Software Solution Relationship Maps for Client Priority Matching
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Solution Overview
Problem
Large-scale enterprises face challenges in understanding clients' business priorities and identifying potential relationships due to their complex nature and decentralized content management, making it difficult to develop effective solution packages that cater to diverse client needs.
Innovation Solution
The method employs Natural Language Processing, Natural Language Understanding, text embedding algorithms, and graph theory to compare software product solutions and business priorities, generating relationship maps and recommending solution packages that align with client aspirations through keyword and key phrase searches, and machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Natural Language Processing and Natural Language Understanding techniques are used to compare software product solutions and business priorities, then the accuracy of matching client needs with solution packages is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary text embedding and relationship map generation before the actual matching process. By pre-processing the software product solutions and business priorities into structured relationship maps using NLP techniques, the system prepares data in advance to enable faster and more accurate matching during query execution, reducing real-time computational burden while maintaining high precision
Solution Approach 2:
The patent introduces relationship maps as an intermediary structure between raw text data and matching algorithms. These maps transform unstructured text into structured representations that capture semantic relationships, serving as a bridge that enables accurate matching without requiring complex real-time NLP processing during the actual solution recommendation phase
2Loss of information
If relationship maps are generated from electronic representations of software product solutions, then the understanding of client business priorities is improved, but the data processing time and computational resources increase
Solution Approach 1:
Relationship maps are generated in advance as pre-computed structures that capture the semantic relationships between software product solutions and business priorities. This preliminary generation allows the system to store processed information for rapid retrieval during matching operations, reducing real-time processing requirements while maintaining complete information about client needs and solution capabilities
Solution Approach 2:
The system creates simplified copies of the complex electronic representations in the form of relationship maps. These maps contain essential semantic information extracted from detailed text descriptions, enabling fast matching operations while preserving the key relationships needed to understand client business priorities without requiring access to the full original data
3Productivity
If keyword and key phrase searches are used to map business priorities to solution packages, then the speed of solution recommendation is improved, but the precision of matching decreases
Solution Approach 1:
The matching process is segmented into multiple stages: first using fast keyword and key phrase searches to identify candidate solution packages, then applying more sophisticated relationship map analysis to refine and rank these candidates. This segmentation allows the system to benefit from both the speed of simple keyword matching and the precision of comprehensive relationship analysis, processing only relevant subsets of data at each stage
Solution Approach 2:
The system dynamically adjusts the matching strategy based on the complexity and specificity of the business priorities. For simple queries, it relies more on fast keyword searches, while for complex queries, it engages deeper relationship map analysis. This dynamic approach optimizes the balance between processing speed and matching precision according to the specific requirements of each recommendation task
Data Source
AI summary
In an approach to improve mining and identifying priority relationships and solution packages by identifying client-satisfied solutions packages based on determined relationships, embodiments identify business priorities and software product solutions to recommend to a user. Additionally, embodiments receive electronic representations of the software product solutions utilized by an organization, and generate software product solution relationship maps based on the received electronic representations of software product solutions utilized by the organization. Further, embodiments map received business priorities to the generated software product solution relationship maps based on keyword and key phrase searches or full text similarity using statistical methods and machine learning models, recommend solution packages based on the mapped business priorities to the generated software product solution relationship maps, and output, by a user interface, the solution packages to users.