RPA Case Assistant for Real-Time Client Support Resolution
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Solution Overview
Problem
Existing client service systems lack the capability to analyze and process the complex interconnections between various factors related to case resolution, such as skill sets, case history, and business execution exceptions, making it difficult for client support professionals to efficiently resolve client cases in real-time.
Innovation Solution
A case assistant system utilizing Robotic Process Automation (RPA) and Machine Learning (ML) technologies to provide real-time assistance by identifying similar cases, skilled experts, and raising execution exceptions, thereby enhancing collaboration and skill gap awareness among professionals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional client service systems are used to collect and track case data, then case resolution can be monitored, but the systems lack the capability to analyze complex interconnections between skill sets, case history, and business execution exceptions in real-time
Solution Approach 1:
The patent introduces an intermediary system comprising multiple modules (skill set analyzer, case history analyzer, exception identifier, and recommendation generator) that act as mediators between raw data sources and client support professionals. These modules process and analyze complex interconnections between skill sets, case history, and business execution exceptions, transforming unstructured data into actionable real-time recommendations without requiring the entire system to be overly complex.
2Productivity
If detailed real-time analysis of skill set data and case history data is performed, then case resolution quality improves, but the analysis capacity required exceeds human capability
Solution Approach 1:
The patent segments the data analysis function into distinct specialized modules: a skill set analyzer that processes professional capability data, a case history analyzer that examines past case information, and an exception identifier that detects business execution anomalies. Each module handles specific data types and analysis tasks independently, dividing the complex analysis workload into manageable segments that can be processed simultaneously and efficiently.
Solution Approach 2:
The system enables self-service analysis by automatically processing and interpreting complex data interconnections without requiring manual human intervention. The automated recommendation generator synthesizes insights from skill set and case history analysis, producing actionable recommendations that client support professionals can directly utilize for case resolution, thereby eliminating the bottleneck of human cognitive capacity.
3Ease of operation
If traditional systems track case data without real-time analysis, then system simplicity is maintained, but the ability to provide practical real-time results is insufficient
Solution Approach 1:
The patent implements continuous real-time analysis through constantly active monitoring and processing. The skill set analyzer, case history analyzer, and exception identifier operate continuously to detect changes in data patterns, enabling the system to provide timely recommendations as cases evolve. This continuous action ensures that client support professionals receive up-to-date assistance without delays, transforming static data tracking into dynamic real-time analysis.
Data Source
AI summary
A case assistant is provided to client support professionals, which utilizes robotic process automation (RPA) technologies to analyze large amounts of data related to historical client cases that are similar to current open cases, data related to skilled experts associated with similar client cases, and data related to business exceptions. Several processes are utilized to provide this data to client support professionals, including a document similarity finder that utilizes a vector data collector, a tokenizer, a stop word remover, a relevance finder, and a similarity finder, several of which utilize a variety of machine learning technologies. Additional processes include a skilled experts finder and a business exceptions finder.