Cognitive Platform for Autonomous Data Orchestration
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Solution Overview
Problem
Current cognitive platforms are domain-specific and isolated, failing to leverage intelligence and context across multiple products or domains, limiting their ability to detect broader signals and events, and lack a common framework for holistic decision-making and outcome attribution.
Innovation Solution
A cognitive platform for autonomous data orchestration that includes edge computing devices, data platforms, and a cognitive computing engine, which receives digital events, determines intent through context and state information, identifies target entities, and communicates actionable insights in natural language, enabling contextual application across entities and contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cognitive platforms are designed around specific use cases to offer deep domain expertise, then product functionality and user experience are improved, but the platforms become isolated from other products and cannot detect broader signals spanning multiple domains
Solution Approach 1:
The patent implements a universal event schema that can represent events across multiple domains (retail, finance, healthcare, etc.) using a common structure. This schema includes universal event types like 'Event', 'Alert', 'Notification' that can be instantiated with domain-specific parameters, allowing the platform to handle diverse domain events while maintaining a unified processing framework that enables cross-domain signal detection.
Solution Approach 2:
The patent introduces an intermediary layer consisting of the event schema and pattern matching engine that sits between domain-specific data sources and the cognitive processing components. This intermediary standardizes and contextualizes events from different domains, enabling the platform to detect complex patterns that span multiple domains without requiring domain-specific customization at each level.
2Adaptability or versatility
If multiple niche AI/ML products work together to prescribe outcomes, then comprehensive decision-making is achieved, but lack of a common framework limits the ability to express provenance in decision making
Solution Approach 1:
The patent segments the decision-making process into distinct, traceable components: event detection, pattern matching, rule evaluation, and action generation. Each component processes and transforms information in a structured way, with the event schema serving as a standardized interface between segments. This segmentation enables clear attribution of decisions to specific patterns, rules, and events while maintaining overall system integration.
3Extent of automation
If cognitive platforms process and learn from unstructured data using complex algorithms, then decision-making capability is improved, but the system complexity and computational requirements increase significantly
Solution Approach 1:
The patent replaces complex mechanical processing of unstructured data with a knowledge-based system using event schemas and pattern matching rules. Instead of relying solely on heavy machine learning models to interpret unstructured data, the system uses predefined event templates and contextual rules to structure and understand data, reducing computational complexity while maintaining decision-making capability.
Data Source
AI summary
A system and/or a method for a cognitive platform for autonomous data orchestration, comprising edge computing devices, data platforms and cognitive computing engine. The cognitive computing engine is configured to receive digital events either generated by external computing devices or within the cognitive computing engine. Context and state information is accessed from a central intelligence store to determine the intent of the received digital events. A target entity is identified based on at least one of the determined intent. A data package is composed for the identified at least one target entity, transformed to a natural language text and communicated to the identified at least one target entity, inciting the target entity to take action on the package delivered. The central intelligence store is updated new context and intents for subsequent digital events making it an autonomous learning platform.