Client-Side AI Strategy Packages for Decision Service Failover
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Solution Overview
Problem
Existing decision services in production computing environments fail frequently, leading to incomplete strategy execution, system errors, and potential losses due to unavailability and latency, with conventional fallback systems being static and inefficient.
Innovation Solution
Generate client-side executable strategies by parsing and bundling available data pathways from decision services, using AI models to determine derivable data, and storing these strategies on user devices for redundant execution during service failures.
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 availability deteriorates due to frequent failures and timeouts
Solution Approach 1:
The patent implements client-side strategy packages that are pre-generated and stored locally on user devices before any service failure occurs. These packages contain executable strategies and available data that can be immediately invoked when the decision service fails, eliminating the need for real-time server communication during failures and ensuring continuous operation.
Solution Approach 2:
The patent creates simplified copies of the decision service functionality by generating client-side strategy packages that replicate essential decision-making capabilities. These packages include parsed strategies, available data, and executable logic that mirror server-side functionality, allowing local execution when the original service is unavailable.
2Reliability
If conventional fallback systems are implemented, then basic service continuity is improved, but system robustness deteriorates due to static and inefficient fallback mechanisms
Solution Approach 1:
The patent implements dynamic fallback by generating strategy packages tailored to each user's specific context, available data, and required strategies. Unlike static fallback systems, these packages are dynamically created based on real-time assessments of data availability and service failure conditions, allowing the system to adapt to varying failure scenarios and user needs.
Solution Approach 2:
The patent enhances robustness by providing customized strategy packages to different users based on their specific contexts. Each user receives a package tailored to their available data sources, required strategies, and device capabilities, rather than a one-size-fits-all fallback mechanism. This local customization optimizes the fallback effectiveness for each specific scenario.
3Reliability
If client-side executable strategies are generated and stored, then service availability is improved during failures, but device storage and processing requirements increase
Solution Approach 1:
The patent extracts only the essential and necessary components for fallback operation, including parsed strategies, available data, and executable logic, while excluding non-essential elements. This selective extraction minimizes the size of strategy packages and reduces the computational burden on client devices while maintaining adequate fallback functionality.
Solution Approach 2:
The patent implements a balanced approach by providing sufficient fallback capability without over-provisioning. Strategy packages include exactly the data and strategies needed for effective fallback operation, avoiding both insufficient provisioning (which would compromise availability) and excessive provisioning (which would burden device resources).
Data Source
AI summary
There are provided systems and methods for dynamic creation of data specification-driven AI-based executable strategies for high availability of evaluation 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.