system

Abstract

The system according to this embodiment aims to reduce the mental stress on employees when handling customer complaints. [Solution] The system according to this embodiment comprises a reception unit, a response unit, and a learning unit. The reception unit receives complaints or inquiries from customers. The response unit responds to the complaints or inquiries received by the reception unit. The learning unit learns from the experience of customer interactions conducted by the response unit and improves the accuracy of responses.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a risk that employees in customer service may suffer mental stress in handling claims from customers.

[0005] The system according to the embodiment aims to reduce the mental stress of employees in handling claims from customers.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a response unit, and a learning unit. The reception unit receives claims or inquiries from customers. The response unit responds to the claims or inquiries received by the reception unit. The learning unit learns the experience of the interaction with customers performed by the response unit and improves the accuracy of the response. [Effects of the Invention]

[0007] The system according to this embodiment can reduce the mental stress on employees when handling customer complaints. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52. <The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The customer service system according to an embodiment of the present invention is a system that efficiently and accurately responds to customer complaints and inquiries. This customer service system has the advantage of being able to address questions, troubles, and issues regarding products and services by directly interacting with customers and making fine adjustments, while also providing a mechanism to reduce the mental stress caused by excessive complaints. Specifically, a dedicated complaint handling window is established, and complex inquiries and complaints are directed to this dedicated window, where an AI agent handles them, enabling customers to respond to inquiries without stress. The AI ​​agent can be pre-trained with complaint patterns and answers, and further learns from experience interacting with customers, thereby improving the accuracy of its responses. For example, if a customer inquires about a product defect, the AI ​​agent recognizes the pattern of the defect and provides an appropriate answer. Next, the AI ​​agent learns from experience interacting with customers and improves the accuracy of its responses. For example, if a customer repeatedly inquires about a particular problem, the AI ​​agent learns the optimal answer to that problem and can respond quickly and appropriately to subsequent inquiries. Furthermore, the AI ​​agent analyzes the customer's emotions and provides empathetic responses. For example, if a customer expresses anger or dissatisfaction, the AI ​​agent can analyze those emotions and soothe the customer with empathetic words. This allows customers to experience quick problem resolution and reduce stress. This system also frees customer service employees from the mental stress of excessive complaints, enabling more efficient and higher-quality customer service. Furthermore, the AI ​​agent can analyze customer requests and use the findings to improve services. For example, by identifying areas for product and service improvement based on customer feedback and reporting them to the company, service quality can be enhanced. In this way, utilizing the AI ​​agent streamlines customer service and reduces employee workload, creating a comfortable environment for both customers and employees. As a result, the customer service system can respond to customer complaints and inquiries efficiently and accurately.

[0029] The customer service system according to this embodiment comprises a reception unit, a response unit, and a learning unit. The reception unit receives complaints or inquiries from customers. The reception unit can receive complaints and inquiries through multiple channels, such as telephone, email, and chat. The reception unit quickly receives customer complaints and inquiries and distributes them to the appropriate response unit. For example, the reception unit can use an algorithm to analyze the content of customer complaints and inquiries and distribute them to the appropriate response unit. The response unit responds to complaints or inquiries received by the reception unit. The response unit provides appropriate answers to customer complaints and inquiries, for example, using an AI agent. The AI ​​agent learns complaint patterns and answers, and improves the accuracy of its responses based on its experience interacting with customers. For example, the AI ​​agent can learn data from past complaints and inquiries and provide optimal answers. The learning unit learns the experience of customer interactions conducted by the response unit and improves the accuracy of its responses. The learning unit can use a machine learning algorithm, for example, to analyze data from customer interactions and improve the accuracy of its responses. For example, the learning unit can use an algorithm to improve the accuracy of the AI ​​agent's responses based on customer feedback. As a result, the customer service system according to this embodiment can respond to customer complaints and inquiries efficiently and accurately.

[0030] The reception department receives customer complaints or inquiries. The reception department can receive complaints and inquiries through multiple channels, such as telephone, email, and chat. Specifically, telephone inquiries utilize an automated voice response system that connects customers to the appropriate representative based on their selections. Email inquiries use a dedicated email address and a system that automatically categorizes and organizes received emails. Chat inquiries are handled by a chatbot deployed on the website and mobile app, allowing customers to make inquiries in real time. Complaints and inquiries received through these channels are centrally aggregated in the reception department's system. The reception department quickly receives customer complaints and inquiries and routes them to the appropriate department. For example, the reception department can use an algorithm to analyze the content of customer complaints and inquiries and route them to the appropriate department. This algorithm uses natural language processing technology to analyze customer statements and texts to determine the type and urgency of the complaint. For example, complaints about product defects are routed to the technical support department, and inquiries about delivery delays are routed to the logistics department. Furthermore, the reception department can refer to the customer's past inquiry and purchase history to provide more personalized service. This allows the reception department to efficiently handle customer complaints and inquiries, enabling them to provide prompt and appropriate responses.

[0031] The customer support department handles complaints and inquiries received by the reception department. For example, the customer support department uses AI agents to provide appropriate answers to customer complaints and inquiries. The AI ​​agents learn complaint patterns and responses, improving their accuracy based on their experience interacting with customers. Specifically, the AI ​​agents use natural language processing technology to understand customer inquiries and search for the most suitable answers from a past database. For example, for inquiries about product usage, they extract relevant information from manuals and FAQs and provide it to the customer. Furthermore, the AI ​​agents can analyze customer emotions and respond in an appropriate tone. For instance, if a customer is angry, they will include an apology in their response to alleviate the customer's dissatisfaction. In addition, the AI ​​agents save the history of customer interactions and refer to it for future inquiries, enabling more consistent responses. This allows the customer support department to respond quickly and accurately to customer complaints and inquiries, improving customer satisfaction.

[0032] The learning unit learns from the customer interactions conducted by the response unit and improves the accuracy of responses. For example, the learning unit uses machine learning algorithms to analyze data from customer interactions and improve the accuracy of responses. Specifically, the learning unit can use algorithms to improve the accuracy of AI agent responses based on customer feedback. For example, it can collect customer satisfaction ratings for the responses provided and learn response patterns that yield high satisfaction. The learning unit can also analyze customer inquiries and complaint trends to identify new trends and problems. This allows the AI ​​agent to provide responses based on the latest information and trends. Furthermore, the learning unit can analyze the response methods and scripts of the response unit operators to derive optimal response methods. For example, it can extract responses from operators that resulted in high customer satisfaction and share those response methods with other operators. This allows the learning unit to improve the overall response quality of the response unit. As a result, the customer service system according to this embodiment can respond to customer complaints and inquiries efficiently and accurately.

[0033] The response unit can learn complaint patterns and responses. For example, the response unit can classify complaint patterns and learn appropriate responses for each pattern. For example, the response unit can classify frequent complaints or complaints related to specific products and learn standard responses for each. The response unit can also provide customized responses based on complaint patterns. For example, the response unit can provide customized responses based on complaint patterns according to the customer's situation and needs. This improves the accuracy of responses by learning complaint patterns and responses. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the response unit can input complaint patterns and responses into a generative AI, which can then generate the optimal response.

[0034] The learning unit can learn from customer interaction experiences and improve the accuracy of its responses. For example, the learning unit collects data from past customer interactions and learns using machine learning algorithms. For example, the learning unit analyzes customer feedback and interaction history to improve the accuracy of the AI ​​agent's responses. The learning unit can also optimize the AI ​​agent's response algorithm based on customer interaction experience. For example, the learning unit analyzes patterns of customer complaints and inquiries and selects the optimal response algorithm. This improves the accuracy of responses by learning from customer interaction experiences. Some or all of the above processes in the learning unit may be performed using, for example, generative AI, or not using generative AI. For example, the learning unit can input data from past customer interactions into a generative AI, which can then generate the optimal response algorithm.

[0035] The response unit may include a requirements analysis unit that analyzes customer requests and uses the results to improve services. For example, the response unit may use a requirements analysis algorithm to analyze customer requests. For example, the response unit may classify customer feedback and requests and identify areas for service improvement. The response unit may also propose service improvement measures based on customer requests. For example, the response unit may propose new services or features based on customer requests. By analyzing customer requests and using the results to improve services, the quality of services is enhanced. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the response unit may input customer request data into a generative AI, which can then identify areas for service improvement.

[0036] The reception department can analyze a customer's past inquiry history and select the most suitable reception method. For example, the reception department can use a history analysis algorithm to analyze a customer's past inquiry history. For example, the reception department can prioritize inquiries about topics that the customer has frequently inquired about in the past. The reception department can also prioritize suggesting reception methods (telephone, email, etc.) that the customer has used in the past. Furthermore, the reception department can select a reception method that corresponds to a specific time period based on the customer's past inquiry history. In this way, the reception department can select the most suitable reception method by analyzing the customer's past inquiry history. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception department can input the customer's past inquiry history data into a generative AI, which can then select the most suitable reception method.

[0037] The reception department can filter complaints and inquiries based on the customer's current situation and areas of interest. For example, if the reception department receives the customer's current situation, it will suggest a suitable method of handling the complaint or inquiry. The reception department can also prioritize complaints and inquiries related to the customer's areas of interest. Furthermore, the reception department can assign complaints and inquiries to the appropriate personnel based on the customer's current situation and areas of interest. This allows for more appropriate responses by filtering based on the customer's current situation and areas of interest. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or not. For example, the reception department can input data on the customer's current situation and areas of interest into a generative AI, which can then perform optimal filtering.

[0038] The reception department can prioritize receiving complaints and inquiries by considering the customer's geographical location. For example, if a customer is in a specific region, the reception department will prioritize complaints and inquiries related to that region. The reception department can also route customers to the nearest service center based on their geographical location. Furthermore, the reception department can address region-specific issues by considering the customer's geographical location. This allows for the priority of receiving highly relevant information by considering the customer's geographical location. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or not. For example, the reception department can input customer geographical location data into a generative AI, which can then prioritize receiving highly relevant information.

[0039] The reception department can analyze a customer's social media activity when receiving complaints or inquiries and receive relevant information. For example, the reception department can use a social media analysis algorithm to analyze a customer's social media activity. For example, the reception department can analyze a customer's recent interests from their social media activity and prioritize receiving relevant complaints and inquiries. The reception department can also suggest appropriate responses based on the customer's social media activity. Furthermore, the reception department can analyze a customer's social media activity and provide relevant information. In this way, by analyzing a customer's social media activity, it can receive relevant information. Some or all of the above processing in the reception department may be performed using, for example, generative AI, or not using generative AI. For example, the reception department can input customer social media activity data into a generative AI, and the generative AI can receive relevant information.

[0040] The response unit can adjust the level of detail in its response based on the importance of the complaint or inquiry. For example, the response unit uses an importance assessment algorithm to evaluate the importance of the complaint or inquiry. For example, the response unit evaluates the magnitude and urgency of the impact of the complaint or inquiry and determines its importance. The response unit also adjusts the level of detail in its response based on the importance. For example, the response unit provides detailed explanations and solutions for high-importance complaints or inquiries. The response unit can also respond to complaints or inquiries of moderate importance with an appropriate level of detail. This allows for an appropriate response by adjusting the level of detail in the response based on the importance of the complaint or inquiry. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the response unit can input complaint and inquiry importance data into a generative AI, and the generative AI can adjust the level of detail in its response.

[0041] The response unit can apply different response algorithms depending on the category of the complaint or inquiry during the response process. For example, the response unit can use a category classification algorithm to categorize complaints and inquiries. For instance, the response unit can apply a technical response algorithm to complaints about product defects. It can also apply a service improvement response algorithm to complaints about service quality. Furthermore, it can apply a logistics-related response algorithm to complaints about delivery. By applying different response algorithms depending on the category of the complaint or inquiry, appropriate responses become possible. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the response unit can input complaint and inquiry category data into a generative AI, which can then apply the most suitable response algorithm.

[0042] The response unit can determine the priority of responses based on when the complaint or inquiry was submitted. For example, the response unit can use a timing evaluation algorithm to assess the submission timing of complaints and inquiries. For example, the response unit can prioritize recently submitted complaints and inquiries. The response unit can also set an appropriate priority for older complaints and inquiries. Furthermore, the response unit can set a low priority for very old complaints and inquiries. This enables a quick and appropriate response by determining the priority of responses based on the submission timing of complaints and inquiries. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the response unit can input complaint and inquiry submission timing data into a generative AI, which can then determine the priority of responses.

[0043] The response unit can adjust the order of responses based on the relevance of complaints and inquiries during the response process. For example, the response unit may use a relevance evaluation algorithm to assess the relevance of complaints and inquiries. For instance, the response unit may prioritize handling customer complaints and inquiries that are related to other complaints and inquiries. It can also handle less relevant complaints and inquiries in the normal order. Furthermore, it can respond quickly to highly relevant complaints and inquiries. This allows for appropriate responses by adjusting the order of responses based on the relevance of complaints and inquiries. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the response unit may input relevance data of complaints and inquiries into a generative AI, which can then adjust the order of responses.

[0044] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit uses a data referencing algorithm to refer to past learning data. For example, the learning unit selects the optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and adjust the parameters of the learning algorithm. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. As a result, the accuracy of learning is improved by optimizing the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input past learning data into a generative AI, and the generative AI can optimize the learning algorithm.

[0045] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0046] The customer service system can further analyze a customer's past purchase history and optimize responses to inquiries. For example, the support department can prioritize relevant complaints and inquiries based on products and services the customer has previously purchased. It can also analyze the customer's usage of previously purchased products to provide appropriate advice and support. Furthermore, the support department can predict future needs based on the customer's past purchase history and provide proactive support. This allows for more personalized service by leveraging the customer's past purchase history.

[0047] The customer service system can further analyze customers' social media activity and optimize responses to inquiries. For example, the support department can analyze what customers post on social media and prioritize relevant complaints and inquiries. The support department can also provide appropriate advice and support based on feedback shared by customers on social media. Furthermore, the support department can predict future needs based on customers' social media activity and provide proactive support. This allows for more personalized responses by leveraging customers' social media activity.

[0048] The customer service system can further optimize responses to inquiries by utilizing the customer's geographical location information. For example, if a customer is in a specific region, the support department can prioritize complaints and inquiries related to that region. The support department can also route customers to the nearest service center based on their geographical location. Furthermore, the support department can address region-specific issues by considering the customer's geographical location. This allows for more appropriate responses by leveraging the customer's geographical location information.

[0049] The customer service system can further analyze a customer's past inquiry history and select the most appropriate method of handling inquiries. For example, the reception department can use a history analysis algorithm to analyze a customer's past inquiry history. For instance, the reception department can prioritize inquiries about topics that customers have frequently inquired about in the past. It can also prioritize suggesting methods of handling inquiries that customers have used in the past (e.g., phone, email). Furthermore, the reception department can select a method of handling inquiries appropriate for specific time periods based on the customer's past inquiry history. In this way, the optimal method of handling inquiries can be selected by analyzing a customer's past inquiry history.

[0050] The customer service system can also prioritize receiving inquiries that are highly relevant by considering the customer's geographical location. For example, if a customer is in a specific region, the reception desk will prioritize complaints and inquiries related to that region. The reception desk can also route customers to the nearest service center based on their geographical location. Furthermore, the reception desk can address region-specific issues by considering the customer's geographical location. In short, by considering the customer's geographical location, the system can prioritize receiving inquiries that are highly relevant.

[0051] The following briefly describes the processing flow for example form 1.

[0052] Step 1: The reception department receives customer complaints or inquiries. The reception department can receive complaints and inquiries through multiple channels, such as phone, email, and chat. The reception department quickly receives customer complaints and inquiries and routes them to the appropriate department. For example, the reception department can use an algorithm to analyze the content of customer complaints and inquiries and route them to the appropriate department. Step 2: The support department handles complaints or inquiries received by the reception department. The support department uses an AI agent to provide appropriate answers to customer complaints and inquiries. The AI ​​agent learns complaint patterns and answers, and improves the accuracy of its responses based on its experience interacting with customers. For example, the AI ​​agent can learn from past complaint and inquiry data to provide the best possible answers. Step 3: The learning unit learns from the customer interactions conducted by the response unit and improves the accuracy of its responses. The learning unit uses machine learning algorithms to analyze data from customer interactions and improve the accuracy of its responses. For example, the learning unit can use algorithms to improve the accuracy of the AI ​​agent's responses based on customer feedback.

[0053] (Example of form 2) The customer service system according to an embodiment of the present invention is a system that efficiently and accurately responds to customer complaints and inquiries. This customer service system has the advantage of being able to address questions, troubles, and issues regarding products and services by directly interacting with customers and making fine adjustments, while also providing a mechanism to reduce the mental stress caused by excessive complaints. Specifically, a dedicated complaint handling window is established, and complex inquiries and complaints are directed to this dedicated window, where an AI agent handles them, enabling customers to respond to inquiries without stress. The AI ​​agent can be pre-trained with complaint patterns and answers, and further learns from experience interacting with customers, thereby improving the accuracy of its responses. For example, if a customer inquires about a product defect, the AI ​​agent recognizes the pattern of the defect and provides an appropriate answer. Next, the AI ​​agent learns from experience interacting with customers and improves the accuracy of its responses. For example, if a customer repeatedly inquires about a particular problem, the AI ​​agent learns the optimal answer to that problem and can respond quickly and appropriately to subsequent inquiries. Furthermore, the AI ​​agent analyzes the customer's emotions and provides empathetic responses. For example, if a customer expresses anger or dissatisfaction, the AI ​​agent can analyze those emotions and soothe the customer with empathetic words. This allows customers to experience quick problem resolution and reduce stress. This system also frees customer service employees from the mental stress of excessive complaints, enabling more efficient and higher-quality customer service. Furthermore, the AI ​​agent can analyze customer requests and use the findings to improve services. For example, by identifying areas for product and service improvement based on customer feedback and reporting them to the company, service quality can be enhanced. In this way, utilizing the AI ​​agent streamlines customer service and reduces employee workload, creating a comfortable environment for both customers and employees. As a result, the customer service system can respond to customer complaints and inquiries efficiently and accurately.

[0054] The customer service system according to this embodiment comprises a reception unit, a response unit, and a learning unit. The reception unit receives complaints or inquiries from customers. The reception unit can receive complaints and inquiries through multiple channels, such as telephone, email, and chat. The reception unit quickly receives customer complaints and inquiries and distributes them to the appropriate response unit. For example, the reception unit can use an algorithm to analyze the content of customer complaints and inquiries and distribute them to the appropriate response unit. The response unit responds to complaints or inquiries received by the reception unit. The response unit provides appropriate answers to customer complaints and inquiries, for example, using an AI agent. The AI ​​agent learns complaint patterns and answers, and improves the accuracy of its responses based on its experience interacting with customers. For example, the AI ​​agent can learn data from past complaints and inquiries and provide optimal answers. The learning unit learns the experience of customer interactions conducted by the response unit and improves the accuracy of its responses. The learning unit can use a machine learning algorithm, for example, to analyze data from customer interactions and improve the accuracy of its responses. For example, the learning unit can use an algorithm to improve the accuracy of the AI ​​agent's responses based on customer feedback. As a result, the customer service system according to this embodiment can respond to customer complaints and inquiries efficiently and accurately.

[0055] The reception department receives customer complaints or inquiries. The reception department can receive complaints and inquiries through multiple channels, such as telephone, email, and chat. Specifically, telephone inquiries utilize an automated voice response system that connects customers to the appropriate representative based on their selections. Email inquiries use a dedicated email address and a system that automatically categorizes and organizes received emails. Chat inquiries are handled by a chatbot deployed on the website and mobile app, allowing customers to make inquiries in real time. Complaints and inquiries received through these channels are centrally aggregated in the reception department's system. The reception department quickly receives customer complaints and inquiries and routes them to the appropriate department. For example, the reception department can use an algorithm to analyze the content of customer complaints and inquiries and route them to the appropriate department. This algorithm uses natural language processing technology to analyze customer statements and texts to determine the type and urgency of the complaint. For example, complaints about product defects are routed to the technical support department, and inquiries about delivery delays are routed to the logistics department. Furthermore, the reception department can refer to the customer's past inquiry and purchase history to provide more personalized service. This allows the reception department to efficiently handle customer complaints and inquiries, enabling them to provide prompt and appropriate responses.

[0056] The customer support department handles complaints and inquiries received by the reception department. For example, the customer support department uses AI agents to provide appropriate answers to customer complaints and inquiries. The AI ​​agents learn complaint patterns and responses, improving their accuracy based on their experience interacting with customers. Specifically, the AI ​​agents use natural language processing technology to understand customer inquiries and search for the most suitable answers from a past database. For example, for inquiries about product usage, they extract relevant information from manuals and FAQs and provide it to the customer. Furthermore, the AI ​​agents can analyze customer emotions and respond in an appropriate tone. For instance, if a customer is angry, they will include an apology in their response to alleviate the customer's dissatisfaction. In addition, the AI ​​agents save the history of customer interactions and refer to it for future inquiries, enabling more consistent responses. This allows the customer support department to respond quickly and accurately to customer complaints and inquiries, improving customer satisfaction.

[0057] The learning unit learns from the customer interactions conducted by the response unit and improves the accuracy of responses. For example, the learning unit uses machine learning algorithms to analyze data from customer interactions and improve the accuracy of responses. Specifically, the learning unit can use algorithms to improve the accuracy of AI agent responses based on customer feedback. For example, it can collect customer satisfaction ratings for the responses provided and learn response patterns that yield high satisfaction. The learning unit can also analyze customer inquiries and complaint trends to identify new trends and problems. This allows the AI ​​agent to provide responses based on the latest information and trends. Furthermore, the learning unit can analyze the response methods and scripts of the response unit operators to derive optimal response methods. For example, it can extract responses from operators that resulted in high customer satisfaction and share those response methods with other operators. This allows the learning unit to improve the overall response quality of the response unit. As a result, the customer service system according to this embodiment can respond to customer complaints and inquiries efficiently and accurately.

[0058] The response unit can learn complaint patterns and responses. For example, the response unit can classify complaint patterns and learn appropriate responses for each pattern. For example, the response unit can classify frequent complaints or complaints related to specific products and learn standard responses for each. The response unit can also provide customized responses based on complaint patterns. For example, the response unit can provide customized responses based on complaint patterns according to the customer's situation and needs. This improves the accuracy of responses by learning complaint patterns and responses. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the response unit can input complaint patterns and responses into a generative AI, which can then generate the optimal response.

[0059] The response unit may include an emotion analysis unit that analyzes customer emotions and provides empathetic responses. For example, the response unit may use an emotion analysis algorithm to analyze customer emotions. For example, the response unit may analyze the customer's tone of voice and word choice to estimate the customer's emotions. The response unit may also provide empathetic responses according to the customer's emotions. For example, if the customer is angry, the response unit may provide a calm and polite response. If the customer is anxious, the response unit may provide a reassuring response. By analyzing customer emotions and providing empathetic responses, customer satisfaction is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the response unit may input customer voice data into a generative AI, which can then estimate the customer's emotions.

[0060] The learning unit can learn from customer interaction experiences and improve the accuracy of its responses. For example, the learning unit collects data from past customer interactions and learns using machine learning algorithms. For example, the learning unit analyzes customer feedback and interaction history to improve the accuracy of the AI ​​agent's responses. The learning unit can also optimize the AI ​​agent's response algorithm based on customer interaction experience. For example, the learning unit analyzes patterns of customer complaints and inquiries and selects the optimal response algorithm. This improves the accuracy of responses by learning from customer interaction experiences. Some or all of the above processes in the learning unit may be performed using, for example, generative AI, or not using generative AI. For example, the learning unit can input data from past customer interactions into a generative AI, which can then generate the optimal response algorithm.

[0061] The sentiment analysis unit can analyze customer emotions and soothe them with empathetic language. For example, the sentiment analysis unit uses an emotion analysis algorithm to analyze customer emotions. For example, the sentiment analysis unit analyzes the customer's tone of voice and word choice to estimate their emotions. The sentiment analysis unit also soothes customers with empathetic language according to their emotions. For example, if the customer is angry, the sentiment analysis unit soothes them with calm and polite language. If the customer is anxious, the sentiment analysis unit can soothe them with reassuring language. In this way, customer dissatisfaction is reduced by analyzing customer emotions and soothing them with empathetic language. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the sentiment analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the emotion analysis unit inputs customer voice data into a generating AI, which can then estimate the customer's emotions.

[0062] The response unit may include a requirements analysis unit that analyzes customer requests and uses the results to improve services. For example, the response unit may use a requirements analysis algorithm to analyze customer requests. For example, the response unit may classify customer feedback and requests and identify areas for service improvement. The response unit may also propose service improvement measures based on customer requests. For example, the response unit may propose new services or features based on customer requests. By analyzing customer requests and using the results to improve services, the quality of services is enhanced. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the response unit may input customer request data into a generative AI, which can then identify areas for service improvement.

[0063] The reception desk can estimate the customer's emotions and adjust the way complaints and inquiries are handled based on the estimated emotions. For example, the reception desk may use an emotion estimation algorithm to estimate the customer's emotions. For example, the reception desk may analyze the customer's tone of voice and word choice to estimate their emotions. The reception desk will also adjust the way complaints and inquiries are handled based on the estimated emotions. For example, if the reception desk perceives a customer as angry, it will prioritize handling their inquiry in order to respond quickly. If the reception desk perceives a customer as anxious, it can provide a thorough explanation to reassure them. By adjusting the handling method according to the customer's emotions, a more appropriate response becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception desk may be performed using, for example, generative AI, or without generative AI. For example, the reception desk can input customer voice data into a generating AI, which can then estimate the customer's emotions.

[0064] The reception department can analyze a customer's past inquiry history and select the most suitable reception method. For example, the reception department can use a history analysis algorithm to analyze a customer's past inquiry history. For example, the reception department can prioritize inquiries about topics that the customer has frequently inquired about in the past. The reception department can also prioritize suggesting reception methods (telephone, email, etc.) that the customer has used in the past. Furthermore, the reception department can select a reception method that corresponds to a specific time period based on the customer's past inquiry history. In this way, the reception department can select the most suitable reception method by analyzing the customer's past inquiry history. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception department can input the customer's past inquiry history data into a generative AI, which can then select the most suitable reception method.

[0065] The reception department can filter complaints and inquiries based on the customer's current situation and areas of interest. For example, if the reception department receives the customer's current situation, it will suggest a suitable method of handling the complaint or inquiry. The reception department can also prioritize complaints and inquiries related to the customer's areas of interest. Furthermore, the reception department can assign complaints and inquiries to the appropriate personnel based on the customer's current situation and areas of interest. This allows for more appropriate responses by filtering based on the customer's current situation and areas of interest. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or not. For example, the reception department can input data on the customer's current situation and areas of interest into a generative AI, which can then perform optimal filtering.

[0066] The reception desk can estimate the customer's emotions and determine the priority of complaints and inquiries based on the estimated emotions. For example, the reception desk may use an emotion estimation algorithm to estimate the customer's emotions. For example, the reception desk may analyze the customer's tone of voice and word choice to estimate their emotions. The reception desk then determines the priority of complaints and inquiries based on the estimated emotions. For example, if the reception desk is angry, it may set a high priority to address the customer's situation promptly. If the customer is anxious, it may set a medium priority to address the situation quickly. This allows for quick and appropriate responses by determining priorities according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or not using generative AI. For example, the reception desk may input customer voice data into a generative AI, which can then estimate the customer's emotions.

[0067] The reception department can prioritize receiving complaints and inquiries by considering the customer's geographical location. For example, if a customer is in a specific region, the reception department will prioritize complaints and inquiries related to that region. The reception department can also route customers to the nearest service center based on their geographical location. Furthermore, the reception department can address region-specific issues by considering the customer's geographical location. This allows for the priority of receiving highly relevant information by considering the customer's geographical location. Some or all of the above processing in the reception department may be performed using, for example, a generative AI, or not. For example, the reception department can input customer geographical location data into a generative AI, which can then prioritize receiving highly relevant information.

[0068] The reception department can analyze a customer's social media activity when receiving complaints or inquiries and receive relevant information. For example, the reception department can use a social media analysis algorithm to analyze a customer's social media activity. For example, the reception department can analyze a customer's recent interests from their social media activity and prioritize receiving relevant complaints and inquiries. The reception department can also suggest appropriate responses based on the customer's social media activity. Furthermore, the reception department can analyze a customer's social media activity and provide relevant information. In this way, by analyzing a customer's social media activity, it can receive relevant information. Some or all of the above processing in the reception department may be performed using, for example, generative AI, or not using generative AI. For example, the reception department can input customer social media activity data into a generative AI, and the generative AI can receive relevant information.

[0069] The response unit can estimate the customer's emotions and adjust the way it responds based on the estimated emotions. For example, the response unit may use an emotion estimation algorithm to estimate the customer's emotions. For example, the response unit may analyze the customer's tone of voice and word choice to estimate the customer's emotions. The response unit may also adjust the way it responds based on the estimated emotions. For example, if the customer is angry, the response unit may use a calm and polite way of responding. If the customer is anxious, the response unit may use a reassuring way of responding. By adjusting the way it responds according to the customer's emotions, a more appropriate response becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the response unit may be performed using a generative AI, or not using a generative AI. For example, the response unit may input customer voice data into a generative AI, which can estimate the customer's emotions.

[0070] The response unit can adjust the level of detail in its response based on the importance of the complaint or inquiry. For example, the response unit uses an importance assessment algorithm to evaluate the importance of the complaint or inquiry. For example, the response unit evaluates the magnitude and urgency of the impact of the complaint or inquiry and determines its importance. The response unit also adjusts the level of detail in its response based on the importance. For example, the response unit provides detailed explanations and solutions for high-importance complaints or inquiries. The response unit can also respond to complaints or inquiries of moderate importance with an appropriate level of detail. This allows for an appropriate response by adjusting the level of detail in the response based on the importance of the complaint or inquiry. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the response unit can input complaint and inquiry importance data into a generative AI, and the generative AI can adjust the level of detail in its response.

[0071] The response unit can apply different response algorithms depending on the category of the complaint or inquiry during the response process. For example, the response unit can use a category classification algorithm to categorize complaints and inquiries. For instance, the response unit can apply a technical response algorithm to complaints about product defects. It can also apply a service improvement response algorithm to complaints about service quality. Furthermore, it can apply a logistics-related response algorithm to complaints about delivery. By applying different response algorithms depending on the category of the complaint or inquiry, appropriate responses become possible. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the response unit can input complaint and inquiry category data into a generative AI, which can then apply the most suitable response algorithm.

[0072] The response unit can estimate the customer's emotions and adjust the length of the response based on the estimated emotions. For example, the response unit may use an emotion estimation algorithm to estimate the customer's emotions. For example, the response unit may analyze the customer's tone of voice and word choice to estimate the customer's emotions. The response unit may also adjust the length of the response based on the estimated emotions. For example, if the customer is angry, the response unit may respond quickly and resolve the issue in a short time. Conversely, if the customer is anxious, the response unit may explain carefully and take its time to respond. By adjusting the length of the response according to the customer's emotions, a more appropriate response becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the response unit may input customer voice data into a generative AI, which can then estimate the customer's emotions.

[0073] The response unit can determine the priority of responses based on when the complaint or inquiry was submitted. For example, the response unit can use a timing evaluation algorithm to assess the submission timing of complaints and inquiries. For example, the response unit can prioritize recently submitted complaints and inquiries. The response unit can also set an appropriate priority for older complaints and inquiries. Furthermore, the response unit can set a low priority for very old complaints and inquiries. This enables a quick and appropriate response by determining the priority of responses based on the submission timing of complaints and inquiries. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the response unit can input complaint and inquiry submission timing data into a generative AI, which can then determine the priority of responses.

[0074] The response unit can adjust the order of responses based on the relevance of complaints and inquiries during the response process. For example, the response unit may use a relevance evaluation algorithm to assess the relevance of complaints and inquiries. For instance, the response unit may prioritize handling customer complaints and inquiries that are related to other complaints and inquiries. It can also handle less relevant complaints and inquiries in the normal order. Furthermore, it can respond quickly to highly relevant complaints and inquiries. This allows for appropriate responses by adjusting the order of responses based on the relevance of complaints and inquiries. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or without a generative AI. For example, the response unit may input relevance data of complaints and inquiries into a generative AI, which can then adjust the order of responses.

[0075] The learning unit can estimate customer emotions and select training data based on the estimated customer emotions. For example, the learning unit uses an emotion estimation algorithm to estimate customer emotions. For example, the learning unit analyzes the customer's tone of voice and word choice to estimate customer emotions. The learning unit also selects training data based on the estimated customer emotions. For example, if the customer is angry, the learning unit will prioritize selecting training data related to that emotion. Similarly, if the customer is anxious, the learning unit can select training data related to that emotion. This improves the accuracy of learning by selecting training data based on customer emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the learning unit may be performed using a generative AI, or not using a generative AI. For example, the learning unit can input customer voice data into a generative AI, which can then estimate the customer's emotions.

[0076] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit uses a data referencing algorithm to refer to past learning data. For example, the learning unit selects the optimal learning algorithm based on past learning data. The learning unit can also analyze past learning data and adjust the parameters of the learning algorithm. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. As a result, the accuracy of learning is improved by optimizing the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input past learning data into a generative AI, and the generative AI can optimize the learning algorithm.

[0077] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0078] The customer service system can further analyze a customer's past purchase history and optimize responses to inquiries. For example, the support department can prioritize relevant complaints and inquiries based on products and services the customer has previously purchased. It can also analyze the customer's usage of previously purchased products to provide appropriate advice and support. Furthermore, the support department can predict future needs based on the customer's past purchase history and provide proactive support. This allows for more personalized service by leveraging the customer's past purchase history.

[0079] The customer service system can further analyze customers' social media activity and optimize responses to inquiries. For example, the support department can analyze what customers post on social media and prioritize relevant complaints and inquiries. The support department can also provide appropriate advice and support based on feedback shared by customers on social media. Furthermore, the support department can predict future needs based on customers' social media activity and provide proactive support. This allows for more personalized responses by leveraging customers' social media activity.

[0080] The customer service system can further optimize responses to inquiries by utilizing the customer's geographical location information. For example, if a customer is in a specific region, the support department can prioritize complaints and inquiries related to that region. The support department can also route customers to the nearest service center based on their geographical location. Furthermore, the support department can address region-specific issues by considering the customer's geographical location. This allows for more appropriate responses by leveraging the customer's geographical location information.

[0081] The customer service system can further estimate the customer's emotions and adjust the way it responds based on those emotions. For example, the response unit analyzes the customer's tone of voice and word choice to estimate their emotions. The response unit then adjusts the way it responds based on those emotions. For instance, if a customer is angry, it can use a calm and polite approach. If a customer is anxious, it can use a reassuring approach. This allows for more appropriate responses by adjusting the response style according to the customer's emotions.

[0082] The customer service system can further estimate the customer's emotions and adjust the length of the response based on those emotions. For example, the response unit analyzes the customer's tone of voice and word choice to estimate their emotions. The response unit then adjusts the length of the response based on the estimated emotions. For instance, if a customer is angry, the system can respond quickly and resolve the issue in a short time. Conversely, if a customer is anxious, the system can provide a thorough explanation and take more time to address the issue. This allows for more appropriate responses by adjusting the length of the response according to the customer's emotions.

[0083] The customer service system can further analyze a customer's past inquiry history and select the most appropriate method of handling inquiries. For example, the reception department can use a history analysis algorithm to analyze a customer's past inquiry history. For instance, the reception department can prioritize inquiries about topics that customers have frequently inquired about in the past. It can also prioritize suggesting methods of handling inquiries that customers have used in the past (e.g., phone, email). Furthermore, the reception department can select a method of handling inquiries appropriate for specific time periods based on the customer's past inquiry history. In this way, the optimal method of handling inquiries can be selected by analyzing a customer's past inquiry history.

[0084] The customer service system can further estimate the customer's emotions and adjust how complaints and inquiries are handled based on those estimated emotions. For example, the reception desk can analyze the customer's tone of voice and word choice to estimate their emotions. Based on these estimated emotions, the reception desk can then adjust how complaints and inquiries are handled. For instance, if a customer is angry, their complaint will be given priority for a quicker response. If a customer is anxious, a more thorough explanation can be provided to reassure them. This allows for more appropriate responses by adjusting the handling process according to the customer's emotions.

[0085] The customer service system can further estimate the customer's emotions and prioritize complaints and inquiries based on those estimated emotions. For example, the reception desk can analyze the customer's tone of voice and word choice to estimate their emotions. Based on these estimated emotions, the reception desk can then prioritize complaints and inquiries. For instance, if a customer is angry, a high priority can be set to address their issue promptly. If a customer is anxious, a medium priority can be set to address their issue quickly. This allows for quick and appropriate responses by prioritizing according to the customer's emotions.

[0086] The customer service system can further estimate customer emotions and select training data based on those estimated emotions. For example, the learning unit analyzes the customer's tone of voice and word choice to estimate their emotions. The learning unit then selects training data based on the estimated emotions. For instance, if a customer is angry, it prioritizes selecting training data related to that emotion. Similarly, if a customer is anxious, it can select training data related to that emotion. By selecting training data based on customer emotions, the accuracy of the learning process is improved.

[0087] The customer service system can also prioritize receiving inquiries that are highly relevant by considering the customer's geographical location. For example, if a customer is in a specific region, the reception desk will prioritize complaints and inquiries related to that region. The reception desk can also route customers to the nearest service center based on their geographical location. Furthermore, the reception desk can address region-specific issues by considering the customer's geographical location. In short, by considering the customer's geographical location, the system can prioritize receiving inquiries that are highly relevant.

[0088] The following briefly describes the processing flow for example form 2.

[0089] Step 1: The reception department receives customer complaints or inquiries. The reception department can receive complaints and inquiries through multiple channels, such as phone, email, and chat. The reception department quickly receives customer complaints and inquiries and routes them to the appropriate department. For example, the reception department can use an algorithm to analyze the content of customer complaints and inquiries and route them to the appropriate department. Step 2: The support department handles complaints or inquiries received by the reception department. The support department uses an AI agent to provide appropriate answers to customer complaints and inquiries. The AI ​​agent learns complaint patterns and answers, and improves the accuracy of its responses based on its experience interacting with customers. For example, the AI ​​agent can learn from past complaint and inquiry data to provide the best possible answers. Step 3: The learning unit learns from the customer interactions conducted by the response unit and improves the accuracy of its responses. The learning unit uses machine learning algorithms to analyze data from customer interactions and improve the accuracy of its responses. For example, the learning unit can use algorithms to improve the accuracy of the AI ​​agent's responses based on customer feedback.

[0090] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0091] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0092] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0093] Each of the multiple elements described above, including the reception unit, response unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives complaints and inquiries through multiple channels such as telephone, email, and chat. The response unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses an AI agent to provide appropriate answers to customer complaints and inquiries. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses a machine learning algorithm to analyze data from interactions with customers and improve the accuracy of responses. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0094] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0095] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0096] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0097] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0098] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0099] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0100] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0101] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0102] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0103] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0104] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0105] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0108] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] Each of the multiple elements described above, including the reception unit, response unit, and learning unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives complaints and inquiries through multiple channels such as telephone, email, and chat. The response unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and uses an AI agent to provide appropriate answers to customer complaints and inquiries. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and uses a machine learning algorithm to analyze data from interactions with customers and improve the accuracy of responses. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0110] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0111] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0112] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0113] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0114] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0116] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0117] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0118] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0119] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0120] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0122] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0124] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the reception unit, response unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives complaints and inquiries through multiple channels such as telephone, email, and chat. The response unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses an AI agent to provide appropriate answers to customer complaints and inquiries. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses a machine learning algorithm to analyze data from interactions with customers and improve the accuracy of responses. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0126] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0127] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0134] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, response unit, and learning unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives complaints and inquiries through multiple channels such as telephone, email, and chat. The response unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses an AI agent to provide appropriate answers to customer complaints and inquiries. The learning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses a machine learning algorithm to analyze data from interactions with customers and improve the accuracy of responses. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0145] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0146] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0147] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0151] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0152] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0153] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0154] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0155] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0156] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0158] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0159] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0160] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0161] (Note 1) A reception department that handles customer complaints or inquiries, A response department that handles complaints or inquiries received by the aforementioned reception department, The system includes a learning unit that learns from the customer interaction experience conducted by the aforementioned response unit and improves the accuracy of the response. A system characterized by the following features. (Note 2) The corresponding part is, Learn about complaint patterns and how to respond to them. The system described in Appendix 1, characterized by the features described herein. (Note 3) The corresponding part is, The company has an emotion analysis department that analyzes customer emotions and provides empathetic responses. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned learning unit, Learn from customer interaction experiences and improve the accuracy of your responses. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned emotion analysis unit, Analyze customer emotions and soothe them with empathetic words. The system described in Appendix 3, characterized by the features described herein. (Note 6) The corresponding part is, We have a customer needs analysis department that analyzes customer requests and uses the findings to improve our services. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We estimate customer sentiment and adjust how we handle complaints or inquiries based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the customer's past inquiry history and select the most suitable contact method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving a complaint or inquiry, filter them based on the customer's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Estimate the customer's emotions and determine the priority of complaints or inquiries to be received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving a complaint or inquiry, we prioritize processing requests based on the customer's geographical location to ensure they are relevant. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving a complaint or inquiry, we analyze the customer's social media activity and take relevant information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 13) The corresponding part is, We estimate the customer's emotions and adjust the way we respond based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The corresponding part is, When responding, adjust the level of detail in the response based on the severity of the complaint or inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 15) The corresponding part is, When responding, different response algorithms are applied depending on the category of the complaint or inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 16) The corresponding part is, The system estimates the customer's emotions and adjusts the length of the response based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The corresponding part is, When responding, we will prioritize the response based on when the complaint or inquiry was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The corresponding part is, When responding, we adjust the order of responses based on the relevance of the complaint or inquiry. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned learning unit, The system estimates customer emotions and selects training data based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception department that handles customer complaints or inquiries, A response department that handles complaints or inquiries received by the aforementioned reception department, The system includes a learning unit that learns from the customer interaction experience conducted by the aforementioned response unit and improves the accuracy of the response. A system characterized by the following features.

2. The corresponding part is, Learn about complaint patterns and how to respond to them. The system according to feature 1.

3. The corresponding part is, The company has an emotion analysis department that analyzes customer emotions and provides empathetic responses. The system according to feature 1.

4. The aforementioned learning unit, Learn from customer interaction experiences and improve the accuracy of your responses. The system according to feature 1.

5. The aforementioned emotion analysis unit, Analyze customer emotions and soothe them with empathetic words. The system according to claim 3.

6. The corresponding part is, We have a customer needs analysis department that analyzes customer requests and uses the findings to improve our services. The system according to feature 1.

7. The aforementioned reception unit is We estimate customer sentiment and adjust how we handle complaints or inquiries based on that estimated sentiment. The system according to feature 1.

8. The aforementioned reception unit is Analyze the customer's past inquiry history and select the most suitable contact method. The system according to feature 1.

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