Automated Communication Routing via NLP Clustering
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Solution Overview
Problem
Existing communication systems for businesses face challenges in efficiently routing client communications across various formats (verbal, electronic, and physical) to the correct client files and departments, leading to misdirection, delayed responses, and compliance issues due to the need for human intervention and manual processing of large volumes of communications.
Innovation Solution
A computer communication network comprising an intake system to convert and format client communications into electronic records, a record distribution system to identify clusters, categorize, and route them to appropriate business departments, and a record handling system to manage the workflow, leveraging Natural Language Processing and machine learning for accurate categorization and routing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processing by customer service representatives is used to route client communications, then flexibility in handling diverse communication formats is maintained, but processing time increases and misrouting occurs
Solution Approach 1:
An automated intermediary system (intake system with NLP capabilities) is introduced between client communications and customer service representatives. This system converts various communication formats into standardized electronic records, extracts key information using natural language processing, and pre-routes communications to appropriate departments, reducing both processing time and misrouting while maintaining flexibility for complex cases
Solution Approach 2:
The system performs preliminary actions by automatically converting communications to standardized formats, extracting relevant information, identifying appropriate client files and departments, and preparing routing decisions before human intervention is needed. This preliminary processing significantly reduces the time and effort required by customer service representatives
2Productivity
If human representatives manually categorize and route each communication, then complex judgments can be made, but processing speed decreases and errors increase
Solution Approach 1:
The mechanical system of manual human judgment is replaced with an automated electronic system using natural language processing and machine learning algorithms. The intake system automatically analyzes communication content, extracts key information, compares it against client files and department criteria, and makes routing decisions electronically, significantly increasing processing volume while maintaining or improving categorization accuracy through consistent application of routing rules
Solution Approach 2:
The system enables self-service routing where communications automatically find their way to the correct departments without human intervention for routine cases. The automated system serves itself by making routing decisions based on extracted information and predefined criteria, reserving human representative involvement for exceptional or complex cases
3Adaptability or versatility
If multiple communication formats are accepted from clients, then client convenience is improved, but system complexity increases
Solution Approach 1:
The intake system is designed with universal capabilities to handle multiple communication formats (email, phone calls, letters, forms) through a single unified platform. The system converts all these different formats into a standardized electronic record structure, allowing the backend processing system to treat all communications uniformly regardless of their original format, thus maintaining versatility while reducing processing complexity
Solution Approach 2:
The system changes the parameter of communication format from diverse (email, phone, letter, form) to unified (standardized electronic record). By transforming all incoming communications into a common data structure with standardized fields and formats, the system maintains adaptability to accept various client preferences while simplifying internal processing to a single format
4Reliability
If client communications are manually associated with client files, then accurate matching can be achieved, but time consumption increases
Solution Approach 1:
The system uses feedback mechanisms where extracted information from communications is compared against stored client file data, and the matching process provides feedback on potential matches. The system can refine its matching based on this feedback, using the extracted information to confirm or adjust file associations, thereby maintaining high accuracy while reducing the time required through automated iterative matching
Data Source
AI summary
A method for processing and routing client communications based on information contained in the communications, the method comprising: receiving client communications in one or more formats selected from verbal, electronic, and physical; converting the received client communications into formatted electronic records; creating system records for the formatted electronic records; identifying clusters from the information in the formatted electronic records; categorizing the identified clusters; identifying clients from the information in the formatted electronic records; saving the system records and the formatted electronic records in data files for the identified clients; identifying business departments charged with handling identified clusters; and routing the system records and formatted electronic records to the identified business departments, wherein the system records and formatted electronic records are added to workflow of the business departments.