Declarative Client Caching for Resource Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current resource management systems face challenges in efficiently processing large data and managing multiple resources, leading to difficulties in tracking progress, establishing measurable objectives, and ensuring adaptability and flexibility, as they often fail to integrate resource provisioning with skilled professionals and are custom-made for specific end-users.
Innovation Solution
The resource management system employs intelligent caching and proxies to streamline processes, using a declarative client for data fetching and caching, a serverless compute engine for abstraction, and a GraphQL-like query builder to manage data efficiently, while providing secure and localized content delivery through immutable images and a full-stack payment platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If separate front-facing software is used to provision access to materials and services, then access control is achieved, but system complexity increases and holistic resource management is lost
Solution Approach 1:
The patent combines separate access control systems, resource provisioning systems, and professional management systems into a unified resource management platform. This integration eliminates the need for multiple separate front-facing software systems while maintaining comprehensive access control and holistic resource management capabilities.
Solution Approach 2:
The resource management system is designed as a universal platform that can manage multiple types of resources (materials, services, professionals) through a single interface. This multi-functional approach replaces multiple specialized software systems with one system that handles diverse resource management tasks.
2Ease of manufacture
If custom-made systems are personalized to different end-users, then user-specific requirements are met, but adaptability and flexibility decrease
Solution Approach 1:
The system employs dynamic configuration capabilities that allow it to adapt to different user requirements without requiring custom development. The platform can dynamically adjust its functionality, interface, and resource allocation based on user needs while maintaining a standardized core architecture that ensures flexibility and scalability.
3Loss of information
If large data associated with multiple resources is processed, then comprehensive resource tracking is achieved, but processing efficiency decreases
Solution Approach 1:
The patent extracts and separates critical resource tracking data from the full dataset, processing only the essential information needed for monitoring and management. This selective data extraction approach maintains comprehensive tracking capabilities while significantly reducing processing overhead and improving system performance.
4Measurement precision
If measurable objectives and progress tracking are implemented, then monitoring capability is improved, but system complexity increases
Solution Approach 1:
The resource management system automatically generates progress metrics, performance reports, and monitoring data without requiring complex manual configuration. The system self-services by automatically tracking resource utilization, generating measurable objectives, and providing progress reports, thereby improving monitoring precision while minimizing the complexity burden on users.
Data Source
AI summary
A system automatically manages remote and local data through a declarative client that retrieves, tracks, and caches data in response to a transmission from an interface. The declarative client sits on an immutable image served by a secure private cloud platform. A serverless compute engine receives the immutable image and a plurality of tasks that process the immutable image in a container. An application programming interface in communication with the declarative client extracts data via queries from a database. The declarative client includes a normalized in-memory cache that breaks up results of the queries into individual objects that are each associated with a unique identifier and a unique name. The extracted data is deconstructed downloaded content in which original computer assigned links between data elements are intercepted and mapped to redirected computer-generated local links that locate the downloaded content in a local database.