Intelligent customer service system

Abstract

The invention provides an intelligent customer service system, which comprises an information acquisition module used for identifying a customer identity according to a user login account and obtaining historical data under the account; the grading module is used for establishing a customer portrait model according to the historical data and classifying customer types; the matching module is used for automatically matching a service strategy model version corresponding to the account according to the customer type obtained by the grading module; the knowledge graph response module is used for carrying out deep analysis on client input contents based on the natural language understanding capability of a large language model in combination with a context, a client portrait and historical data after the version of the service strategy model is determined, automatically generating a task list and a semantic tag tree for a dialogue, and continuously optimizing the task list and the semantic tag tree; starting from a problem node of the task list, analyzing a corresponding semantic tag along an optimized and trained path in the knowledge graph, and matching a service processing action; and triggering a back-end system interface associated with the node according to needs, transferring real data, and solving client problems.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence and logistics integration technology, and in particular to an intelligent customer service system. Background Art

[0002] With the rapid growth of cross-border e-commerce and international logistics in South Africa, the logistics industry faces unprecedented challenges. As a key hub connecting the African continent with global markets, South Africa's logistics and supply chain management systems are becoming increasingly complex. This is particularly true in international transportation, warehousing, customs clearance, and distribution, which involve a vast amount of information and rely heavily on timely and accurate communication. South Africa's geographical environment, infrastructure, and government regulatory policies all place higher demands on the efficiency and quality of logistics services.

[0003] In cross-border e-commerce and logistics services, customers often face complex issues such as order tracking, shipment delays, and customs clearance. Currently, the traditional customer service model still dominates cross-border and international logistics companies in South Africa, mainly including the following forms:

[0004] Telephone customer service (human): Customers must proactively call, resulting in long wait times and high customer service pressure. This leads to high turnover within the customer service team and a knowledge gap. New employees require frequent training to adapt to business processes, which not only prolongs customer problem resolution cycles but also makes it difficult to maintain consistent service levels. It also makes it difficult for managers to build stable customer relationships, and training and management costs remain high.

[0005] Web page automatic question answering system: Based on a fixed knowledge base and keyword search, it cannot understand user context, has a high keyword misjudgment rate, and is very likely to cause customer complaints.

[0006] Web-based manual customer service: Although it can make up for the shortcomings of keyword search, the response time is still unstable, it cannot be expanded on a large scale, and its operational efficiency is low.

[0007] Manually proactively calling to respond to risk events: For example, packages being stolen, customers failing to receive their packages, etc., relying solely on manual one-on-one communication is inefficient and has serious delays.

[0008] The above approach not only causes unstable customer satisfaction, but also makes it difficult to manage the customer service team, resulting in high long-term operating costs and restricting the sustainable expansion of the company.

[0009] Therefore, we look forward to developing an intelligent customer service system to provide customers with more intelligent and efficient services. Summary of the Invention

[0010] The purpose of this invention is to provide an intelligent customer service system that can provide customers with more intelligent and efficient services.

[0011] In order to achieve the above objectives, the present invention provides an intelligent customer service system, comprising:

[0012] The information acquisition module is used to identify the customer's identity based on the user's login account and obtain the historical data under the account;

[0013] The classification module is used to build a customer portrait model based on historical data and classify customer types;

[0014] A matching module automatically matches the service policy model version corresponding to the account according to the customer type obtained by the classification module;

[0015] After determining the service strategy model version, the knowledge graph response module conducts an in-depth analysis of customer input based on the natural language understanding capabilities of the large language model, combined with context, customer portraits, and historical data. It automatically generates task orders and semantic label trees for the conversation, and continuously optimizes the task orders and semantic label trees. Starting from the problem node of the task order, it follows an optimized and trained path in the knowledge graph to parse the corresponding semantic labels and match business processing actions. It triggers the back-end system interface associated with the node as needed, mobilizes real data, and solves customer problems.

[0016] In an optional solution, the labels of the semantic label tree include: the type of problem input this time and the event risk level.

[0017] In an optional solution, the historical information includes: historical order data, historical conversation content and feedback records.

[0018] In an optional solution, the service policy model includes: a basic version, a professional version and an advanced version.

[0019] In an optional solution, the knowledge graph response module can continuously learn new paths from user behavior and system responses and dynamically expand capacity.

[0020] In an optional solution, when the knowledge graph response module identifies a historical high misjudgment problem or a time-sensitive problem, the model will be forced to adjust the confidence output.

[0021] In an optional solution, the intelligent customer service system also includes a feedback loop module, which is used to make structured records of each customer consultation process and write them into a unified service history database as a basis for dynamic updates of the customer portrait model and model version updates.

[0022] Among the optional solutions, the paths include: for routine inquiries, AI customer service completes the closed-loop response;

[0023] For events that meet escalation criteria, a work order is automatically generated and pushed to a human customer service representative. The human customer service representative will make a comprehensive assessment of the nature and priority of the issue. If necessary, the system can trigger a multi-department collaboration mechanism with one click to simultaneously distribute the work order to the responsible department.

[0024] For emergency high-risk events, manual pre-review is bypassed and the work order alert mechanism is directly triggered. The warning information is pushed to the account of the direct superior leader for emergency risk processing according to the event level, and the multi-department coordination mechanism is automatically triggered.

[0025] In an optional solution, the grading module is also used to establish a customer portrait model based on historical data and grade the account risk level.

[0026] In an optional solution, the knowledge graph response module can also send instructions to the back-end system to achieve information synchronization, task dispatching and status tracking.

[0027] The beneficial effects of the present invention are:

[0028] The intelligent customer service system of the present invention communicates with the back-end system interface, mobilizes real data, and solves customer problems instead of feeding back a preset result, breaking through the problems of universalization and delayed updates of existing AI customer service systems; classifies customer types and automatically matches the service strategy model version corresponding to the account; by generating task orders and semantic tag trees for conversations, it can find an optimal problem-solving path in the knowledge graph, shortening consultation time, improving the ability of intelligent customer service to automatically answer questions, reducing the frequency of manual intervention, and improving customer service quality.

[0029] Furthermore, the system includes an external customer-facing part and the function of sending feedback to internal employees. It can automatically generate work orders and push them to manual customer service. For urgent high-risk events, it bypasses manual pre-review and directly triggers the work order alert mechanism, improving the problem closure capability and the speed of abnormal response.

[0030] Furthermore, the system communicates with the back-end system and can send instructions to the back-end system to achieve information synchronization, task dispatching and status tracking. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings, in which like reference numerals generally represent like components.

[0032] Figure 1 This is a framework diagram of an intelligent customer service system in one embodiment of the present invention.

[0033] Figure 2Schematic diagram of the workflow of the intelligent customer service system in one embodiment of the present invention. DETAILED DESCRIPTION

[0034] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The advantages and features of the present invention will become more apparent from the following description and drawings. However, it should be noted that the technical solutions of the present invention can be implemented in a variety of different forms and are not limited to the specific embodiments described herein. The drawings are highly simplified and not to exact scale, and are intended solely to facilitate and clearly illustrate the embodiments of the present invention.

[0035] It should be understood that when an element or layer is referred to as being "on," "adjacent to," "connected to," or "coupled to" another element or layer, it can be directly on, adjacent to, connected to, or coupled to the other element or layer, or there can be intervening elements or layers. Conversely, when an element is referred to as being "directly on," "directly adjacent to," "directly connected to," or "directly coupled to" another element or layer, there are no intervening elements or layers. It should be understood that although the terms first, second, third, etc. may be used to describe various elements, components, regions, layers, and / or parts, these elements, components, regions, layers, and / or parts should not be limited by these terms. These terms are merely used to distinguish one element, component, region, layer, or part from another element, component, region, layer, or part. Thus, a first element, component, region, layer, or part discussed below may be represented as a second element, component, region, layer, or part without departing from the teachings of the present invention.

[0036] Spatially relative terms such as "under," "beneath," "below," "under," "above," "above," etc., may be used herein for convenience of description to describe the relationship of one element or feature shown in the figures to other elements or features. It should be understood that the spatially relative terms are intended to include different orientations of the device in use and operation in addition to the orientations shown in the figures. For example, if the device in the drawings is flipped, then the elements or features described as "under" or "beneath" or "beneath" the other elements will be oriented as "over" the other elements or features. Thus, the exemplary terms "under" and "under" may include both the upper and lower orientations. The device may be oriented otherwise (rotated 90 degrees or in other orientations) and the spatial descriptors used herein are interpreted accordingly.

[0037] The purpose of the terms used herein is only to describe specific embodiments and is not intended to limit the present invention. When used herein, the singular forms "a", "an", and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "comprising" and / or "comprising", when used in this specification, determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0038] Example 1

[0039] Reference Figure 1 and Figure 2 This embodiment provides an intelligent customer service system, including:

[0040] The information acquisition module is used to identify the customer's identity based on the user's login account and obtain the historical data under the account;

[0041] The classification module is used to build a customer portrait model based on historical data and classify customer types;

[0042] A matching module automatically matches the service policy model version corresponding to the account according to the customer type obtained by the classification module;

[0043] After determining the service strategy model version, the knowledge graph response module conducts an in-depth analysis of customer input based on the natural language understanding capabilities of the large language model, combined with context, customer portraits, and historical data. It automatically generates task orders and semantic label trees for the conversation, and continuously optimizes the task orders and semantic label trees. Starting from the problem node of the task order, it follows an optimized and trained path in the knowledge graph, parses the corresponding semantic labels, matches business processing actions, and triggers the back-end system interface associated with the node as needed, mobilizing real data to solve customer problems.

[0044] Specifically, when a customer initiates a consultation request through a website, app, mini-program, social media, or other channels, the system's information acquisition module identifies the customer based on the user's login account. Using AI models, the system automatically accesses historical data related to the account, including account information (such as account risk level and customer type), historical order data, past conversations, and feedback records, to achieve accurate customer identification and context restoration. The classification module uses this historical data to build a customer profile model and categorize customer types (e.g., regular customers, VIP customers, frequent complainers, etc.). This customer profile model combines order frequency, consultation content, and complaint records to dynamically classify customer types. Based on the customer type determined by the classification module, the matching module automatically matches the account with the corresponding service policy model version, including basic, professional, and advanced versions. For example, matching a VIP customer with the professional version of the service policy model allows for a shorter response path or manual fallback mechanism. All recognition results are recorded in the interaction context and service log for subsequent model inference and business tracking.

[0045] The knowledge graph response module treats customer-initiated conversations as "tasks." Using underlying structured tags, it drives behavioral control throughout the entire question-and-answer chain, including question routing, exception identification, priority determination, interface scheduling, and responsibility assignment. A "semantic tag tree" is automatically generated for all customer conversations, covering dimensions such as inquiry topic, behavioral action, emotional state, event risk level, order number, and time. Semantic tags dynamically participate in the model inference process. For example, every customer message is automatically identified as a pending task by the system. For example, if a customer says, "My delivery hasn't arrived in three days," the system will automatically generate a task ticket containing "logistics inquiry + complaint tendency." The system tags each conversation, such as topic tags: logistics issue / product inquiry / after-sales complaint; emotion tags: calm / doubtful / angry (determined by exclamation marks, repeated questions, etc.); event risk tags: general inquiry / escalated complaint / potential dispute; time tags: unresolved on the same day / over three days; and document tags: order number, express delivery number. The system then makes a comprehensive assessment of these dynamic semantic tags and adjusts the processing flow. If the comprehensive assessment results in a "normal" response, a standard answer is automatically provided, such as inquiring about the delivery status. If the overall judgment result is medium risk, it will be prioritized, such as when a logistics delay exceeds 3 days. If the overall judgment result is high risk, it will be immediately transferred to manual customer service, such as when sensitive words such as "I want to complain" are detected. The system will continuously optimize semantic label judgment: for example, the initial recognition of "refund" may be marked as a general inquiry, but combined with the customer's urgent tone of repeatedly asking "When will the money be credited to the account?", it will automatically be upgraded to a high-risk complaint. At the same time, historical records are linked to find that the customer just complained last week, which can immediately trigger the supervisor's intervention mechanism. When complex issues arise, the system automatically connects to the back-end system interface, mobilizes the latest real-world data, and can choose to simultaneously notify the customer service supervisor of the preparation for intervention and simultaneously generate a work order for the logistics department to verify.

[0046] The graph recognition response module can automatically identify the type of business problem a customer inquiry belongs to (such as status inquiry, address change, exception feedback, etc.) and the event risk level (normal, high priority, warning). During the recognition process, the system introduces the following key natural language processing technologies:

[0047] Named Entity Recognition (NER) automatically extracts entity information with business significance, such as waybill number, time, location, contact name, package number, etc., from unstructured text entered by customers, providing structured data support for subsequent question understanding and instruction execution.

[0048] Relation Extraction (RE) identifies multiple entities and then further identifies the semantic relationships between them. For example, in the sentence "Order SF202405016789 was delayed by 3 days," it identifies the attribution or causal relationship between "order number" and "status delay," thereby constructing a clear semantic graph for the problem.

[0049] The system combines the preset problem classification model with the knowledge graph linkage mechanism, and automatically generates corresponding problem labels (such as "not received", "complaint", "delayed for more than 3 days", etc.) based on the NER+RE recognition results, and creates a unique problem event ID through label drive to realize the structuring of subsequent processing paths.

[0050] Based on the problem type and event risk level judgment results, the system automatically calls the policy engine decision processing path. In this embodiment, for routine consultations (such as logistics status inquiries), the AI ​​customer service completes the closed-loop response; for events that hit the upgrade conditions (such as repeated complaints, timeouts, negative customer feedback, etc.), a work order is automatically generated and pushed to the manual customer service. The manual customer service makes a comprehensive judgment on the nature and priority of the problem. If necessary, the system triggers the multi-department collaboration mechanism with one click, and distributes the work order to the corresponding responsible department (such as warehousing, finance, delivery, etc.) to ensure the integrity and timeliness of the problem handling; for emergency high-risk events (such as reporting that the express was snatched, public opinion keywords involving major negative plots, etc.), bypassing manual pre-examination, directly triggering the work order alarm mechanism, and pushing the warning information to the direct superior leader account for emergency risk processing according to the event level, and automatically triggering the multi-department collaboration mechanism to ensure that the event is responded to and handled in the shortest time.

[0051] This embodiment is different from static FAQs or traditional knowledge bases. It builds a "scenario-specific knowledge graph" framework with the logistics life cycle as the core:

[0052] Each type of problem node (such as "customs delay" and "driver failed to pick up the package") is bound to the real system interface and handling rules; the "bound to the real interface" here means that the problem node does not exist only as a text label, but is directly connected to the actual operation interface in the backend business system. For example, the "customs delay" node will call the customs clearance status interface to obtain the latest information; the "driver failed to pick up the package" node will connect to the status interface of the driver dispatch system to confirm the processing progress. This binding mechanism makes each problem node in the knowledge graph executable, and can dynamically call system operations, obtain real business data, or initiate processing requests, thereby realizing the direct linkage between semantic understanding and system behavior.

[0053] After the model parses the question, it does not simply retrieve the answer, but instead calls the graph path to achieve the "semantics-action-system" trinity linkage. The so-called "calling the graph path" means that after the system understands the customer's intention, it does not stop at matching the answer on the surface, but starts from the question node and follows an optimized and trained path in the knowledge graph, successively parsing the corresponding semantic labels, matching business processing actions, and finally triggering the back-end system interface associated with the node. For example, when a customer says "my package is stuck at customs", the system will automatically follow the "customs clearance delay" path in the graph, identify the action of "customs clearance status query", and call the real-time customs clearance interface in the customs system to obtain status feedback. The entire path, from problem understanding, action selection to system operation, is supported by the structural definition and feedback mechanism of the knowledge graph, forming an intelligent decision-making chain with business executable.

[0054] The knowledge graph response module can continuously learn new paths (such as new processing solutions and new scenario classifications) from user behavior and system responses, and dynamically expand capacity.

[0055] In traditional customer service systems, robots typically only recognize keywords and provide a "predetermined answer." For example, if someone asks, "My package hasn't arrived," they'll simply display a fixed message: "Please be patient. Logistics is being processed." Whether it's the first time the question is asked or the fifth time, the system will respond the same way, often failing to resolve the issue. However, the customer service system presented in this invention is different. It employs a "scenario knowledge graph," like a mobile map, to pinpoint the problem based on the customer's words. It then leverages real-world data within the system to verify, for example, whether the driver has accepted the order, whether the package is stuck at customs, or whether the customer has previously refused delivery. Not only does it identify these issues, but it also provides recommendations for next steps based on the results, even directly triggering internal processes such as generating a customer service follow-up ticket or notifying the dispatcher to reassign a driver. Simply put, this system doesn't provide a rigid answer, but rather an action—or even the beginning of a new transaction. In addition, the system can remember what the customer said, even if it was expressed vaguely, such as "Why is there no news about that returned item yet?" It can know that the customer is talking about yesterday's shipment and which order number it is. It can also review the questions previously asked by the customer and make more reasonable judgments.

[0056] To ensure the AI ​​customer service model possesses cross-temporal and self-learning capabilities, this embodiment employs the following adaptive mechanisms: It collects missed questions, low-rated responses, and repeated escalation requests in real time to automatically generate a model optimization task pool. It also incorporates a "Risk Calibration Layer" to forcibly adjust the model's confidence output when identifying issues with a high historical misjudgment rate or time-sensitive issues (such as holiday deliveries) to prevent misleading customers. It also integrates a "task chain evaluation system" to track the hit rate, response time, and customer satisfaction of each interaction across the entire service path, serving as a basis for model version updates. The "task chain evaluation system" here is not an independent platform external to the AI ​​customer service system, but rather a feedback loop module embedded within the customer service system architecture of the present invention. It primarily performs a structured evaluation of each complete customer interaction path. Its functions include determining whether the AI ​​successfully identified the semantic path, whether the correct graph interface was hit, whether the response was completed within the specified timeframe, and recording whether the customer was satisfied or transferred to a human operator, thus providing data support for system optimization. Each service process is automatically converted into a structured service log for subsequent learning and optimization. The system has a built-in anomaly cluster analyzer to regularly screen the consistency, accuracy, and upgrade rate of responses to similar questions.

[0057] The system maintains a structured record of every customer consultation (including initial problem description, automated identification results, processing path, manual intervention points, final conclusion, and satisfaction score) and stores it in a unified service history database. Event IDs are integrated with processing logs from various business systems to enable traceability and accountability. All archived information is used to dynamically update customer profiling models and accumulate samples for subsequent model training.

[0058] The system generates daily / weekly problem analysis reports through the data analysis platform, counting key indicators such as the frequency of different problem types, work order response time, upgrade conversion rate, manual intervention ratio, customer satisfaction, and displaying trends and fluctuations through a visual dashboard. The reports are pushed to relevant departments (such as product, customer service, warehousing, delivery, etc.) by business domain, supporting the optimization of process bottlenecks and the identification of high-frequency anomalies and unreasonable rules at the operational level. At the same time, the system automatically summarizes misjudgment samples and unresponsive issues and feeds them back to the model training system for iterative optimization of the language model and completion of the knowledge base, forming a closed loop for continuous improvement of service capabilities.

[0059] In this embodiment, the grading module is also used to build a customer profile model based on historical data and classify account risk levels, such as high, medium, and low risk accounts, to determine whether to initiate early warning monitoring, trigger multiple rounds of verification, and record risk control logs.

[0060] Natural Language Understanding (NLU) is an important branch of artificial intelligence technology, aiming to enable the system to understand the intention, semantics, and emotions in human language. This embodiment uses "multilingual NLU" technology, which is specifically reflected in:

[0061] Syntax Parsing: Accurately identify the input sentence structure and analyze the subject, predicate, and object relationships of the sentence.

[0062] Intent Detection: Determine customer intent, such as status inquiries, complaints, and return requests.

[0063] Entity Recognition: Extract business-critical entities such as order numbers, time nodes, addresses, contacts, etc.

[0064] Semantic Normalization: This method achieves unified semantic modeling for different languages, slang, and mixed expressions (such as mixed Chinese and English, or Zulu). Given the complex language landscape in South Africa, this paper proposes a "dynamic multi-semantic recognition + contextual cross-language normalization" approach that integrates multilingual NLU and contextual discrimination technologies.

[0065] A cross-language variant dictionary is introduced to support the recognition of expressions such as English-Zulu hybrids, slang, and transcoded semantics.

[0066] Real-time detection of high-risk representations in vocabulary (such as "missing again," "never arrived," and "police") and labeling and push notifications;

[0067] The system will simultaneously check whether the issue is raised for the first time, whether it occurs repeatedly, and whether it has been paid in the past to improve the accuracy of judgment.

[0068] In this embodiment, a "customer-order-problem" association path is established in combination with the graph structure to improve the concurrent recognition capability of multiple orders and multiple scenarios.

[0069] Graph structure: refers to a graphical data structure composed of "nodes" and "relationships." In this scenario, nodes can be customers, orders, question types, etc., and relationships can be "orders placed," "returns occurred," "status inquired about," etc. Customer-order-question path: That is, in the graph, the system links a customer with their historical orders and questions they are currently inquiring about, forming a "path" with semantic logic. Concurrent recognition capability: The system can recognize multiple orders or multiple questions simultaneously without confusion or omission, and is particularly suitable for complex situations where customers "ask multiple questions in succession" or "ask questions across orders."

[0070] For example, suppose a customer says in a conversation:

[0071] "I haven't received either of my two packages from last week. Has the returned one been processed? The other one, which was sent to Cape Town, has no update either."

[0072] This sentence involves: two different orders; two status issues (one is return processing, the other is delivery delay); ambiguous time expression ("last week"); ambiguous reference ("the one that was returned").

[0073] Difficulties of traditional systems: Traditional FAQ or keyword systems may only recognize the keyword "not received" and cannot determine which ticket it refers to, nor can they separate two different problem scenarios.

[0074] The method of the system of the present invention:

[0075] 1. Customer node: Identifies the user who asked the question; 2. Order node: Retrieves their order records from last week; 3. Problem node: Identifies issues such as returned items and delivery delays; 4. The system automatically creates a path map:

[0076] Customer A → Order ① (return) → Question: Process or not

[0077] Customer A → Order ② (delivery in progress) → Question: Status Inquiry

[0078] The system automatically dispatches two processing flows through the graph path to respond separately. By establishing this "customer-order-problem" path relationship, the system has the following capabilities:

[0079] Multi-order identification: Detects multiple orders in a single conversation and handles them separately. Multi-question splitting: Identifies when a customer's question involves multiple problem scenarios. Exception tracing: Quickly traces whether a question is a repeat or has been addressed previously. Problem path linkage: Enables a closed-loop decision-making process from identification → routing → response.

[0080] When an issue is identified requiring reassignment, the system connects with multiple systems to synchronize information, assign tasks, and track status. For example, if the issue is a "failed pickup or returned package," the system will collaborate with the delivery platform to search the delivery record, initiate an exception confirmation, and notify warehouse personnel to prepare for reassignment. Task status within the system is synchronized with the user interface, enabling real-time, visual tracking of issues.

[0081] The intelligent customer service system of the present invention communicates with the back-end system interface, mobilizes real data, and solves customer problems instead of feeding back a preset result, breaking through the problems of universalization and delayed updates of existing AI customer service systems; classifies customer types and automatically matches the service strategy model version corresponding to the account; by generating task orders and semantic tag trees for conversations, it can find an optimal problem-solving path in the knowledge graph, shortening consultation time, improving the ability of intelligent customer service to automatically answer questions, reducing the frequency of manual intervention, and improving customer service quality.

[0082] Furthermore, the system includes an external customer-facing part and the function of sending feedback to internal employees. It can automatically generate work orders and push them to manual customer service. For urgent high-risk events, it bypasses manual pre-review and directly triggers the work order alert mechanism, improving the problem closure capability and the speed of abnormal response.

[0083] Furthermore, the system communicates with the back-end system and can send instructions to the back-end system to achieve information synchronization, task dispatching and status tracking.

[0084] The system performs semantic analysis and event risk classification on all issues, identifying common anomaly categories such as "delay," "return," "loss," and "unreachable recipient." Based on pre-set enterprise rules, it determines whether to automatically transfer the issue to a human operator, generate a work order, or trigger an alert. Conversations identified as "escalated events" require no manual screening and are automatically pushed to the system account of the responsible team (such as the warehouse or management department), initiating a rapid response channel.

[0085] The system has achieved the following beneficial effects:

[0086] 1. Significantly improve response efficiency and accuracy

[0087] The system can automatically answer more than 70% of customer questions, increasing the first-question resolution rate to more than 80%, significantly reducing repeated consultations and misunderstandings.

[0088] 2Significantly reduce the burden of manual operations

[0089] Automatic identification and dispatch capabilities have reduced the proportion of manual agent intervention by more than 50%, allowing the customer service team to focus on handling highly complex incidents, significantly improving overall labor efficiency.

[0090] 3Optimize customer experience and improve satisfaction

[0091] It supports functions such as natural language understanding, multi-round conversations, and real-time linkage feedback, greatly improving the user communication experience. The customer satisfaction score has steadily increased to above 4.5 points (out of 5 points).

[0092] 4. Improve problem-solving capabilities and exception response speed

[0093] The problem classification and escalation mechanism (referring to the upgrade of event risk levels) has shortened the time for exception handling by more than 40%, especially the average processing time for returns, complaints, and delays has been shortened from 48 hours to less than 12 hours.

[0094] The above description is only a description of the preferred embodiments of the present invention and does not limit the scope of the present invention. Any changes and modifications made by ordinary technicians in the field of the present invention based on the above disclosure shall fall within the scope of protection of the claims.

Claims

1. An intelligent customer service system, characterized in that: include: The information acquisition module is used to identify the customer's identity based on the user's login account and obtain the historical data under the account; The classification module is used to build a customer portrait model based on historical data and classify customer types; A matching module automatically matches the service policy model version corresponding to the account according to the customer type obtained by the classification module; After determining the service strategy model version, the knowledge graph response module conducts an in-depth analysis of customer input based on the natural language understanding capabilities of the large language model, combined with context, customer portraits, and historical data. It automatically generates task orders and semantic label trees for the conversation, and continuously optimizes the task orders and semantic label trees. Starting from the problem node of the task order, it follows an optimized and trained path in the knowledge graph to parse the corresponding semantic labels and match business processing actions. It triggers the back-end system interface associated with the node as needed, mobilizes real data, and solves customer problems.

2. The intelligent customer service system according to claim 1, wherein: The labels of the semantic label tree include: the type of the problem input this time and the event risk level.

3. The intelligent customer service system according to claim 1, wherein: The historical information includes: historical order data, historical conversation content and feedback records.

4. The intelligent customer service system according to claim 1, wherein: The service strategy model includes: basic version, professional version and advanced version.

5. The intelligent customer service system according to claim 1, wherein: The knowledge graph response module can continuously learn new paths from user behavior and system responses and dynamically expand capacity.

6. The intelligent customer service system according to claim 1, wherein: When the knowledge graph response module identifies a historically high misjudgment problem or a time-sensitive problem, the model will be forced to adjust the confidence output.

7. The intelligent customer service system according to claim 1, wherein: The intelligent customer service system also includes a feedback loop module for making structured records of each customer consultation process and writing them into a unified service history database as a basis for dynamic updates of the customer portrait model and model version updates.

8. The intelligent customer service system according to claim 1, wherein: The paths include: For routine inquiries, AI customer service will provide closed-loop responses. For events that meet escalation criteria, a work order is automatically generated and pushed to a human customer service representative. The human customer service representative will make a comprehensive assessment of the nature and priority of the issue. If necessary, the system can trigger a multi-department collaboration mechanism with one click to simultaneously distribute the work order to the responsible department. For urgent high-risk events, manual pre-review is bypassed and the work order alert mechanism is directly triggered. The warning information is pushed to the account of the immediate superior for emergency risk processing based on the event level, and the multi-department coordination mechanism is automatically triggered.

9. The intelligent customer service system according to claim 1, wherein: The grading module is also used to establish a customer profile model based on historical data and grade account risk levels.

10. The intelligent customer service system according to claim 1, wherein: The knowledge graph response module can also send instructions to the back-end system to achieve information synchronization, task dispatching and status tracking.

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