Autonomous Software Component Deployment via Knowledge Matching

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Solution Overview

Problem

Current systems lack the ability to comprehensively and effectively identify and deploy intelligent autonomous business solutions that can evolve intelligently and autonomously to handle new tasks and utilize the latest technologies without human intervention, relying heavily on tacit and explicit knowledge from domain experts.

Innovation Solution

The Archemy system employs a digital knowledge management framework (DKMF) and ecosystem (DKME) that uses taxonomies and machine learning to match business problems with reusable software components, allowing for the creation and management of intelligent autonomous business solutions across various domains, enabling semi-automated deployment and adaptation of software components within a network of compute nodes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional expert knowledge-based approaches are used to identify and deploy business solutions, then solution accuracy and reliability are improved, but system complexity and time consumption increase

Engineering Contradiction:

Improvesolution accuracyVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system enables autonomous self-service through automated component identification, selection, and deployment. The software automatically queries repositories, evaluates candidates using multiple criteria, and deploys selected components without requiring expert manual intervention, thereby maintaining solution reliability while reducing system complexity and time consumption.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual expert knowledge-based processes with automated machine learning algorithms and software agents. These computational systems automatically perform tasks previously requiring human experts, such as identifying suitable software components and making deployment decisions, thus reducing both system complexity and time consumption while maintaining or improving solution accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If manual expert intervention is used for software component selection and deployment, then solution quality is improved, but productivity and response time deteriorate

Engineering Contradiction:

Improvesolution qualityVSAvoiddeployment speed

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system implements autonomous self-service capabilities where software agents automatically query repositories, evaluate candidate components against multiple criteria including quality metrics, and deploy selected components. This automation maintains solution quality through systematic evaluation while dramatically improving deployment speed and productivity by eliminating manual expert intervention bottlenecks.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary actions by pre-querying repositories for candidate components and pre-evaluating them against selection criteria before actual deployment is needed. This advance preparation enables rapid deployment decisions while maintaining quality through thorough preliminary assessment, thus resolving the contradiction between solution quality and deployment speed.

Inventive Principle:

Principle #10Preliminary action

3Reliability

If comprehensive vetting and selection processes are applied to software components, then system reliability is improved, but time consumption and operational complexity increase

Engineering Contradiction:

Improvesystem reliabilityVSAvoidtime consumption

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs comprehensive vetting and selection processes in advance by automatically querying repositories and evaluating candidate components against multiple quality criteria before deployment is needed. This preliminary action ensures high system reliability through thorough assessment while reducing time consumption during actual deployment operations, as the selection work has already been completed.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces time-consuming manual vetting processes with automated machine learning algorithms and software agents that can evaluate multiple candidate components against comprehensive quality criteria in parallel. This substitution maintains high system reliability through thorough assessment while dramatically reducing time consumption by eliminating sequential manual review processes.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11836473B2Active adaptation of networked compute devices using vetted reusable software components

Publication Date: 2023.12.05 ARCHEMY INC

AI summary

A method includes receiving a text description of a system capability request, and converting the text description into a normalized description of the system capability request. A repository is then queried, based on the normalized description and using a search algorithm, to identify multiple candidate application software units (ASUs). The candidate ASUs are displayed to a user for selection. The user-selected ASU is then deployed, either locally or to at least one remote compute device, in response to receiving the user selection. Deployment can include the user-selected candidate ASU being integrated into a local or remote software package, thus defining a modified software package that is configured to provide the system capability.