Modular AI Configuration Pipeline for Real-Time Adaptability
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Solution Overview
Problem
Existing AI technologies face challenges in modular configuration, leading to inconsistencies and repeatability issues due to different programming approaches and modules.
Innovation Solution
The development of a modular AI configuration system that allows for the selection and integration of modules from various categories, forming a pipeline that can be modified in real-time to learn trends and predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional programming approaches are used for AI, then AI functionality can be achieved, but programming difficulty and complexity increase
Solution Approach 1:
The patent segments the AI programming process into modular components including data loading modules, preprocessing modules, model training modules, and evaluation modules. Each module can be independently selected, configured, and assembled, transforming the complex monolithic programming task into manageable discrete units that are easier to program and maintain.
Solution Approach 2:
The patent creates universal module templates that can serve multiple functions across different AI applications. For example, a preprocessing module can be configured for different data types and models, and a training module can work with various model architectures, reducing the need for custom programming in each scenario.
2Adaptability or versatility
If custom AI modules are developed for specific applications, then application-specific functionality is achieved, but consistency and repeatability decrease
Solution Approach 1:
The patent implements dynamic module configuration where the AI pipeline can be adjusted and reconfigured based on specific application requirements while maintaining a consistent core framework. Modules can be dynamically selected, added, or removed from the pipeline without compromising the overall system structure or repeatability.
Solution Approach 2:
The patent enables parameter-based customization of modules rather than structural changes. By modifying parameters and configuration settings within standardized modules, application-specific functionality is achieved while maintaining consistent module interfaces and execution flows, ensuring repeatability across different applications.
3Adaptability or versatility
If AI pipelines are made modular and configurable, then flexibility and adaptability improve, but system complexity increases
Solution Approach 1:
The patent segments the AI system into standardized modular components with well-defined interfaces and responsibilities. This segmentation allows flexible assembly of different module combinations while managing complexity through clear module boundaries and standardized interaction protocols.
Solution Approach 2:
The patent introduces intermediary components such as module registries, configuration managers, and pipeline orchestrators that mediate between the user's high-level specifications and the underlying complex module interactions. These intermediaries abstract away the complexity while enabling flexible module composition and configuration.
4Adaptability or versatility
If real-time modification of AI modules is enabled, then adaptability improves, but computational overhead increases
Solution Approach 1:
The patent implements dynamic module substitution capabilities that allow real-time modification of AI pipeline components. When modules are modified or replaced, the system efficiently manages resource allocation and model loading to minimize computational overhead during transitions.
Solution Approach 2:
The patent employs preliminary actions such as pre-compiling module configurations, pre-loading models into memory, and caching intermediate results before real-time modifications are needed. This preparation reduces the computational burden during actual real-time operation and module switching.
Data Source
AI summary
Technologies and implementations for a modular machine learning system including an artificial intelligence configuration module (AICM). The AICM may be configured to provide a modular process via a user interface to facilitate machine learning.