AI-Generated Client-Side Strategies for Decision Service Fallback
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Solution Overview
Problem
Existing decision services in production computing environments fail frequently, leading to incomplete strategy execution, system vulnerabilities, and high failure rates, which conventional fallback systems cannot adequately address due to their static nature and high maintenance costs.
Innovation Solution
Generate client-side executable strategies by parsing and analyzing existing decision service strategies, determining viable pathways using available data, and creating data packages that can be executed locally on user devices to ensure redundancy and high availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If decision services are implemented in production computing environment, then real-time data processing capability is improved, but system reliability deteriorates due to frequent failures and timeouts
Solution Approach 1:
The patent applies preliminary action by pre-generating multiple alternative strategy pathways before decision service execution. When a decision service fails, the system can immediately switch to pre-prepared alternative pathways without waiting for failure detection and analysis, thus maintaining real-time processing capability while improving reliability through proactive redundancy preparation
Solution Approach 2:
The patent implements beforehand cushioning by creating a portfolio of alternative strategy pathways that serve as buffers against decision service failures. These alternative pathways are prepared in advance and can be activated when primary decision services fail, cushioning the system against reliability deterioration while maintaining processing continuity
2Reliability
If conventional fallback systems are implemented, then system redundancy is improved, but device complexity and maintenance cost worsen due to static nature and high upkeep requirements
Solution Approach 1:
The patent applies dynamics by making the fallback system adaptive rather than static. The system dynamically generates alternative strategy pathways based on real-time analysis of decision service failures and available data, allowing the redundancy mechanism to evolve and optimize itself, thereby reducing maintenance complexity while maintaining reliability
Solution Approach 2:
The patent implements self-service by enabling the system to automatically generate and optimize alternative strategy pathways without external intervention. The AI/ML models autonomously analyze failures, identify viable alternative pathways, and update the redundancy portfolio, eliminating the need for manual configuration and reducing maintenance overhead
3Adaptability or versatility
If client-side executable strategies are generated dynamically, then system adaptability is improved, but data processing complexity worsens due to parsing and pathway determination requirements
Solution Approach 1:
The patent replaces mechanical parsing and analysis systems with AI/ML-based intelligent systems. Instead of using complex rule-based parsers to analyze decision services and generate alternative pathways, the system employs machine learning models that automatically understand decision service logic and generate appropriate alternative strategies, reducing processing complexity while enhancing adaptability
Data Source
AI summary
A service provider may create data specification-driven AI-based executable strategies, which may be used to ensure high uptime and availability of decision services. A service provider, such as an electronic transaction processor for digital transactions, may utilize different decision services that implement rules and artificial intelligence models for decision-making of data including data in production computing environment. A decision service may normally be used for data processing and decision-making. However, at certain times, the decision service may fail or the services and/or a gateway for such services may be inaccessible. To provide higher availability and better SLA times, a client-side executable strategy for decision service execution may be determined using the pathways for strategy execution and available data from called resources. This strategy may be loaded in parallel to calling the decision service, and when failure occurs, may be used as a fallback to request processing.