System for optimizing multi-channel customer service using AI and distributed architecture

Abstract

A system (100) for optimizing multi-channel customer service using artificial intelligence (AI) and a distributed architecture, wherein the system comprises: a channel integration layer (1) configured to receive customer requests from multiple communication channels, including voice, email, chat and social media, and normalize the requests into a uniform format; a distributed message queue (2) configured to asynchronously receive and manage requests, wherein the message queue (2) ensures scalability, fault tolerance and high availability; a KL processing engine (3) comprising the following: a sentiment analysis module (31) configured to classify customer sentiment using a transformer-based model trained on labeled datasets of customer interactions; an intent detection module (32) configured to identify the customer's intent based on the requests using supervised learning algorithms trained on historical data; a task prioritization unit (33) configured to calculate a priority value for each request based on sentiment, intent and urgency; a task distribution service (4) configured to dynamically assign tasks to agents based on availability, skills and task priority; a real-time dashboard module (5) that includes the following: an agent dashboard (51) configured to display prioritized task queues with live status updates; a supervisor dashboard (52) configured to visualize query trends, sentiment distribution, backlog of tasks and agent performance metrics; where the system (100) uses a microservices architecture (6) and a cloud-based deployment (7) to dynamically scale services, maintain fault tolerance, and deliver actionable insights for optimizing customer service.

Description

INVENTION AREA

[0001] The present invention relates to the field of customer service management systems.

[0002] In particular, it concerns a multi-channel platform that integrates artificial intelligence (AI) and a distributed system architecture to optimize customer service operations across different communication channels.

[0003] In particular, the present invention relates to a system for optimizing multi-channel customer service using AI and a distributed architecture. BACKGROUND OF THE INVENTION

[0004] The subject matter discussed in the "Background" section should not be considered prior art solely because it is mentioned in that section. Likewise, a problem mentioned in the "Background" section or related to the subject matter of the "Background" section should not be considered prior art. The subject matter in the "Background" section merely presents various approaches, which could themselves also be inventions.

[0005] In today's digital environment, customer service departments face the challenge of managing an ever-increasing volume of interactions across multiple communication channels. Customers expect businesses to be accessible through traditional phone calls, emails, live chat portals, and newer platforms like social media and instant messaging applications. However, most traditional systems are channel-specific and siloed, meaning each channel is managed independently without seamless integration. This fragmentation leads to duplication of effort, a lack of contextual awareness, and inefficient handling of customer inquiries.

[0006] A critical drawback of these fragmented systems is the delay in response times. While voice queries, for example, can be answered quickly, email or social media interactions often remain unresolved for extended periods. This inconsistency negatively impacts customer satisfaction and brand reputation. Furthermore, without centralized intelligence, these systems are unable to prioritize requests effectively. A critical request, such as when a customer expresses dissatisfaction or reports a technical issue, might be treated with the same priority as a routine request, thus delaying the resolution of urgent cases.

[0007] On most traditional platforms, manual task assignment remains the norm. Supervisors or administrators manually assign requests to employees, often without considering their specific skills, workload, or real-time availability. This approach frequently leads to inappropriate assignments—for example, a technical issue being assigned to an employee in the billing department—which not only delays resolution but also frustrates both customers and employees.

[0008] Another significant limitation lies in the lack of scalability and fault tolerance. With increasing digitalization, companies frequently experience spikes in customer interactions, particularly during sales events, product launches, or service outages. Existing customer service platforms, which are monolithic and centralized, struggle to efficiently handle such spikes. This leads to system slowdowns, downtime, and the loss of critical inquiries. Furthermore, fault tolerance mechanisms are often minimal, meaning that a single point of failure can disrupt the entire customer service operation.

[0009] While some modern solutions offer rudimentary automation capabilities, they typically lack the sophistication of AI-based analytics. Without sentiment analysis, intent recognition, and real-time prioritization, such systems cannot distinguish between a satisfied customer making a request and a dissatisfied customer expressing urgency. Consequently, opportunities for proactive solutions and customer retention are often missed.

[0010] The monitoring tools in existing platforms are also inadequate. Managers often rely on static reports and manual monitoring instead of real-time dashboards. This lack of live insights prevents supervisors from identifying bottlenecks, effectively tracking employee performance, or quickly responding to new trends in customer sentiment.

[0011] Therefore, there is an urgent need for a unified, intelligent, and distributed system to optimize customer service. Such a system should not only consolidate communication channels onto a single, integrated platform but also leverage AI technologies for sentiment analysis, intent recognition, and intelligent routing. Furthermore, the system should utilize distributed and cloud-based architectures to ensure scalability, resilience, and high availability. By combining these features, the proposed invention overcomes the inefficiencies of traditional systems and offers an optimized, future-proof solution for modern customer service challenges.

[0012] As used in the present description and in the following claims, the meaning of "ein", "eine", and "das" also includes the plural unless the context clearly indicates otherwise. As used in the present description, the meaning of "in" also includes "in" and "on" unless the context clearly indicates otherwise.

[0013] The use of value ranges in this description serves only as a shorthand way to refer to each individual value within the range. Unless otherwise stated herein, each individual value is included in the description as if it were listed separately.

[0014] The use of all examples or illustrative phrases (e.g., "as") relating to specific embodiments in this description serves only to better illustrate the invention and does not constitute a limitation of the otherwise claimed scope of the invention. No phrase in the description shall be construed as referring to an unclaimed element that is essential for carrying out the invention.

[0015] The information disclosed above in this "Background" section is provided solely for a better understanding of the background of the invention and may therefore contain information that is not part of the prior art already known to a person skilled in the art in this country. SUMMARY

[0016] Before describing the systems and methods presented here, it should be noted that this application is not limited to the specific systems and methods described, as there may be several possible embodiments not expressly presented in this disclosure. It should also be noted that the terminology used in the description serves only to describe the specific versions or embodiments and is not intended to limit the scope of this application.

[0017] The present invention addresses and solves primarily the technical problems existing in the existing technology. In response to these problems, the present invention discloses a system for optimizing multi-channel customer service using AI and a distributed architecture.

[0018] The invention provides a system (100) for optimizing multi-channel customer service, which uses AI-supported sentiment analysis (31), intent recognition (32) and task prioritization (33) in combination with a distributed message queue (2) and a microservices architecture (6).

[0019] The system (100) integrates multiple channels, including voice, email, chat, and social media, via a channel integration layer (1). Requests are normalized and routed through a distributed queue (2), where the AI ​​processing engine (3) assesses sentiment, intent, and urgency to assign a priority score. A task distribution service (4) assigns requests to agents based on their real-time availability, skills, and priority.

[0020] Supervisors and agents access real-time dashboards (5), including the agent dashboard (51) and the supervisor dashboard (52), which provide actionable insights into workload, sentiment trends, and performance metrics.

[0021] By using cloud deployment (7) and distributed architecture, the system (100) ensures scalability, fault tolerance and high availability, thus enabling the efficient processing of millions of requests simultaneously. BRIEF DESCRIPTION OF THE DRAWING

[0022] To clarify various aspects of some embodiments of the present invention, a more detailed description of the invention is given with reference to specific embodiments illustrated in the accompanying drawing. It should be noted that this drawing only illustrates embodiments of the invention and is therefore not to be considered a limitation of its scope. The invention is described and explained with additional specificity and detail using the accompanying drawing.

[0023] To make the advantages of the present invention easily understandable, a detailed description of the invention is given below in conjunction with the accompanying drawing, which, however, is not to be understood as limiting the scope of the invention to the accompanying drawing, in which: Fig. a block diagram of the system (100) for optimizing multi-channel customer service using AI and a distributed architecture is shown. DETAILED DESCRIPTION

[0024] The present invention relates to the system (100) for optimizing multi-channel customer service using AI and distributed architecture.

[0025] Fig. shows a detailed block diagram of the system (100) for optimizing multi-channel customer service using AI and distributed architecture.

[0026] Although the present disclosure has been described with the aim of providing a system for optimizing multi-channel customer service using AI and distributed architecture, it should be noted that this is merely to illustrate the invention by way of example and to highlight other purposes or functions for which the described structures or configurations could be used and which fall within the scope of the present disclosure.

[0027] The present invention discloses a system (100) for optimizing multi-channel customer service using artificial intelligence (AI) and a distributed architecture. The system (100) is designed to seamlessly receive inquiries from various customer communication channels, intelligently process them to determine sentiment and intent, prioritize tasks according to urgency, and dynamically route them to suitable agents. Furthermore, the system (100) provides real-time dashboards for agents and supervisors, thus ensuring transparency, efficiency, and improved customer satisfaction.

[0028] The system (100) includes a channel integration layer (1) that serves as the entry point for customer requests. The channel integration layer (1) is configured to accept input from multiple sources, including voice calls, emails, live chat sessions, and social media platforms. To this end, layer (1) provides several application programming interfaces (APIs), including REST-based endpoints for chat and email integration, and WebSocket-based endpoints for real-time voice and social media interactions. Upon receipt of the requests, they are normalized to a uniform JSON data format to ensure compatibility and standardization for downstream AI processing components. This uniform representation eliminates inconsistencies that can arise due to the heterogeneity of the communication channels.

[0029] A distributed message queue (2) is connected downstream of the channel integration layer (1). The distributed message queue (2) is responsible for the asynchronous capture and buffering of customer requests, thus enabling the processing of large data volumes and fault tolerance. The queue (2) supports partitioned topics that separate requests by channel origin, ensuring efficient parallel processing. Furthermore, it includes offset management for the accurate playback and recovery of messages in the event of a node failure. Replication mechanisms are implemented within the distributed queue (2) to ensure high availability and fault tolerance. A typical implementation of the queue (2) can use Apache Kafka or equivalent distributed streaming technologies.

[0030] The core of the invention is an AI processing engine (3) comprising several intelligent modules. The sentiment analysis module (31) uses pre-trained transformer-based models such as BERT or RoBERTa, which have been fine-tuned using large datasets of labeled customer interactions. This module (31) categorizes sentiment into positive, neutral, or negative classes with associated confidence levels. This is complemented by the intent detection module (32), which applies supervised learning models trained on historical customer interaction data to classify the intent of a request, e.g., "billing request," "technical support," or "account management." Finally, the task prioritization unit (33) integrates the outputs of modules (31) and (32) together with urgency indicators to calculate a priority value according to the following formula: Priority value = w1 × Urgency + w2 × Mood + w3 × Certainty of Intention where w1, w2, and w3 are configurable weights. This prioritization ensures that urgent and important requests are processed before routine requests.

[0031] The invention further comprises a task routing service (4) responsible for assigning tasks to agents in real time. The routing service (4) uses the calculated priority rating together with contextual metadata such as agent availability and competency profiles to determine the most appropriate assignment. It ensures that critical requests are routed to available agents with the required expertise, while lower-priority requests are assigned accordingly to prevent backlogs. Furthermore, the routing service (4) has dynamic reassignment mechanisms that enable real-time reassignment if an agent becomes unavailable or if the priority of a request changes during processing.

[0032] To provide transparency and actionable insights, the system (100) features a real-time dashboard module (5). This module is divided into two main components: an agent dashboard (51) and a supervisor dashboard (52). The agent dashboard (51) displays a prioritized queue of tasks assigned to the agent, with live status updates reflecting new tasks and their completion. The supervisor dashboard (52) provides a more comprehensive overview of operations, including customer sentiment trends, distribution of request types, backlog statistics, and agent performance metrics. Both dashboards are implemented using modern web technologies, such as React.js for the front end and WebSocket protocols for continuous real-time updates, ensuring minimal latency in data visualization.

[0033] The entire platform is based on a microservices architecture (6), in which independent services handle specific functions such as AI processing, task routing, dashboard rendering, and channel integration. This modular approach enables independent scaling, fault isolation, and flexible deployment. The system (100) is hosted via a cloud deployment (7), which ensures elasticity, distributed load balancing, and robust resilience under varying workloads. The cloud infrastructure also supports geographically distributed deployments, improving global accessibility and minimizing latency for international customer bases.

[0034] To address concerns regarding data protection and security, the system (100) incorporates several security features. Data in transit is protected by TLS (Transport Layer Security) encryption, while stored data is secured by AES-256 encryption. Furthermore, the system (100) uses role-based access control (RBAC) to restrict access to sensitive functions and data according to predefined user roles, thus ensuring compliance with industry standards such as GDPR and ISO / IEC 27001.

[0035] In operation, requests are initially received via the channel integration layer (1) and moved to the distributed message queue (2). The AI ​​processing engine (3) analyzes each request to extract sentiment, intent, and urgency, after which a priority score is calculated. The task routing service (4) then assigns the requests to the appropriate agents, and the real-time dashboards (5) provide visibility to both agents (51) and supervisors (52). By combining AI intelligence, distributed scalability, and real-time visualization, the system (100) offers a robust, efficient, and future-proof solution for optimizing customer service. Example of the invention

[0036] The system architecture comprises modular components designed for scalability, fault tolerance, and efficient query processing. Key components include: • Channel integration layer

[0037] The channel integration layer is responsible for capturing requests from multiple communication channels, including voice, email, chat, and social media. APIs normalize these requests into a uniform JSON format so they can be processed by downstream components. These APIs include: • REST-based endpoints for email and chat integration. • WebSocket endpoints for real-time voice and social media interactions. • Distributed message queue

[0038] A distributed message queue (e.g., Apache Kafka) ensures reliable and scalable task handling. Each channel has its own topic to separate requests by source. Features include: • Partitioned topics to enable parallel processing. • Offset management for fault tolerance and message reproduction. • Replication to ensure high availability in the event of a node failure. • AI processing engine

[0039] The AI ​​engine is the central processing unit for analyzing and prioritizing tasks. It includes: • Sentiment analysis: • Implements a transformer-based model (e.g., BERT) that is fine-tuned on tagged customer service datasets. • The results include the classification of mood (positive, neutral, negative) and confidence scores. • Intention recognition: • Uses supervised learning algorithms trained on historical customer datasets. • Predicts customer intentions such as "invoice request" or "technical support" with confidence levels. • Task prioritization: • Combines values ​​from mood and intention models using a weighted formula: • Priority value = w1 * urgency + w2 * mood + w3 * certainty of intent, where w1: urgency (or criticality) in customer service, w2: mood influences customer satisfaction and w3: (certainty of intent ensures the relevance of the task. • Task forwarding service

[0040] This service dynamically assigns tasks to agents based on: • Availability: Checks the status of agents in real time. • Skill Match: Routes tasks requiring specific expertise to qualified agents. • Priority: Assigns high-priority tasks first, thus ensuring a timely solution. • Real-time dashboards

[0041] Dashboards provide agents and managers with actionable insights. Features include: • Agent dashboard: Displays prioritized task queues with live status updates. • Supervisor dashboard: Visualizes mood trends, backlogs of tasks, and team performance metrics.

[0042] The dashboards are implemented using React.js for the frontend and WebSocket for real-time updates. • Safety measures

[0043] To ensure data integrity and privacy, the system includes: • TLS encryption: Secures data during transmission. • AES-256 encryption: Protects sensitive data at rest. • Role-based access control (RBAC): Restricts access based on user roles. Workflow • Query capture: Queries from channels are forwarded to a distributed message queue. • AI processing: Sentiment and intent models analyze queries to determine task priorities. • Task routing: Tasks are routed to agents based on priority and skills. • Visualization: Dashboards display task status, trends, and metrics.

[0044] The figure and the preceding description provide examples of embodiments. Those skilled in the art will recognize that one or more of the described elements can be combined to form a single functional element. Alternatively, certain elements can be divided into several functional elements. Elements from one embodiment can be added to another embodiment. For example, the sequence of the processes described here can be changed and is not limited to the manner described here. Furthermore, the actions of a block diagram need not be implemented in the order shown, nor does it necessarily have to be executed all actions. In addition, those actions that are not dependent on other actions can be executed in parallel with the other actions. The scope of embodiments is by no means limited by these specific examples.

[0045] Although the embodiments of the invention have been described in language relating to structural features and / or methods, it should be noted that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of embodiments of the invention.

Claims

[1] A system (100) for optimizing multi-channel customer service using artificial intelligence (AI) and a distributed architecture, wherein the system comprises: a channel integration layer (1) configured to receive customer requests from multiple communication channels, including voice, email, chat and social media, and normalize the requests into a uniform format; a distributed message queue (2) configured to asynchronously receive and manage requests, wherein the message queue (2) ensures scalability, fault tolerance and high availability; a KL processing engine (3) comprising the following: a sentiment analysis module (31) configured to classify customer sentiment using a transformer-based model trained on labeled datasets of customer interactions; an intent detection module (32) configured to identify the customer's intent based on the requests using supervised learning algorithms trained on historical data; a task prioritization unit (33) configured to calculate a priority value for each request based on sentiment, intent and urgency; a task distribution service (4) configured to dynamically assign tasks to agents based on availability, skills and task priority; a real-time dashboard module (5) that includes the following: an agent dashboard (51) configured to display prioritized task queues with live status updates; a supervisor dashboard (52) configured to visualize query trends, sentiment distribution, backlog of tasks and agent performance metrics; where the system (100) uses a microservices architecture (6) and a cloud-based deployment (7) to dynamically scale services, maintain fault tolerance, and deliver actionable insights for optimizing customer service. [2] System (100) according to claim 1, wherein the distributed message queue (2) comprises partitioned topics, offset management and replication to enable parallel processing and message replay in the event of a failure. [3] System (100) according to claim 1, wherein the sentiment analysis module (31) uses a finely tuned transformation model selected from BERT-, RoBERTa- or GPT-based classifiers for multi-class sentiment classification. [4] System (100) according to claim 1, wherein the task forwarding service (4) implements real-time reassignment mechanisms to handle changes in agent availability or query priority. [5] System (100) according to claim 1, wherein the real-time dashboard module (5) is implemented using a web-based frontend with WebSocket protocols to provide continuous updates. [6] System (100) according to claim 1, wherein the task prioritization unit (33) calculates the priority rating using a weighted algorithm defined as follows: Priority value = w1 × Urgency + w2 × Mood + w3 × Certainty of Intention where w1, w2 and w3 are configurable weights. [7] System (100) according to claim 1, wherein the security measures include Transport Layer Security (TLS) for data during transmission, AES-256 encryption for stored data and role-based access control (RBAC) for access management. [8] System (100) according to claim 1, wherein the channel integration layer (1) comprises application programming interfaces (APIs), including REST-based endpoints for email and chat, and WebSocket endpoints for voice and social media requests. [9] System (100) according to claim 1, wherein the agent dashboard (51) provides real-time task queues, while the supervisor dashboard (52) displays trends in customer sentiment, backlog statistics and performance analysis. [10] System (100) according to claim 1, wherein the system (100) is configured to handle millions of simultaneous queries without performance degradation by employing distributed load balancing across the microservices architecture (6).

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