Autonomous Decision Lifecycle Orchestration with Tokenized Evidence
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Solution Overview
Problem
Existing decision-making systems face challenges such as latency in processing data streams, insufficient security measures, and computational inefficiencies, hindering real-time responsiveness and secure decision orchestration across distributed infrastructures, especially with the increasing adoption of AI technologies.
Innovation Solution
A computer-controlled decision lifecycle management system that categorizes decisions based on manual, AI, or collaborative inputs, aggregates evidence using blockchain tokenization, processes data with AI algorithms, and executes decisions through a hybrid hardware-software architecture, ensuring traceability and compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional software-based decision-making platforms are used, then ease of operation is maintained, but processing speed and real-time responsiveness deteriorate due to latency in handling voluminous data streams
Solution Approach 1:
The system segments decision-making into distinct categories (manual, autonomous AI, collaborative) with dedicated processing pathways. Each category has optimized handling procedures that reduce processing latency while maintaining appropriate complexity only where needed, rather than using a single complex software pathway for all decisions.
Solution Approach 2:
The patent introduces an intermediary architecture that bridges software-based decision platforms with hardware components. This intermediary layer enables direct hardware-software integration, allowing critical decisions to bypass traditional software processing bottlenecks and achieve real-time responsiveness without completely replacing the software platform.
2Reliability
If existing software-based decision platforms are used, then ease of operation is maintained, but security measures are insufficient to protect sensitive decision workflows
Solution Approach 1:
The patent introduces hardware intermediaries (secure enclaves, trusted execution environments) that act as security layers between the software decision platform and external systems. These hardware intermediaries provide cryptographic protection and secure key management without requiring complete redesign of the software architecture, thus enhancing security while limiting complexity increases to specific security-critical components.
3Productivity
If traditional decision-making processes are replaced with AI automation, then productivity increases, but traceability and auditability deteriorate
Solution Approach 1:
The system implements comprehensive feedback mechanisms that automatically record and log all AI-driven decision processes, inputs, and outcomes. This feedback loop maintains detailed audit trails that enable full traceability of autonomous decisions, allowing organizations to track productivity improvements while preserving the ability to audit and review AI decision-making processes.
Solution Approach 2:
The patent creates digital copies and representations of decision processes, evidence, and outcomes that are stored and preserved separately from the actual decision-execution pathway. These copies enable traceability and auditing without interfering with the speed and efficiency of the actual decision-making process, as the copying occurs in parallel rather than sequentially.
4Adaptability or versatility
If distributed infrastructures are adopted for decision orchestration, then adaptability increases, but computational efficiency deteriorates due to coordination overhead
Solution Approach 1:
The patent implements local quality by allowing different distributed nodes to specialize in specific decision categories or functions. Each node optimizes its computational resources and processing capabilities for its specific function, reducing the need for complex inter-node coordination. This specialization maintains adaptability across the distributed system while improving overall computational efficiency by minimizing redundant processing and communication overhead.
Data Source
AI summary
A computer-controlled decision lifecycle management system and method are provided for orchestrating a decision lifecycle. The system is to classify a decision type as one of a decision made by a manual intervention, autonomously by an artificial intelligence (AI) system, or collaboratively by both the AI system and the manual intervention. The system is to trigger an initiation of the decision based on a triggering event and generate a decision token representing the decision. The system collects evidence in the form of computer-executable data from a plurality of data sources and associates the evidence with a decision token to enable traceability. An AI-based analysis module of the system processes the evidence by executing one or more AI algorithms and generate a computer executable decision recommendation based on processed evidence.