Augmented Intelligence System Governance via Modular Segmentation
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Solution Overview
Problem
Current approaches to building and deploying artificial intelligence (AI) systems face challenges such as complexity, resource constraints, and the need for repeatable processes to reduce skill requirements and optimize human and computing resources, particularly in processing and utilizing big data and dark data for actionable insights.
Innovation Solution
A method and system for cognitive inference and learning operations using an augmented intelligence system that processes data from multiple sources, providing cognitively processed insights through a hardware processor-based information processing system, with governance and assurance operations to ensure performance, and interacts with cognitive applications for user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data processing approaches are used to handle big data, then data processing can be performed with simple tools, but processing efficiency is insufficient and cannot handle large datasets within tolerable time intervals
Solution Approach 1:
The patent segments the AI system into modular components including data collection modules, data processing modules, model training modules, and deployment modules. Each module can be independently developed, tested, and optimized, allowing parallel processing and improving overall system productivity while managing complexity through clear separation of concerns.
Solution Approach 2:
The patent implements universal data processing frameworks and standardized interfaces that can handle multiple data types and sources through a common architecture. This multi-functional approach allows the same infrastructure to process diverse big data workloads, improving resource utilization and processing efficiency without proportionally increasing system complexity.
2Adaptability or versatility
If AI systems are built with high customization to handle specific business requirements, then functionality is improved, but implementation time and resource requirements increase
Solution Approach 1:
The patent implements pre-built templates, pre-trained models, and standardized data processing pipelines that can be quickly deployed and customized for specific business requirements. This preliminary preparation significantly reduces implementation time while maintaining the ability to adapt to different business needs through configuration rather than extensive development.
Solution Approach 2:
The patent employs dynamic configuration capabilities that allow the AI system to adapt its behavior and parameters based on specific business requirements without requiring structural changes. The system can dynamically adjust data sources, processing parameters, and model selection to match business needs, providing high adaptability with minimal implementation overhead.
3Reliability
If governance and assurance operations are added to ensure AI performance, then system reliability is improved, but process complexity increases
Solution Approach 1:
The patent implements automated feedback loops that continuously monitor AI system performance, data quality, and model behavior. These feedback mechanisms automatically adjust system parameters, trigger retraining when performance degrades, and flag anomalies for review, improving reliability through continuous self-correction without requiring complex manual governance processes.
Solution Approach 2:
The patent incorporates self-service governance capabilities where the AI system automatically performs validation, monitoring, and basic compliance checks without requiring extensive external governance infrastructure. The system self-manages many governance tasks through built-in validation rules and automated auditing, improving reliability while minimizing the complexity of external governance processes.
Data Source
AI summary
A system, method, and computer-readable medium are disclosed for cognitive information processing. The cognitive information processing includes receiving data from a plurality of data sources; processing the data from the plurality of data sources to provide cognitively processed insights via an augmented intelligence system, the augmented intelligence system executing on a hardware processor of an information processing system, the augmented intelligence system and the information processing system providing a cognitive computing function; performing an augmented intelligence governance and assurance operation, the augmented intelligence governance and assurance operation ensuring augmented intelligence performance of the cognitive computing function; and, providing the cognitively processed insights to a destination, the destination comprising a cognitive application, the cognitive application enabling a user to interact with the cognitive insights.