy through systematic processing of component metadata while enabling rapid knowledge base expansion as new software components are added to the ecosystem.
4Reliability
If extensive validation of environmental permutations is performed, then installation reliability improves, but loss of time in deployment increases
Solution Approach 1:
The patent applies preliminary action by performing validation of environmental permutations during the software packaging phase rather than at deployment time. Dependency declarations are created and validated upfront, and the knowledge base is pre-populated with deployment rules. This preliminary validation ensures installation reliability while reducing deployment time, as the actual installation process simply needs to match the pre-validated environment against the stored rules without performing extensive real-time validation.
Data Source
AI summary
A knowledge generation machine (KGM) that collects information of varying types from a plurality of different sources is provided. Because information about a software component may come from third parties or external information, the KGM is configured to find and store the new information from these sources. The KGM detects this new information quickly and automatically, and extends a knowledge model using this new information. The KGM generates a knowledge model that expands and adapts as new information is acquired, without the need for manual intervention.