Cloud Case-Based Reasoning Service for Enterprise Problem Solving
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Enterprise solutions face challenges such as the need for large data sets for machine-learning algorithms, dedicated training environments, and user experience issues like lack of transparency in data results, leading to customized and scalable designs that are disadvantageous for cloud-based solutions.
Innovation Solution
A cloud-based case-based reasoning system that receives problem descriptions, generates metadata, stores and matches solutions, and provides new solution descriptions through an API, enabling automated analysis and incremental learning without requiring extensive data, while allowing for transparency and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine-learning algorithms are used to provide intelligent enterprise solutions, then the quality and intelligence of solutions improve, but the system requires large amounts of data and dedicated training environments, increasing complexity and resource requirements
Solution Approach 1:
The patent introduces a case management system as an intermediary layer between data collection and machine learning algorithms. This system curates, manages, and prepares relevant case data, serving as a mediator that reduces the raw data volume required by ML algorithms while maintaining solution quality. The case management functionality acts as a buffer that pre-processes and structures information before it reaches the computational models.
Solution Approach 2:
The patent segments the enterprise solution into distinct functional modules: case management, collaboration tools, and machine learning components. This segmentation allows each component to operate independently with optimized resource requirements. The case management system handles data curation separately from the ML algorithms, reducing the burden on training environments and data infrastructure.
2Manufacturing precision
If customized enterprise solutions are designed for specific use cases, then the precision and relevance of solutions improve, but the scalability and adaptability to other contexts deteriorate
Solution Approach 1:
The patent implements a universal case management platform that can handle multiple types of enterprise problems across different industries and contexts. The system uses standardized case structures and metadata schemas that allow the same platform to serve diverse use cases, from legal document review to financial fraud detection, without requiring customization for each specific application.
Solution Approach 2:
The patent employs configurable parameters and metadata fields that can be adjusted to adapt the system to different use cases. By changing parameters such as case attributes, collaboration rules, and ML model selection, the same core system can be tuned for precision in specific applications while maintaining the ability to scale to other contexts through parameter modification rather than structural redesign.
3Adaptability or versatility
If cloud-based architecture is used to improve scalability, then the adaptability and accessibility of solutions improve, but the handling of sensitive enterprise data and maintenance of security standards become more challenging
Solution Approach 1:
The patent segments data handling functions into cloud-based accessibility services and security-critical processing. The case management system separates data storage and collaboration features (handled in the cloud) from sensitive data processing and security enforcement (handled through controlled interfaces and on-premises components). This segmentation allows cloud benefits while mitigating security risks.
Solution Approach 2:
The patent introduces security protocols and authentication mechanisms as intermediary layers between cloud services and enterprise data. These intermediaries enforce access controls, encrypt data transmissions, and manage permissions, allowing the system to leverage cloud scalability while maintaining security standards through mediating security infrastructure.
Data Source
AI summary
The disclosure generally describes methods, software, and systems for providing solution descriptions. A problem description of a problem is received, from a client, at a cloud-based reasoning service. A solution description for a solution to the problem is received. Case metadata for a case defining the problem and solution are generated by the cloud-based reasoning service. The case metadata, including the problem description and solution description, are stored by the cloud-based reasoning service in a cases repository associating solutions with problems. A new problem is received at the cloud-based reasoning service. An automated analysis of the new problem is performed, and a comparison is made of the new problem with existing solutions in the cases repository to identify solutions matching the new problem. A new solution description is provided that is based on a match between the new problem description and the problem description and using the problem solution.