Intelligent customer service system based on deep learning

Abstract

The invention discloses an intelligent customer service system based on deep learning, and the system comprises a front-end receiving module which is used for receiving the input of a user; the voice processing module is used for preprocessing the audio signal and converting the voice into a text; the natural language processing module is used for analyzing the text generated by the voice processing module; the dialogue management module is used for maintaining a dialogue state and guiding a dialogue process; the knowledge base and information retrieval module is used for providing a structured database or a knowledge graph; the natural language generation module is used for converting a result of the dialogue management module into a language response; and the speech synthesis module is used for converting the text into speech and outputting the speech. According to the invention, real-time monitoring and response to user emotion are realized, interaction experience is improved, user stickiness is enhanced through personalized service, and customer service cost is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent customer service, and specifically relates to an intelligent customer service system based on deep learning. Background Art

[0002] With the development of artificial intelligence technology, automated customer service systems have been widely used in all walks of life. However, traditional automated customer service systems cannot understand and adapt to customer emotional changes, resulting in poor service experience. Therefore, an intelligent customer service system that can understand customer emotions and adjust service strategies accordingly is particularly necessary.

[0003] Specifically, the shortcomings of traditional automatic customer service systems include:

[0004] 1. Lack of understanding of complex contexts

[0005] Semantic understanding limitations: Although intelligent customer service systems can handle a large number of common questions, they are still insufficient in their ability to understand complex or ambiguous contexts. The diversity and complexity of natural language make it difficult for AI to accurately capture customer intentions and needs, sometimes resulting in inaccurate answers or failure to meet customer expectations.

[0006] Difficulty in handling non-standard requests: When faced with special or innovative questions, the flexibility of intelligent customer service systems is limited. These questions may be beyond the scope of their training data sets, resulting in the system being unable to give effective answers or requiring transfer to human customer service.

[0007] 2. Limitations of Personalized Services

[0008] Templated answers: Although intelligent customer service can provide a certain degree of personalized service, their answers are usually generated based on preset scripts or algorithms, and therefore may lack the empathy and delicate emotional communication provided by human customer service.

[0009] Difficulty in handling complex emotions: It is difficult for intelligent customer service to understand and adapt to users’ emotional changes, especially when dealing with sensitive or emotional conversations, and it is unable to provide comfort or appropriate responses like humans. Summary of the invention

[0010] In view of the above problems, the present invention is proposed to provide an intelligent customer service system based on deep learning that overcomes the above problems or at least partially solves the above problems.

[0011] To achieve the above object, the present invention adopts the following technical solutions:

[0012] An intelligent customer service system based on deep learning, the system comprising:

[0013] The front-end receiving module is used to receive user input;

[0014] A speech processing module, which is used to pre-process the audio signal and convert the speech into text;

[0015] A natural language processing module for analyzing the text generated by the speech processing module;

[0016] The dialogue management module is used to maintain the conversation state and guide the conversation process;

[0017] Knowledge base and information retrieval module, used to provide structured database or knowledge graph;

[0018] A natural language generation module, which is used to convert the results of the dialogue management module into language responses;

[0019] The speech synthesis module is used to convert text into speech and output it.

[0020] Optionally, the system further comprises a backend service and integration module, wherein the backend service and integration module is used to store customer data, log records and analysis reports.

[0021] Optionally, the system further includes a feedback and learning module, which is used to collect user feedback and system performance indicators.

[0022] Optionally, the system further includes a security and privacy module, which is used to ensure the security of all interactive data and protect user personal information.

[0023] Optionally, the voice processing module includes:

[0024] Pre-processing unit for audio signal acquisition, noise reduction, audio enhancement and voice activity detection;

[0025] Speech recognition unit, which converts speech into text by using a deep learning model.

[0026] Optionally, the deep learning model includes a Connectionist Temporal Classification model and a Transformer model.

[0027] Optionally, the natural language processing module includes:

[0028] A text analysis unit, used to analyze the text generated by the speech processing module;

[0029] Intent recognition unit, used to understand the intent of the user's question or request;

[0030] Sentiment analysis unit uses deep learning models to analyze the emotional color in text.

[0031] Optionally, the dialogue management module includes:

[0032] The dialogue state tracking unit is used to maintain and update the context of the dialogue;

[0033] The decision-making unit generates the next action based on user intent and system strategy.

[0034] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0035] 1. The present invention uses a deep learning model, which has better context understanding capabilities, can handle complex contexts and ambiguous expressions, can combine speech recognition and sentiment analysis, and multimodal information can provide a more comprehensive understanding of user intent.

[0036] 2. The present invention can provide dynamic, personalized services and suggestions based on the user's historical interactions and real-time emotional state. The intelligent customer service system should have the ability to self-learn and optimize, and continuously adjust service strategies based on user feedback.

[0037] 3. The present invention realizes real-time monitoring and response to user emotions, improves the interactive experience, enhances user stickiness through personalized services, and reduces customer service costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic diagram of a deep learning-based intelligent customer service system flow provided in an embodiment of the present application;

[0039] Figure 2 A flow chart of an intelligent customer service system based on deep learning provided in an embodiment of the present application;

[0040] Figure 3 A timing diagram of an intelligent customer service system based on deep learning provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0042] See also Figure 1 , Figure 2 and Figure 3 This embodiment provides an intelligent customer service system based on deep learning, and the system includes:

[0043] The front-end receiving module is used to receive user input, including voice, text and other forms of interactive data.

[0044] The speech processing module is used to pre-process the audio signal and convert the speech into text.

[0045] The speech processing module includes:

[0046] Pre-processing unit for audio signal acquisition, noise reduction, audio enhancement, and voice activity detection.

[0047] Speech recognition unit, which converts speech into text by using a deep learning model.

[0048] The deep learning model includes a Connectionist Temporal Classification model and a Transformer model.

[0049] The natural language processing module is used to analyze the text generated by the speech processing module.

[0050] The natural language processing module includes:

[0051] The text analysis unit is used to analyze the text generated by the speech processing module.

[0052] Intent recognition unit, which is used to understand the intent of the question or request raised by the user.

[0053] Sentiment analysis unit uses deep learning models to analyze the emotional color in text.

[0054] The dialogue management module is used to maintain the session state and guide the dialogue process.

[0055] The dialogue management module includes:

[0056] The dialogue state tracking unit is used to maintain and update the context of the dialogue and understand the history of the dialogue.

[0057] The decision-making unit generates the next action based on user intent and system policy.

[0058] The knowledge base and information retrieval module is used to provide a structured database or knowledge graph to find and provide accurate information.

[0059] The natural language generation module is used to convert the results of the dialogue management module into natural and fluent language responses.

[0060] The speech synthesis module uses deep learning technology (such as waveform generation network WaveNet, Tacotron, etc.) to convert text into speech and output it.

[0061] Backend services and integration modules for storing customer data, logging, and analytical reporting.

[0062] Connect to other enterprise systems, such as CRM, ERP or other business logic, to obtain or update information. Provide API interface to allow interaction with other external services or applications.

[0063] The feedback and learning module is used to collect user feedback and system performance indicators to continuously improve and optimize the model.

[0064] Security and privacy module, used to ensure the security of all interactive data, comply with privacy regulations, and protect user personal information.

[0065] This embodiment uses a deep learning model, which has better context understanding capabilities, can handle complex contexts and ambiguous expressions, and can combine speech recognition and sentiment analysis. Multimodal information can provide a more comprehensive understanding of user intent.

[0066] This embodiment can provide dynamic, personalized services and suggestions based on the user's historical interactions and real-time emotional state. The intelligent customer service system should have the ability to self-learn and optimize, and continuously adjust service strategies based on user feedback.

[0067] This embodiment realizes real-time monitoring and response to user emotions, improves the interactive experience, enhances user stickiness through personalized services, and reduces customer service costs.

[0068] The specific steps for launching this system include:

[0069] S1. Data collection and preprocessing: Collect a large amount of labeled voice data and text data, and clean and label the data.

[0070] S2. Model training: Use deep learning frameworks such as TensorFlow and PyTorch to train each component.

[0071] S3. Integration testing: Integrate each module together for testing to ensure the consistency and correctness of the entire system.

[0072] S4. Deployment and launch: Deploy the system to a server or cloud platform and provide an API interface for external calls.

[0073] S5. Monitoring and maintenance: Continuously monitor system performance and collect user feedback for system optimization.

[0074] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. An intelligent customer service system based on deep learning, characterized in that: The system comprises: The front-end receiving module is used to receive user input; A speech processing module, which is used to pre-process the audio signal and convert the speech into text; A natural language processing module for analyzing the text generated by the speech processing module; The dialogue management module is used to maintain the conversation state and guide the conversation process; Knowledge base and information retrieval module, used to provide structured database or knowledge graph; A natural language generation module, which is used to convert the results of the dialogue management module into language responses; The speech synthesis module is used to convert text into speech and output it.

2. The deep learning-based intelligent customer service system according to claim 1, characterized in that: The system also includes a backend service and integration module for storing customer data, logging and analysis reports.

3. The deep learning-based intelligent customer service system according to claim 1, characterized in that: The system also includes a feedback and learning module, which is used to collect user feedback and system performance indicators.

4. The deep learning-based intelligent customer service system according to claim 1, characterized in that: The system also includes a security and privacy module, which is used to ensure the security of all interactive data and protect user personal information.

5. The deep learning-based intelligent customer service system according to claim 1, characterized in that: The speech processing module includes: Pre-processing unit for audio signal acquisition, noise reduction, audio enhancement and voice activity detection; Speech recognition unit, which converts speech into text by using a deep learning model.

6. The deep learning-based intelligent customer service system according to claim 5, characterized in that: The deep learning model includes a Connectionist Temporal Classification model and a Transformer model.

7. The deep learning-based intelligent customer service system according to claim 1, characterized in that: The natural language processing module includes: A text analysis unit, used to analyze the text generated by the speech processing module; Intent recognition unit, used to understand the intent of the user's question or request; Sentiment analysis unit uses deep learning models to analyze the emotional color in text.

8. The deep learning-based intelligent customer service system according to claim 1, characterized in that: The dialogue management module includes: The dialogue state tracking unit is used to maintain and update the context of the dialogue; The decision-making unit generates the next action based on user intent and system policy.

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