system

Abstract

The system according to this embodiment aims to measure customer emotions and generate appropriate responses based on them. [Solution] The system according to this embodiment comprises a reception unit, an emotion measurement unit, an analysis unit, and a response generation unit. The reception unit receives customer inquiries. The emotion measurement unit measures emotions based on inquiries received by the reception unit. The analysis unit analyzes inquiries based on emotions measured by the emotion measurement unit. The response generation unit generates appropriate responses based on inquiries analyzed by the analysis unit.

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, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot 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] [[ID=2I]]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to generate an appropriate response considering the emotion for a customer inquiry, and there are problems in improving customer satisfaction.

[0005] The system according to the embodiment aims to measure the emotion of a customer and generate an appropriate response based on it.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an emotion measurement unit, an analysis unit, and a response generation unit. The reception unit receives customer inquiries. The emotion measurement unit measures emotions based on the inquiries received by the reception unit. The analysis unit analyzes the inquiries based on the emotions measured by the emotion measurement unit. The response generation unit generates an appropriate response based on the inquiries analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can measure customer emotions and generate an appropriate response based on them. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] 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 call center system according to an embodiment of the present invention is an advanced system that innovates customer service using artificial intelligence. This call center system provides intelligent 24 / 7 support across multiple communication channels, such as voice, chat, and social media, through natural language processing, sentiment analysis, and speech recognition. The system automatically processes customer inquiries and intelligently routes them to human agents as needed. Furthermore, by measuring emotions and responding appropriately, it ensures more personalized interactions and improves the customer experience. Seamless CRM integration and data-driven insights allow businesses to reduce operating costs and improve efficiency. Multilingual support enables the system to accommodate diverse customer bases. For example, the call center system provides intelligent 24 / 7 support across multiple communication channels, such as voice, chat, and social media, through natural language processing, sentiment analysis, and speech recognition. In this process, a reception unit that receives customer inquiries, an emotion measurement unit that measures emotions, an analysis unit that analyzes inquiries, and a response generation unit that generates appropriate responses work together. The system then automatically processes customer inquiries and intelligently routes them to human agents as needed. For example, if a customer inquiry is complex, the system automatically routes it to a human agent. Furthermore, by measuring emotions and responding appropriately, the system ensures more personalized interactions and improves the customer experience. For example, if a customer is dissatisfied, the system measures their emotions and generates an appropriate response. Seamless CRM integration and data-driven insights allow businesses to reduce operational costs and improve efficiency. For example, the system integrates customer inquiry history with the CRM system and provides data-driven insights. Providing multilingual support allows businesses to cater to diverse customer bases. For example, the system supports multiple languages, allowing customers to receive support in their native language. This enables call center systems to efficiently receive customer inquiries, measure and analyze emotions, and generate appropriate responses.

[0029] The call center system according to this embodiment comprises a reception unit, an emotion measurement unit, an analysis unit, and a response generation unit. The reception unit receives customer inquiries. Customer inquiries include, but are not limited to, multiple communication channels such as voice, chat, and social media. The reception unit can, for example, receive customer inquiries through a voice channel. The reception unit can also receive customer inquiries through a chat channel. Furthermore, the reception unit can also receive customer inquiries through a social media channel. For example, when receiving customer inquiries through a voice channel, the reception unit uses speech recognition technology to convert the customer's statements into text data. When receiving customer inquiries through a chat channel, it uses natural language processing technology to analyze the customer's message. When receiving customer inquiries through a social media channel, it uses the API of a social media platform to obtain the customer's message. The emotion measurement unit measures emotions based on inquiries received by the reception unit. The emotion measurement unit can, for example, estimate emotions from the content of customer statements using natural language processing technology. The emotion measurement unit can also estimate emotions from the tone and speed of the customer's voice using speech recognition technology. Furthermore, the emotion measurement unit can also estimate emotions from a customer's facial expressions using facial recognition technology. For example, the emotion measurement unit can analyze the customer's statements using natural language processing technology and estimate emotions such as positive, negative, or neutral. It can also analyze the tone and speed of the customer's voice using speech recognition technology and estimate the intensity of the emotion. It can analyze the customer's facial expressions using facial recognition technology and estimate the type of emotion. The analysis unit analyzes the inquiry based on the emotions measured by the emotion measurement unit. The analysis unit can, for example, analyze the customer's inquiry using natural language processing technology and extract information to generate an appropriate response. The analysis unit can also classify the customer's inquiry using machine learning technology and build a model to generate an appropriate response. Furthermore, the analysis unit can extract patterns from the customer's inquiry using data mining technology and create rules to generate an appropriate response.For example, the analysis unit analyzes customer inquiries using natural language processing technology and extracts keywords and phrases. It uses machine learning technology to classify customer inquiries and builds a model for generating appropriate responses. It uses data mining technology to extract patterns from customer inquiries and creates rules for generating appropriate responses. The response generation unit generates appropriate responses based on the inquiries analyzed by the analysis unit. The response generation unit can, for example, generate responses to customer inquiries using natural language generation technology. It can also generate responses based on pre-prepared templates using template-based response generation technology. Furthermore, the response generation unit can generate responses through dialogue with customers using a dialogue system. For example, when the response generation unit generates responses to customer inquiries using natural language generation technology, it generates grammatically correct sentences. It generates responses based on pre-prepared templates using template-based response generation technology. It generates responses through dialogue with customers using a dialogue system. As a result, the call center system according to this embodiment can efficiently receive customer inquiries, measure and analyze sentiment, and generate appropriate responses.

[0030] The reception desk receives customer inquiries. These inquiries may include, but are not limited to, multiple communication channels such as voice, chat, and social media. For example, the reception desk can receive customer inquiries through voice channels, chat channels, and social media channels. For instance, when receiving inquiries through voice channels, the reception desk uses speech recognition technology to convert customer statements into text data. When receiving inquiries through chat channels, it uses natural language processing technology to analyze customer messages. When receiving inquiries through social media channels, it uses the social media platform's API to retrieve customer messages. This allows the reception desk to smoothly receive inquiries regardless of the communication channel used by the customer. For voice channels, customer statements are transcribed in real time; for chat channels, customer messages are analyzed and responded to immediately; and for social media channels, customer posts are quickly retrieved and appropriate responses are provided. This allows customers to make inquiries in a way that suits them, and the reception desk can respond quickly. Furthermore, the reception department can centrally manage this inquiry data and smoothly hand it over to subsequent processing departments. For example, data transcribed using speech recognition technology is sent to the sentiment measurement and analysis departments for further processing. Similarly, chat and social media data is converted into an appropriate format and sent to the next processing step. This allows the reception department to efficiently receive customer inquiries and ensure the smooth operation of the entire system.

[0031] The emotion measurement unit measures emotions based on inquiries received by the reception unit. For example, the emotion measurement unit can estimate emotions from the content of customer statements using natural language processing technology. It can also estimate emotions from the tone and speed of customer voices using speech recognition technology. Furthermore, it can estimate emotions from customer facial expressions using facial expression recognition technology. For example, the emotion measurement unit analyzes the content of customer statements using natural language processing technology and estimates emotions such as positive, negative, and neutral. It analyzes the tone and speed of customer voices using speech recognition technology and estimates the intensity of emotions. It analyzes customer facial expressions using facial expression recognition technology and estimates the type of emotion. This allows the emotion measurement unit to grasp the customer's emotional state from multiple perspectives. By using natural language processing technology, it reads the nuances of emotions from the content of customer statements, and by using speech recognition technology, it measures the intensity of emotions from the tone and speed of voices. By using facial expression recognition technology, it identifies the type of emotion from customer facial expressions. This allows the emotion measurement unit to accurately grasp the customer's emotional state and provide information for appropriate responses. Furthermore, the emotion measurement unit can transmit this emotion data to the analysis unit, which can then use it as foundational data to generate appropriate responses. For example, if a customer is very angry, the emotion measurement unit transmits this information to the analysis unit, which then uses it as a guide for taking appropriate action. This allows the emotion measurement unit to accurately understand the customer's emotional state and improve the overall response quality of the system.

[0032] The analysis unit analyzes inquiries based on the emotions measured by the emotion measurement unit. For example, the analysis unit can use natural language processing technology to analyze customer inquiries and extract information to generate appropriate responses. It can also use machine learning technology to classify customer inquiries and build models to generate appropriate responses. Furthermore, the analysis unit can use data mining technology to extract patterns from customer inquiries and create rules to generate appropriate responses. For example, the analysis unit uses natural language processing technology to analyze customer inquiries and extract keywords and phrases. It uses machine learning technology to classify customer inquiries and build models to generate appropriate responses. It uses data mining technology to extract patterns from customer inquiries and create rules to generate appropriate responses. This allows the analysis unit to analyze customer inquiries in detail and provide information to generate appropriate responses. Furthermore, the analysis unit can leverage past inquiry data to continuously improve models for generating more accurate responses. For example, it uses past inquiry data to classify customer inquiries and build models to generate appropriate responses. This allows the analysis unit to analyze customer inquiries in detail and provide information to generate appropriate responses. Furthermore, the analysis unit can continuously improve its model for generating more accurate responses by utilizing past inquiry data. This allows the analysis unit to analyze customer inquiries in detail and provide information to generate appropriate responses.

[0033] The response generation unit generates an appropriate response based on the query analyzed by the analysis unit. The response generation unit can, for example, generate a response to a customer inquiry using natural language generation technology. It can also generate a response based on a pre-prepared template using template-based response generation technology. Furthermore, it can generate a response through dialogue with the customer using a dialogue system. For example, when generating a response to a customer inquiry using natural language generation technology, the response generation unit generates grammatically correct sentences. It can also generate a response based on a pre-prepared template using template-based response generation technology. It can also generate a response through dialogue with the customer using a dialogue system. This allows the response generation unit to provide a quick and appropriate response to customer inquiries. Furthermore, the response generation unit can evaluate the quality of the generated response and improve it as needed. For example, if the generated response does not meet customer expectations, the response generation unit receives the feedback and improves the response generation algorithm. The response generation unit can also adjust the tone and style of the response according to the content of the customer's inquiry and emotional state. This allows the response generation unit to provide flexible responses tailored to customer needs and improve customer satisfaction. Furthermore, the response generation unit can evaluate the quality of the generated responses and improve them as needed. For example, if the generated response does not meet customer expectations, the response generation unit receives the feedback and improves the response generation algorithm. The response generation unit can also adjust the tone and style of the response according to the customer's inquiry and emotional state. This allows the response generation unit to provide flexible responses tailored to customer needs and improve customer satisfaction.

[0034] The reception department may include a CRM integration unit that provides seamless CRM integration and data-driven insights. The CRM integration unit can, for example, integrate customer inquiry history into the CRM system. For instance, it can automatically register customer inquiry history into the CRM system and refer to past customer inquiries. Furthermore, the CRM integration unit can provide data-driven insights based on customer inquiries. For example, it can analyze customer inquiries and generate reports to understand customer needs and trends. This allows the CRM integration unit to reduce operational costs and improve efficiency for the company. Some or all of the above processes in the CRM integration unit may be performed using AI, or not. For example, the CRM integration unit can input customer inquiry history into AI and have the AI ​​perform an analysis of customer needs and trends.

[0035] The reception department may include a multilingual support unit that provides multilingual support. The multilingual support unit can, for example, support multiple languages. The multilingual support unit can, for example, perform real-time translations so that customers can receive support in their native language. Furthermore, the multilingual support unit can automatically translate customer inquiries and generate responses in the appropriate language. For example, the multilingual support unit translates customer inquiries in real time, allowing customers to receive support in their native language. The multilingual support unit automatically translates customer inquiries and generates responses in the appropriate language. This allows the multilingual support unit to accommodate a diverse customer base. Some or all of the above-described processes in the multilingual support unit may be performed using AI, for example, or not. For example, the multilingual support unit can input customer inquiries into AI and have the AI ​​perform real-time translations.

[0036] The reception desk can receive customer inquiries through multiple communication channels, such as voice, chat, and social media. For example, the reception desk can receive customer inquiries through a voice channel. The reception desk can also receive customer inquiries through a chat channel. The reception desk can also receive customer inquiries through a social media channel. For example, when receiving customer inquiries through a voice channel, the reception desk can use speech recognition technology to convert the customer's statements into text data. When receiving customer inquiries through a chat channel, the reception desk can use natural language processing technology to analyze the customer's message. When receiving customer inquiries through a social media channel, the reception desk can use the social media platform's API to retrieve the customer's message. This allows the reception desk to receive customer inquiries through multiple communication channels. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, when receiving customer inquiries through a voice channel, the reception desk can have AI perform the process of converting the customer's statements into text data using speech recognition technology.

[0037] The emotion measurement unit can measure the customer's emotions. For example, the emotion measurement unit can estimate emotions from the content of the customer's statements using natural language processing technology. For example, the emotion measurement unit can estimate emotions from the tone and speed of the customer's voice using speech recognition technology. For example, the emotion measurement unit can estimate emotions from the customer's facial expressions using facial recognition technology. For example, the emotion measurement unit analyzes the content of the customer's statements using natural language processing technology and estimates emotions such as positive, negative, and neutral. The emotion measurement unit analyzes the tone and speed of the customer's voice using speech recognition technology and estimates the intensity of the emotion. The emotion measurement unit analyzes the customer's facial expressions using facial recognition technology and estimates the type of emotion. In this way, the emotion measurement unit can measure the customer's emotions. Some or all of the above processing in the emotion measurement unit may be performed using AI, for example, or without AI. For example, the emotion measurement unit can input the content of the customer's statements into AI and have AI perform the emotion estimation.

[0038] The analysis unit can analyze customer inquiries. For example, the analysis unit can use natural language processing technology to analyze customer inquiries and extract information to generate appropriate responses. The analysis unit can also use machine learning technology to classify customer inquiries and build models to generate appropriate responses. The analysis unit can also use data mining technology to extract patterns from customer inquiries and create rules to generate appropriate responses. For example, the analysis unit uses natural language processing technology to analyze customer inquiries and extract keywords and phrases. The analysis unit uses machine learning technology to classify customer inquiries and build models to generate appropriate responses. The analysis unit uses data mining technology to extract patterns from customer inquiries and create rules to generate appropriate responses. This allows the analysis unit to analyze customer inquiries. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input customer inquiries into AI and have the AI ​​perform the analysis of the inquiries.

[0039] The response generation unit can generate an appropriate response. For example, the response generation unit can generate a response to a customer inquiry using natural language generation technology. The response generation unit can also generate a response based on a pre-prepared template using template-based response generation technology. The response generation unit can also generate a response through dialogue with a customer using a dialogue system. For example, when the response generation unit generates a response to a customer inquiry using natural language generation technology, it generates grammatically correct sentences. The response generation unit generates a response based on a pre-prepared template using template-based response generation technology. The response generation unit generates a response through dialogue with a customer using a dialogue system. This allows the response generation unit to generate an appropriate response. Some or all of the above-described processes in the response generation unit may be performed using AI, for example, or without AI. For example, the response generation unit can input the customer inquiry into AI and have AI generate the response.

[0040] The reception department can analyze a customer's past inquiry history and select the most suitable reception method. For example, the reception department can automatically display as suggestions the customer has frequently inquired about in the past. For example, the reception department can prioritize suggesting reception methods (voice, chat, etc.) that the customer has used in the past. For example, the reception department can predict and suggest the reception method to be used at a specific time of day 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 processes in the reception department may be performed using AI, for example, or not. For example, the reception department can input the customer's past inquiry history into AI and have the AI ​​select the most suitable reception method.

[0041] The reception desk can filter inquiries based on the customer's current situation and areas of interest when receiving them. For example, when a customer enters their current situation, the reception desk can prioritize displaying relevant inquiries. For example, the reception desk can provide relevant information based on the customer's areas of interest. For example, the reception desk can route the customer to the most suitable agent based on the customer's current situation and areas of interest. This allows the reception desk to provide a more appropriate response by filtering based on the customer's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the customer's current situation and areas of interest into the AI ​​and have the AI ​​perform the filtering.

[0042] The reception department can 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 department can prioritize receiving inquiries related to that region. For example, the reception department can route customers to the most suitable agent based on the customer's geographical location. For example, the reception department can provide relevant information considering the customer's geographical location. This allows the reception department to provide more appropriate responses by prioritizing highly relevant inquiries while considering the customer's geographical location. Some or all of the above processes in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input the customer's geographical location into the AI ​​and have the AI ​​prioritize receiving highly relevant inquiries.

[0043] The reception desk can analyze a customer's social media activity when receiving an inquiry and accept relevant inquiries. For example, the reception desk can analyze a customer's social media activity and prioritize displaying relevant inquiries. For example, the reception desk can route customers to the most suitable agent based on their social media activity. For example, the reception desk can provide relevant information considering a customer's social media activity. In this way, the reception desk can accept relevant inquiries by analyzing a customer's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input a customer's social media activity into an AI and have the AI ​​handle the acceptance of relevant inquiries.

[0044] The emotion measurement unit can optimize its measurement algorithm by referring to the customer's past emotional data during emotion measurement. For example, the emotion measurement unit can optimize its measurement algorithm based on the customer's past emotional data. For example, the emotion measurement unit can improve the accuracy of emotion measurement by referring to the customer's past emotional data. For example, the emotion measurement unit can analyze the customer's past emotional data and select the optimal measurement algorithm. In this way, the emotion measurement unit can optimize its measurement algorithm and improve the accuracy of emotion measurement by referring to the customer's past emotional data. Some or all of the above processing in the emotion measurement unit may be performed using AI, for example, or without using AI. For example, the emotion measurement unit can input the customer's past emotional data into AI and have AI perform the optimization of the measurement algorithm.

[0045] The emotion measurement unit can measure emotions by analyzing the content and tone of the customer's statements during emotion measurement. For example, the emotion measurement unit can analyze the content of the customer's statements and measure emotions. For example, the emotion measurement unit can analyze the tone of the customer and measure emotions. For example, the emotion measurement unit can comprehensively analyze the content and tone of the customer's statements and measure emotions. As a result, the emotion measurement unit can perform more accurate emotion measurement by analyzing the content and tone of the customer's statements. Some or all of the above processing in the emotion measurement unit may be performed using AI, for example, or without using AI. For example, the emotion measurement unit can input the content and tone of the customer's statements into AI and have the AI ​​perform emotion measurement.

[0046] The emotion measurement unit can measure emotions while considering the geographical distribution of customers. The emotion measurement unit can adjust the accuracy of emotion measurement based on the geographical distribution of customers, for example. The emotion measurement unit can display the results of emotion measurement while considering the geographical distribution of customers, for example. The emotion measurement unit can analyze the geographical distribution of customers and select the optimal emotion measurement method, for example. This allows the emotion measurement unit to perform more accurate emotion measurement by considering the geographical distribution of customers. Some or all of the above processing in the emotion measurement unit may be performed using AI, for example, or without AI. For example, the emotion measurement unit can input the geographical distribution of customers into AI and have AI perform adjustments to the accuracy of emotion measurement.

[0047] The sentiment measurement unit can improve the accuracy of sentiment measurement by referring to relevant customer literature during sentiment measurement. For example, the sentiment measurement unit can improve the accuracy of sentiment measurement by referring to relevant customer literature. For example, the sentiment measurement unit can select the optimal sentiment measurement method based on relevant customer literature. For example, the sentiment measurement unit can optimize the accuracy of sentiment measurement by analyzing relevant customer literature. In this way, the sentiment measurement unit can improve the accuracy of sentiment measurement by referring to relevant customer literature. Some or all of the above processing in the sentiment measurement unit may be performed using AI, for example, or without using AI. For example, the sentiment measurement unit can input relevant customer literature into AI and have AI perform the improvement of sentiment measurement accuracy.

[0048] The analysis unit can optimize its analysis algorithm by referring to past customer inquiry data during analysis. For example, the analysis unit can optimize its analysis algorithm based on past customer inquiry data. For example, the analysis unit can improve the accuracy of the analysis by referring to past customer inquiry data. For example, the analysis unit can analyze past customer inquiry data and select the optimal analysis algorithm. In this way, the analysis unit can optimize its analysis algorithm and improve the accuracy of the analysis by referring to past customer inquiry data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past customer inquiry data into AI and have AI perform the optimization of the analysis algorithm.

[0049] The analysis unit can analyze customer inquiries by analyzing the content and tone of their statements during the analysis process. For example, the analysis unit can analyze customer inquiries by analyzing the content of their statements. For example, the analysis unit can analyze inquiries by analyzing customer tone. For example, the analysis unit can analyze inquiries by comprehensively analyzing customer statements and tone. This allows the analysis unit to perform more accurate inquiry analysis by analyzing customer statements and tone. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input customer statements and tone into AI and have AI perform the inquiry analysis.

[0050] The analysis unit can perform analysis while considering the geographical distribution of customers. The analysis unit can, for example, adjust the accuracy of the analysis based on the geographical distribution of customers. The analysis unit can, for example, display the analysis results while considering the geographical distribution of customers. The analysis unit can, for example, analyze the geographical distribution of customers and select the optimal analysis method. This allows the analysis unit to perform more accurate analysis by considering the geographical distribution of customers. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution of customers into the AI ​​and have the AI ​​adjust the accuracy of the analysis.

[0051] The analysis unit can improve the accuracy of its analysis by referring to relevant customer literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant customer literature. For example, the analysis unit can select the optimal analysis method based on relevant customer literature. For example, the analysis unit can analyze relevant customer literature and optimize the accuracy of its analysis. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant customer literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant customer literature into AI and have AI perform the improvement of analysis accuracy.

[0052] The response generation unit can adjust the level of detail in the response based on the importance of the query when generating a response. For example, the response generation unit can generate a detailed response for important queries. For example, the response generation unit can generate a response with normal level of detail for general queries. For example, the response generation unit can generate a concise response for simple queries. In this way, the response generation unit can provide a more appropriate response by adjusting the level of detail in the response based on the importance of the query. Some or all of the above processing in the response generation unit may be performed using AI, for example, or without AI. For example, the response generation unit can input the importance of the query into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the response.

[0053] The response generation unit can apply different response algorithms depending on the category of the inquiry when generating a response. For example, the response generation unit can generate a response containing technical details for a technical inquiry. For example, the response generation unit can generate a response containing service details for a service inquiry. For example, the response generation unit can generate a response containing general information for a general inquiry. This allows the response generation unit to provide a more appropriate response by applying different response algorithms depending on the category of the inquiry. Some or all of the above processing in the response generation unit may be performed using AI, for example, or without AI. For example, the response generation unit can input the category of the inquiry into the AI ​​and have the AI ​​apply the response algorithm.

[0054] The response generation unit can determine the priority of responses based on when the inquiry was submitted. For example, the response generation unit can quickly generate responses to urgent inquiries. For example, the response generation unit can generate responses to regular inquiries with normal priority. For example, the response generation unit can postpone generating responses to past inquiries. This allows the response generation unit to provide more appropriate responses by determining the priority of responses based on when the inquiry was submitted. Some or all of the above processing in the response generation unit may be performed using AI, for example, or without AI. For example, the response generation unit can input the inquiry submission date to the AI ​​and have the AI ​​determine the priority of responses.

[0055] The response generation unit can adjust the order of responses based on the relevance of the queries when generating responses. For example, the response generation unit can prioritize generating responses for important queries. For example, the response generation unit can generate responses in the normal order for general queries. For example, the response generation unit can postpone generating responses for simple queries. In this way, the response generation unit can provide more appropriate responses by adjusting the order of responses based on the relevance of the queries. Some or all of the above processing in the response generation unit may be performed using AI, for example, or without AI. For example, the response generation unit can input the relevance of the queries into the AI ​​and have the AI ​​perform the adjustment of the order of responses.

[0056] The CRM integration unit can select the optimal integration method by referring to the customer's past inquiry history during CRM integration. For example, the CRM integration unit can select the optimal integration method based on the customer's past inquiry history. For example, the CRM integration unit can improve the accuracy of CRM integration by referring to the customer's past inquiry history. For example, the CRM integration unit can analyze the customer's past inquiry history and select the optimal integration method. In this way, the CRM integration unit can improve the accuracy of CRM integration by selecting the optimal integration method by referring to the customer's past inquiry history. Some or all of the above processes in the CRM integration unit may be performed using AI, for example, or without AI. For example, the CRM integration unit can input the customer's past inquiry history into AI and have AI select the optimal integration method.

[0057] The CRM integration unit can select the optimal integration method by considering the customer's geographical location information during CRM integration. For example, the CRM integration unit can select the optimal integration method based on the customer's geographical location information. For example, the CRM integration unit can improve the accuracy of CRM integration by considering the customer's geographical location information. For example, the CRM integration unit can analyze the customer's geographical location information and select the optimal integration method. As a result, the CRM integration unit can select the optimal integration method and improve the accuracy of CRM integration by considering the customer's geographical location information. Some or all of the above processing in the CRM integration unit may be performed using AI, for example, or without AI. For example, the CRM integration unit can input the customer's geographical location information into AI and have the AI ​​select the optimal integration method.

[0058] The multilingual support unit can select the optimal response method by referring to the customer's past inquiry history when providing multilingual support. For example, the multilingual support unit can select the optimal response method based on the customer's past inquiry history. For example, the multilingual support unit can improve the accuracy of multilingual support by referring to the customer's past inquiry history. For example, the multilingual support unit can analyze the customer's past inquiry history and select the optimal response method. As a result, the multilingual support unit can improve the accuracy of multilingual support by referring to the customer's past inquiry history and selecting the optimal response method. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without using AI. For example, the multilingual support unit can input the customer's past inquiry history into AI and have AI select the optimal response method.

[0059] The multilingual support unit can select the optimal response method when providing multilingual support, taking into account the customer's geographical location information. For example, the multilingual support unit can select the optimal response method based on the customer's geographical location information. For example, the multilingual support unit can improve the accuracy of multilingual support by considering the customer's geographical location information. For example, the multilingual support unit can analyze the customer's geographical location information and select the optimal response method. As a result, the multilingual support unit can select the optimal response method and improve the accuracy of multilingual support by considering the customer's geographical location information. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input the customer's geographical location information into AI and have AI select the optimal response method.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception department can automatically assess the urgency of customer inquiries and prioritize them based on their content. For example, it can analyze customer inquiries and prioritize those with higher urgency. It can also refer to a customer's past inquiry history and prioritize inquiries from frequent users. Furthermore, it can prioritize inquiries that are highly relevant based on the customer's current situation and areas of interest. This allows the reception department to provide more appropriate responses based on customer inquiries.

[0062] The analysis unit can perform more accurate analysis by referring to the customer's past inquiry history when analyzing customer inquiries. For example, the analysis unit can classify current inquiries based on past inquiries and extract information to generate appropriate responses. Furthermore, the analysis unit can analyze past inquiry history to build models for understanding customer needs and trends. In addition, the analysis unit can refer to past inquiry history to extract patterns in inquiries and create rules for generating appropriate responses. This allows the analysis unit to perform more accurate analysis by referring to past inquiry history.

[0063] The reception desk can route customers to the most suitable agent by considering their geographical location. For example, if a customer is in a specific region, the reception desk can prioritize inquiries related to that region. Furthermore, the reception desk can route customers to the most suitable agent based on their geographical location. In addition, the reception desk can provide relevant information considering the customer's geographical location. This allows the reception desk to provide more appropriate service by considering the customer's geographical location.

[0064] The analysis unit can analyze customer inquiries by analyzing the content and tone of their statements. For example, the analysis unit can analyze the content of customer statements and classify inquiries. Furthermore, the analysis unit can analyze the tone of customer statements to assess the urgency of inquiries. In addition, the analysis unit can comprehensively analyze the content and tone of customer statements to perform more accurate analysis. This allows the analysis unit to perform more accurate inquiry analysis by analyzing the content and tone of customer statements.

[0065] The reception desk can analyze customers' social media activity and prioritize the processing of relevant inquiries. For example, it can analyze customers' social media activity and display relevant inquiries preferentially. It can also route customers to the most suitable agent based on their social media activity. Furthermore, it can provide relevant information considering customers' social media activity. This allows the reception desk to provide more appropriate responses by analyzing customers' social media activity.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk receives customer inquiries. Customer inquiries include multiple communication channels such as voice, chat, and social media. For example, when receiving customer inquiries via voice channels, speech recognition technology is used to convert customer statements into text data. When receiving customer inquiries via chat channels, natural language processing technology is used to analyze customer messages. When receiving customer inquiries via social media channels, the social media platform's API is used to retrieve customer messages. Step 2: The emotion measurement unit measures emotions based on inquiries received by the reception unit. For example, it can estimate emotions from the content of customer statements using natural language processing technology. It can also estimate emotions from the tone and speed of customer voices using speech recognition technology. Furthermore, it can estimate emotions from customer facial expressions using facial recognition technology. Step 3: The analysis unit analyzes the inquiry based on the emotions measured by the emotion measurement unit. For example, natural language processing technology can be used to analyze the customer's inquiry and extract information to generate an appropriate response. Machine learning technology can also be used to classify the customer's inquiry and build a model to generate an appropriate response. Furthermore, data mining technology can be used to extract patterns from the customer's inquiry and create rules to generate an appropriate response. Step 4: The response generation unit generates an appropriate response based on the query analyzed by the analysis unit. For example, it can generate a response to a customer inquiry using natural language generation technology. Alternatively, it can generate a response based on a pre-prepared template using template-based response generation technology. Furthermore, it can generate a response through dialogue with the customer using a dialogue system.

[0068] (Example of form 2) The call center system according to an embodiment of the present invention is an advanced system that innovates customer service using artificial intelligence. This call center system provides intelligent 24 / 7 support across multiple communication channels, such as voice, chat, and social media, through natural language processing, sentiment analysis, and speech recognition. The system automatically processes customer inquiries and intelligently routes them to human agents as needed. Furthermore, by measuring emotions and responding appropriately, it ensures more personalized interactions and improves the customer experience. Seamless CRM integration and data-driven insights allow businesses to reduce operating costs and improve efficiency. Multilingual support enables the system to accommodate diverse customer bases. For example, the call center system provides intelligent 24 / 7 support across multiple communication channels, such as voice, chat, and social media, through natural language processing, sentiment analysis, and speech recognition. In this process, a reception unit that receives customer inquiries, an emotion measurement unit that measures emotions, an analysis unit that analyzes inquiries, and a response generation unit that generates appropriate responses work together. The system then automatically processes customer inquiries and intelligently routes them to human agents as needed. For example, if a customer inquiry is complex, the system automatically routes it to a human agent. Furthermore, by measuring emotions and responding appropriately, the system ensures more personalized interactions and improves the customer experience. For example, if a customer is dissatisfied, the system measures their emotions and generates an appropriate response. Seamless CRM integration and data-driven insights allow businesses to reduce operational costs and improve efficiency. For example, the system integrates customer inquiry history with the CRM system and provides data-driven insights. Providing multilingual support allows businesses to cater to diverse customer bases. For example, the system supports multiple languages, allowing customers to receive support in their native language. This enables call center systems to efficiently receive customer inquiries, measure and analyze emotions, and generate appropriate responses.

[0069] The call center system according to this embodiment comprises a reception unit, an emotion measurement unit, an analysis unit, and a response generation unit. The reception unit receives customer inquiries. Customer inquiries include, but are not limited to, multiple communication channels such as voice, chat, and social media. The reception unit can, for example, receive customer inquiries through a voice channel. The reception unit can also receive customer inquiries through a chat channel. Furthermore, the reception unit can also receive customer inquiries through a social media channel. For example, when receiving customer inquiries through a voice channel, the reception unit uses speech recognition technology to convert the customer's statements into text data. When receiving customer inquiries through a chat channel, it uses natural language processing technology to analyze the customer's message. When receiving customer inquiries through a social media channel, it uses the API of a social media platform to obtain the customer's message. The emotion measurement unit measures emotions based on inquiries received by the reception unit. The emotion measurement unit can, for example, estimate emotions from the content of customer statements using natural language processing technology. The emotion measurement unit can also estimate emotions from the tone and speed of the customer's voice using speech recognition technology. Furthermore, the emotion measurement unit can also estimate emotions from a customer's facial expressions using facial recognition technology. For example, the emotion measurement unit can analyze the customer's statements using natural language processing technology and estimate emotions such as positive, negative, or neutral. It can also analyze the tone and speed of the customer's voice using speech recognition technology and estimate the intensity of the emotion. It can analyze the customer's facial expressions using facial recognition technology and estimate the type of emotion. The analysis unit analyzes the inquiry based on the emotions measured by the emotion measurement unit. The analysis unit can, for example, analyze the customer's inquiry using natural language processing technology and extract information to generate an appropriate response. The analysis unit can also classify the customer's inquiry using machine learning technology and build a model to generate an appropriate response. Furthermore, the analysis unit can extract patterns from the customer's inquiry using data mining technology and create rules to generate an appropriate response.For example, the analysis unit analyzes customer inquiries using natural language processing technology and extracts keywords and phrases. It uses machine learning technology to classify customer inquiries and builds a model for generating appropriate responses. It uses data mining technology to extract patterns from customer inquiries and creates rules for generating appropriate responses. The response generation unit generates appropriate responses based on the inquiries analyzed by the analysis unit. The response generation unit can, for example, generate responses to customer inquiries using natural language generation technology. It can also generate responses based on pre-prepared templates using template-based response generation technology. Furthermore, the response generation unit can generate responses through dialogue with customers using a dialogue system. For example, when the response generation unit generates responses to customer inquiries using natural language generation technology, it generates grammatically correct sentences. It generates responses based on pre-prepared templates using template-based response generation technology. It generates responses through dialogue with customers using a dialogue system. As a result, the call center system according to this embodiment can efficiently receive customer inquiries, measure and analyze sentiment, and generate appropriate responses.

[0070] The reception desk receives customer inquiries. These inquiries may include, but are not limited to, multiple communication channels such as voice, chat, and social media. For example, the reception desk can receive customer inquiries through voice channels, chat channels, and social media channels. For instance, when receiving inquiries through voice channels, the reception desk uses speech recognition technology to convert customer statements into text data. When receiving inquiries through chat channels, it uses natural language processing technology to analyze customer messages. When receiving inquiries through social media channels, it uses the social media platform's API to retrieve customer messages. This allows the reception desk to smoothly receive inquiries regardless of the communication channel used by the customer. For voice channels, customer statements are transcribed in real time; for chat channels, customer messages are analyzed and responded to immediately; and for social media channels, customer posts are quickly retrieved and appropriate responses are provided. This allows customers to make inquiries in a way that suits them, and the reception desk can respond quickly. Furthermore, the reception department can centrally manage this inquiry data and smoothly hand it over to subsequent processing departments. For example, data transcribed using speech recognition technology is sent to the sentiment measurement and analysis departments for further processing. Similarly, chat and social media data is converted into an appropriate format and sent to the next processing step. This allows the reception department to efficiently receive customer inquiries and ensure the smooth operation of the entire system.

[0071] The emotion measurement unit measures emotions based on inquiries received by the reception unit. For example, the emotion measurement unit can estimate emotions from the content of customer statements using natural language processing technology. It can also estimate emotions from the tone and speed of customer voices using speech recognition technology. Furthermore, it can estimate emotions from customer facial expressions using facial expression recognition technology. For example, the emotion measurement unit analyzes the content of customer statements using natural language processing technology and estimates emotions such as positive, negative, and neutral. It analyzes the tone and speed of customer voices using speech recognition technology and estimates the intensity of emotions. It analyzes customer facial expressions using facial expression recognition technology and estimates the type of emotion. This allows the emotion measurement unit to grasp the customer's emotional state from multiple perspectives. By using natural language processing technology, it reads the nuances of emotions from the content of customer statements, and by using speech recognition technology, it measures the intensity of emotions from the tone and speed of voices. By using facial expression recognition technology, it identifies the type of emotion from customer facial expressions. This allows the emotion measurement unit to accurately grasp the customer's emotional state and provide information for appropriate responses. Furthermore, the emotion measurement unit can transmit this emotion data to the analysis unit, which can then use it as foundational data to generate appropriate responses. For example, if a customer is very angry, the emotion measurement unit transmits this information to the analysis unit, which then uses it as a guide for taking appropriate action. This allows the emotion measurement unit to accurately understand the customer's emotional state and improve the overall response quality of the system.

[0072] The analysis unit analyzes inquiries based on the emotions measured by the emotion measurement unit. For example, the analysis unit can use natural language processing technology to analyze customer inquiries and extract information to generate appropriate responses. It can also use machine learning technology to classify customer inquiries and build models to generate appropriate responses. Furthermore, the analysis unit can use data mining technology to extract patterns from customer inquiries and create rules to generate appropriate responses. For example, the analysis unit uses natural language processing technology to analyze customer inquiries and extract keywords and phrases. It uses machine learning technology to classify customer inquiries and build models to generate appropriate responses. It uses data mining technology to extract patterns from customer inquiries and create rules to generate appropriate responses. This allows the analysis unit to analyze customer inquiries in detail and provide information to generate appropriate responses. Furthermore, the analysis unit can leverage past inquiry data to continuously improve models for generating more accurate responses. For example, it uses past inquiry data to classify customer inquiries and build models to generate appropriate responses. This allows the analysis unit to analyze customer inquiries in detail and provide information to generate appropriate responses. Furthermore, the analysis unit can continuously improve its model for generating more accurate responses by utilizing past inquiry data. This allows the analysis unit to analyze customer inquiries in detail and provide information to generate appropriate responses.

[0073] The response generation unit generates an appropriate response based on the query analyzed by the analysis unit. The response generation unit can, for example, generate a response to a customer inquiry using natural language generation technology. It can also generate a response based on a pre-prepared template using template-based response generation technology. Furthermore, it can generate a response through dialogue with the customer using a dialogue system. For example, when generating a response to a customer inquiry using natural language generation technology, the response generation unit generates grammatically correct sentences. It can also generate a response based on a pre-prepared template using template-based response generation technology. It can also generate a response through dialogue with the customer using a dialogue system. This allows the response generation unit to provide a quick and appropriate response to customer inquiries. Furthermore, the response generation unit can evaluate the quality of the generated response and improve it as needed. For example, if the generated response does not meet customer expectations, the response generation unit receives the feedback and improves the response generation algorithm. The response generation unit can also adjust the tone and style of the response according to the content of the customer's inquiry and emotional state. This allows the response generation unit to provide flexible responses tailored to customer needs and improve customer satisfaction. Furthermore, the response generation unit can evaluate the quality of the generated responses and improve them as needed. For example, if the generated response does not meet customer expectations, the response generation unit receives the feedback and improves the response generation algorithm. The response generation unit can also adjust the tone and style of the response according to the customer's inquiry and emotional state. This allows the response generation unit to provide flexible responses tailored to customer needs and improve customer satisfaction.

[0074] The reception department may include a CRM integration unit that provides seamless CRM integration and data-driven insights. The CRM integration unit can, for example, integrate customer inquiry history into the CRM system. For instance, it can automatically register customer inquiry history into the CRM system and refer to past customer inquiries. Furthermore, the CRM integration unit can provide data-driven insights based on customer inquiries. For example, it can analyze customer inquiries and generate reports to understand customer needs and trends. This allows the CRM integration unit to reduce operational costs and improve efficiency for the company. Some or all of the above processes in the CRM integration unit may be performed using AI, or not. For example, the CRM integration unit can input customer inquiry history into AI and have the AI ​​perform an analysis of customer needs and trends.

[0075] The reception department may include a multilingual support unit that provides multilingual support. The multilingual support unit can, for example, support multiple languages. The multilingual support unit can, for example, perform real-time translations so that customers can receive support in their native language. Furthermore, the multilingual support unit can automatically translate customer inquiries and generate responses in the appropriate language. For example, the multilingual support unit translates customer inquiries in real time, allowing customers to receive support in their native language. The multilingual support unit automatically translates customer inquiries and generates responses in the appropriate language. This allows the multilingual support unit to accommodate a diverse customer base. Some or all of the above-described processes in the multilingual support unit may be performed using AI, for example, or not. For example, the multilingual support unit can input customer inquiries into AI and have the AI ​​perform real-time translations.

[0076] The reception desk can receive customer inquiries through multiple communication channels, such as voice, chat, and social media. For example, the reception desk can receive customer inquiries through a voice channel. The reception desk can also receive customer inquiries through a chat channel. The reception desk can also receive customer inquiries through a social media channel. For example, when receiving customer inquiries through a voice channel, the reception desk can use speech recognition technology to convert the customer's statements into text data. When receiving customer inquiries through a chat channel, the reception desk can use natural language processing technology to analyze the customer's message. When receiving customer inquiries through a social media channel, the reception desk can use the social media platform's API to retrieve the customer's message. This allows the reception desk to receive customer inquiries through multiple communication channels. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, when receiving customer inquiries through a voice channel, the reception desk can have AI perform the process of converting the customer's statements into text data using speech recognition technology.

[0077] The emotion measurement unit can measure the customer's emotions. For example, the emotion measurement unit can estimate emotions from the content of the customer's statements using natural language processing technology. For example, the emotion measurement unit can estimate emotions from the tone and speed of the customer's voice using speech recognition technology. For example, the emotion measurement unit can estimate emotions from the customer's facial expressions using facial recognition technology. For example, the emotion measurement unit analyzes the content of the customer's statements using natural language processing technology and estimates emotions such as positive, negative, and neutral. The emotion measurement unit analyzes the tone and speed of the customer's voice using speech recognition technology and estimates the intensity of the emotion. The emotion measurement unit analyzes the customer's facial expressions using facial recognition technology and estimates the type of emotion. In this way, the emotion measurement unit can measure the customer's emotions. Some or all of the above processing in the emotion measurement unit may be performed using AI, for example, or without AI. For example, the emotion measurement unit can input the content of the customer's statements into AI and have AI perform the emotion estimation.

[0078] The analysis unit can analyze customer inquiries. For example, the analysis unit can use natural language processing technology to analyze customer inquiries and extract information to generate appropriate responses. The analysis unit can also use machine learning technology to classify customer inquiries and build models to generate appropriate responses. The analysis unit can also use data mining technology to extract patterns from customer inquiries and create rules to generate appropriate responses. For example, the analysis unit uses natural language processing technology to analyze customer inquiries and extract keywords and phrases. The analysis unit uses machine learning technology to classify customer inquiries and build models to generate appropriate responses. The analysis unit uses data mining technology to extract patterns from customer inquiries and create rules to generate appropriate responses. This allows the analysis unit to analyze customer inquiries. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input customer inquiries into AI and have the AI ​​perform the analysis of the inquiries.

[0079] The response generation unit can generate an appropriate response. For example, the response generation unit can generate a response to a customer inquiry using natural language generation technology. The response generation unit can also generate a response based on a pre-prepared template using template-based response generation technology. The response generation unit can also generate a response through dialogue with a customer using a dialogue system. For example, when the response generation unit generates a response to a customer inquiry using natural language generation technology, it generates grammatically correct sentences. The response generation unit generates a response based on a pre-prepared template using template-based response generation technology. The response generation unit generates a response through dialogue with a customer using a dialogue system. This allows the response generation unit to generate an appropriate response. Some or all of the above-described processes in the response generation unit may be performed using AI, for example, or without AI. For example, the response generation unit can input the customer inquiry into AI and have AI generate the response.

[0080] The reception desk can estimate the customer's emotions and adjust the way inquiries are handled based on those emotions. For example, if the customer is stressed, the reception desk can provide a simple interface and minimize the input steps. If the customer is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. If the customer is in a hurry, the reception desk can prioritize voice input to ensure the inquiry is processed quickly. This allows the reception desk to provide a more appropriate response by adjusting the way inquiries are handled according to the customer's emotions. Emotion estimation is achieved using emotion estimation functions, such as 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 processing in the reception desk may be performed using AI or not. For example, the reception desk can input the customer's statements into the AI ​​to estimate the customer's emotions and have the AI ​​perform the emotion estimation.

[0081] The reception department can analyze a customer's past inquiry history and select the most suitable reception method. For example, the reception department can automatically display as suggestions the customer has frequently inquired about in the past. For example, the reception department can prioritize suggesting reception methods (voice, chat, etc.) that the customer has used in the past. For example, the reception department can predict and suggest the reception method to be used at a specific time of day 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 processes in the reception department may be performed using AI, for example, or not. For example, the reception department can input the customer's past inquiry history into AI and have the AI ​​select the most suitable reception method.

[0082] The reception desk can filter inquiries based on the customer's current situation and areas of interest when receiving them. For example, when a customer enters their current situation, the reception desk can prioritize displaying relevant inquiries. For example, the reception desk can provide relevant information based on the customer's areas of interest. For example, the reception desk can route the customer to the most suitable agent based on the customer's current situation and areas of interest. This allows the reception desk to provide a more appropriate response by filtering based on the customer's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the customer's current situation and areas of interest into the AI ​​and have the AI ​​perform the filtering.

[0083] The reception desk can estimate the customer's emotions and determine the priority of inquiries based on the estimated emotions. For example, if the customer is dissatisfied, the reception desk can prioritize their inquiry. For example, if the customer has an urgent inquiry, the reception desk can respond quickly. For example, if the customer is relaxed, the reception desk can respond with the usual priority. This allows the reception desk to provide more appropriate service by prioritizing inquiries 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 is, 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 AI or not. For example, the reception desk can input the customer's statements into the AI ​​and have the AI ​​perform the emotion estimation in order to estimate the customer's emotions.

[0084] The reception department can 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 department can prioritize receiving inquiries related to that region. For example, the reception department can route customers to the most suitable agent based on the customer's geographical location. For example, the reception department can provide relevant information considering the customer's geographical location. This allows the reception department to provide more appropriate responses by prioritizing highly relevant inquiries while considering the customer's geographical location. Some or all of the above processes in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input the customer's geographical location into the AI ​​and have the AI ​​prioritize receiving highly relevant inquiries.

[0085] The reception desk can analyze a customer's social media activity when receiving an inquiry and accept relevant inquiries. For example, the reception desk can analyze a customer's social media activity and prioritize displaying relevant inquiries. For example, the reception desk can route customers to the most suitable agent based on their social media activity. For example, the reception desk can provide relevant information considering a customer's social media activity. In this way, the reception desk can accept relevant inquiries by analyzing a customer's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input a customer's social media activity into an AI and have the AI ​​handle the acceptance of relevant inquiries.

[0086] The emotion measurement unit can estimate the customer's emotions and adjust the accuracy of the emotion measurement based on the estimated emotions. For example, if the customer is stressed, the emotion measurement unit can increase the accuracy of the emotion measurement. For example, if the customer is relaxed, the emotion measurement unit can maintain normal accuracy. For example, if the customer is in a hurry, the emotion measurement unit can quickly measure emotions. In this way, the emotion measurement unit can perform more accurate emotion measurement by adjusting the accuracy of the emotion measurement according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emotion measurement unit may be performed using AI or not using AI. For example, in order to estimate the customer's emotions, the emotion measurement unit can input the customer's statements into the AI ​​and have the AI ​​perform the emotion estimation.

[0087] The emotion measurement unit can optimize its measurement algorithm by referring to the customer's past emotional data during emotion measurement. For example, the emotion measurement unit can optimize its measurement algorithm based on the customer's past emotional data. For example, the emotion measurement unit can improve the accuracy of emotion measurement by referring to the customer's past emotional data. For example, the emotion measurement unit can analyze the customer's past emotional data and select the optimal measurement algorithm. In this way, the emotion measurement unit can optimize its measurement algorithm and improve the accuracy of emotion measurement by referring to the customer's past emotional data. Some or all of the above processing in the emotion measurement unit may be performed using AI, for example, or without using AI. For example, the emotion measurement unit can input the customer's past emotional data into AI and have AI perform the optimization of the measurement algorithm.

[0088] The emotion measurement unit can measure emotions by analyzing the content and tone of the customer's statements during emotion measurement. For example, the emotion measurement unit can analyze the content of the customer's statements and measure emotions. For example, the emotion measurement unit can analyze the tone of the customer and measure emotions. For example, the emotion measurement unit can comprehensively analyze the content and tone of the customer's statements and measure emotions. As a result, the emotion measurement unit can perform more accurate emotion measurement by analyzing the content and tone of the customer's statements. Some or all of the above processing in the emotion measurement unit may be performed using AI, for example, or without using AI. For example, the emotion measurement unit can input the content and tone of the customer's statements into AI and have the AI ​​perform emotion measurement.

[0089] The emotion measurement unit can estimate the customer's emotions and adjust the order in which it displays the emotion measurement results based on the estimated emotions. For example, if the customer is feeling dissatisfied, the emotion measurement unit can prioritize displaying the emotion measurement results. For example, if the customer is relaxed, the emotion measurement unit can display the emotion measurement results in the normal order. For example, if the customer is in a hurry, the emotion measurement unit can quickly display the emotion measurement results. This allows the emotion measurement unit to provide a more appropriate response by adjusting the order in which it displays the emotion measurement results according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the emotion measurement unit may be performed using AI, for example, or without AI. For example, the emotion measurement unit can input the customer's statements into the AI ​​to estimate the customer's emotions and have the AI ​​perform the emotion estimation.

[0090] The emotion measurement unit can measure emotions while considering the geographical distribution of customers. The emotion measurement unit can adjust the accuracy of emotion measurement based on the geographical distribution of customers, for example. The emotion measurement unit can display the results of emotion measurement while considering the geographical distribution of customers, for example. The emotion measurement unit can analyze the geographical distribution of customers and select the optimal emotion measurement method, for example. This allows the emotion measurement unit to perform more accurate emotion measurement by considering the geographical distribution of customers. Some or all of the above processing in the emotion measurement unit may be performed using AI, for example, or without AI. For example, the emotion measurement unit can input the geographical distribution of customers into AI and have AI perform adjustments to the accuracy of emotion measurement.

[0091] The sentiment measurement unit can improve the accuracy of sentiment measurement by referring to relevant customer literature during sentiment measurement. For example, the sentiment measurement unit can improve the accuracy of sentiment measurement by referring to relevant customer literature. For example, the sentiment measurement unit can select the optimal sentiment measurement method based on relevant customer literature. For example, the sentiment measurement unit can optimize the accuracy of sentiment measurement by analyzing relevant customer literature. In this way, the sentiment measurement unit can improve the accuracy of sentiment measurement by referring to relevant customer literature. Some or all of the above processing in the sentiment measurement unit may be performed using AI, for example, or without using AI. For example, the sentiment measurement unit can input relevant customer literature into AI and have AI perform the improvement of sentiment measurement accuracy.

[0092] The analysis unit can estimate the customer's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the customer is dissatisfied, the analysis unit can tighten the analysis criteria. For example, if the customer is relaxed, the analysis unit can perform the analysis using normal criteria. For example, if the customer is in a hurry, the analysis unit can perform the analysis quickly. This allows the analysis unit to perform a more appropriate analysis by adjusting the analysis criteria according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, in order to estimate the customer's emotions, the analysis unit can input the customer's statements into the AI ​​and have the AI ​​perform the emotion estimation.

[0093] The analysis unit can optimize its analysis algorithm by referring to past customer inquiry data during analysis. For example, the analysis unit can optimize its analysis algorithm based on past customer inquiry data. For example, the analysis unit can improve the accuracy of the analysis by referring to past customer inquiry data. For example, the analysis unit can analyze past customer inquiry data and select the optimal analysis algorithm. In this way, the analysis unit can optimize its analysis algorithm and improve the accuracy of the analysis by referring to past customer inquiry data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past customer inquiry data into AI and have AI perform the optimization of the analysis algorithm.

[0094] The analysis unit can analyze customer inquiries by analyzing the content and tone of their statements during the analysis process. For example, the analysis unit can analyze customer inquiries by analyzing the content of their statements. For example, the analysis unit can analyze inquiries by analyzing customer tone. For example, the analysis unit can analyze inquiries by comprehensively analyzing customer statements and tone. This allows the analysis unit to perform more accurate inquiry analysis by analyzing customer statements and tone. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input customer statements and tone into AI and have AI perform the inquiry analysis.

[0095] The analysis unit can estimate the customer's emotions and adjust the order in which the analysis results are displayed based on the estimated emotions. For example, if the customer is dissatisfied, the analysis unit can prioritize displaying the analysis results. For example, if the customer is relaxed, the analysis unit can display the analysis results in the normal order. For example, if the customer is in a hurry, the analysis unit can quickly display the analysis results. This allows the analysis unit to provide a more appropriate response by adjusting the order in which the analysis results are displayed according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, in order to estimate the customer's emotions, the analysis unit can input the customer's statements into the AI ​​and have the AI ​​perform the emotion estimation.

[0096] The analysis unit can perform analysis while considering the geographical distribution of customers. The analysis unit can, for example, adjust the accuracy of the analysis based on the geographical distribution of customers. The analysis unit can, for example, display the analysis results while considering the geographical distribution of customers. The analysis unit can, for example, analyze the geographical distribution of customers and select the optimal analysis method. This allows the analysis unit to perform more accurate analysis by considering the geographical distribution of customers. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution of customers into the AI ​​and have the AI ​​adjust the accuracy of the analysis.

[0097] The analysis unit can improve the accuracy of its analysis by referring to relevant customer literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant customer literature. For example, the analysis unit can select the optimal analysis method based on relevant customer literature. For example, the analysis unit can analyze relevant customer literature and optimize the accuracy of its analysis. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant customer literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant customer literature into AI and have AI perform the improvement of analysis accuracy.

[0098] The response generation unit can estimate the customer's emotions and adjust the way it expresses its response based on the estimated emotions. For example, if the customer is dissatisfied, the response generation unit can use a polite and empathetic expression. For example, if the customer is relaxed, the response generation unit can use a casual expression. For example, if the customer is in a hurry, the response generation unit can use a concise and quick expression. This allows the response generation unit to provide a more appropriate response by adjusting the way it expresses its response according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, 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 generation unit may be performed using AI, for example, or not using AI. For example, the response generation unit can input the customer's statements into the AI ​​to estimate the customer's emotions and have the AI ​​perform the emotion estimation.

[0099] The response generation unit can adjust the level of detail in the response based on the importance of the query when generating a response. For example, the response generation unit can generate a detailed response for important queries. For example, the response generation unit can generate a response with normal level of detail for general queries. For example, the response generation unit can generate a concise response for simple queries. In this way, the response generation unit can provide a more appropriate response by adjusting the level of detail in the response based on the importance of the query. Some or all of the above processing in the response generation unit may be performed using AI, for example, or without AI. For example, the response generation unit can input the importance of the query into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the response.

[0100] The response generation unit can apply different response algorithms depending on the category of the inquiry when generating a response. For example, the response generation unit can generate a response containing technical details for a technical inquiry. For example, the response generation unit can generate a response containing service details for a service inquiry. For example, the response generation unit can generate a response containing general information for a general inquiry. This allows the response generation unit to provide a more appropriate response by applying different response algorithms depending on the category of the inquiry. Some or all of the above processing in the response generation unit may be performed using AI, for example, or without AI. For example, the response generation unit can input the category of the inquiry into the AI ​​and have the AI ​​apply the response algorithm.

[0101] The response generation unit can estimate the customer's emotions and adjust the length of the response based on the estimated emotions. For example, if the customer is dissatisfied, the response generation unit can generate a detailed response. For example, if the customer is relaxed, the response generation unit can generate a response of normal length. For example, if the customer is in a hurry, the response generation unit can generate a concise response. This allows the response generation unit to provide a more appropriate response by adjusting the length of the response according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, 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 generation unit may be performed using AI or not using AI. For example, the response generation unit can input the customer's statements into the AI ​​to estimate the customer's emotions and have the AI ​​perform the emotion estimation.

[0102] The response generation unit can determine the priority of responses based on when the inquiry was submitted. For example, the response generation unit can quickly generate responses to urgent inquiries. For example, the response generation unit can generate responses to regular inquiries with normal priority. For example, the response generation unit can postpone generating responses to past inquiries. This allows the response generation unit to provide more appropriate responses by determining the priority of responses based on when the inquiry was submitted. Some or all of the above processing in the response generation unit may be performed using AI, for example, or without AI. For example, the response generation unit can input the inquiry submission date to the AI ​​and have the AI ​​determine the priority of responses.

[0103] The response generation unit can adjust the order of responses based on the relevance of the queries when generating responses. For example, the response generation unit can prioritize generating responses for important queries. For example, the response generation unit can generate responses in the normal order for general queries. For example, the response generation unit can postpone generating responses for simple queries. In this way, the response generation unit can provide more appropriate responses by adjusting the order of responses based on the relevance of the queries. Some or all of the above processing in the response generation unit may be performed using AI, for example, or without AI. For example, the response generation unit can input the relevance of the queries into the AI ​​and have the AI ​​perform the adjustment of the order of responses.

[0104] The CRM integration unit can estimate customer emotions and adjust the CRM integration method based on the estimated customer emotions. For example, if a customer is dissatisfied, the CRM integration unit can perform CRM integration quickly. For example, if a customer is relaxed, the CRM integration unit can perform CRM integration in the usual way. For example, if a customer is in a hurry, the CRM integration unit can perform CRM integration quickly. This allows the CRM integration unit to perform more appropriate integration by adjusting the CRM integration method according to customer emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the CRM integration unit may be performed using AI or not using AI. For example, the CRM integration unit can input customer statements into the AI ​​to estimate customer emotions and have the AI ​​perform the emotion estimation.

[0105] The CRM integration unit can select the optimal integration method by referring to the customer's past inquiry history during CRM integration. For example, the CRM integration unit can select the optimal integration method based on the customer's past inquiry history. For example, the CRM integration unit can improve the accuracy of CRM integration by referring to the customer's past inquiry history. For example, the CRM integration unit can analyze the customer's past inquiry history and select the optimal integration method. In this way, the CRM integration unit can improve the accuracy of CRM integration by selecting the optimal integration method by referring to the customer's past inquiry history. Some or all of the above processes in the CRM integration unit may be performed using AI, for example, or without AI. For example, the CRM integration unit can input the customer's past inquiry history into AI and have AI select the optimal integration method.

[0106] The CRM integration unit can estimate customer emotions and determine the priority of CRM integrations based on the estimated customer emotions. For example, if a customer is dissatisfied, the CRM integration unit can prioritize CRM integration. If a customer is relaxed, the CRM integration unit can perform CRM integration with normal priority. If a customer is in a hurry, the CRM integration unit can perform CRM integration quickly. This allows the CRM integration unit to perform more appropriate integrations by determining the priority of CRM integrations according to customer emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines 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 processing in the CRM integration unit may be performed using AI or not. For example, the CRM integration unit can input customer statements into an AI to estimate customer emotions and have the AI ​​perform the emotion estimation.

[0107] The CRM integration unit can select the optimal integration method by considering the customer's geographical location information during CRM integration. For example, the CRM integration unit can select the optimal integration method based on the customer's geographical location information. For example, the CRM integration unit can improve the accuracy of CRM integration by considering the customer's geographical location information. For example, the CRM integration unit can analyze the customer's geographical location information and select the optimal integration method. As a result, the CRM integration unit can select the optimal integration method and improve the accuracy of CRM integration by considering the customer's geographical location information. Some or all of the above processing in the CRM integration unit may be performed using AI, for example, or without AI. For example, the CRM integration unit can input the customer's geographical location information into AI and have the AI ​​select the optimal integration method.

[0108] The multilingual support unit can estimate the customer's emotions and adjust its multilingual support method based on the estimated emotions. For example, if the customer is dissatisfied, the multilingual support unit can provide rapid multilingual support. For example, if the customer is relaxed, the multilingual support unit can provide multilingual support in the usual way. For example, if the customer is in a hurry, the multilingual support unit can provide rapid multilingual support. This allows the multilingual support unit to provide more appropriate support by adjusting its multilingual support method according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, in order to estimate the customer's emotions, the multilingual support unit can input the customer's statements into the AI ​​and have the AI ​​perform the emotion estimation.

[0109] The multilingual support unit can select the optimal response method by referring to the customer's past inquiry history when providing multilingual support. For example, the multilingual support unit can select the optimal response method based on the customer's past inquiry history. For example, the multilingual support unit can improve the accuracy of multilingual support by referring to the customer's past inquiry history. For example, the multilingual support unit can analyze the customer's past inquiry history and select the optimal response method. As a result, the multilingual support unit can improve the accuracy of multilingual support by referring to the customer's past inquiry history and selecting the optimal response method. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without using AI. For example, the multilingual support unit can input the customer's past inquiry history into AI and have AI select the optimal response method.

[0110] The multilingual support unit can estimate the customer's emotions and determine the priority of multilingual support based on the estimated emotions. For example, if the customer is dissatisfied, the multilingual support unit can prioritize multilingual support. For example, if the customer is relaxed, the multilingual support unit can provide multilingual support with normal priority. For example, if the customer is in a hurry, the multilingual support unit can provide multilingual support quickly. This allows the multilingual support unit to provide more appropriate support by determining the priority of multilingual support according to the customer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the multilingual support unit may be performed using AI or not using AI. For example, in order to estimate the customer's emotions, the multilingual support unit can input the customer's statements into the AI ​​and have the AI ​​perform the emotion estimation.

[0111] The multilingual support unit can select the optimal response method when providing multilingual support, taking into account the customer's geographical location information. For example, the multilingual support unit can select the optimal response method based on the customer's geographical location information. For example, the multilingual support unit can improve the accuracy of multilingual support by considering the customer's geographical location information. For example, the multilingual support unit can analyze the customer's geographical location information and select the optimal response method. As a result, the multilingual support unit can select the optimal response method and improve the accuracy of multilingual support by considering the customer's geographical location information. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input the customer's geographical location information into AI and have AI select the optimal response method.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The reception department can automatically assess the urgency of customer inquiries and prioritize them based on their content. For example, it can analyze customer inquiries and prioritize those with higher urgency. It can also refer to a customer's past inquiry history and prioritize inquiries from frequent users. Furthermore, it can prioritize inquiries that are highly relevant based on the customer's current situation and areas of interest. This allows the reception department to provide more appropriate responses based on customer inquiries.

[0114] The emotion measurement unit can estimate the customer's emotions and adjust the tone of its response based on those estimates. For example, if the customer is feeling dissatisfied, the emotion measurement unit can respond in a polite and empathetic tone. It can also respond in a casual tone if the customer is relaxed. Furthermore, if the customer is in a hurry, the emotion measurement unit can respond in a quick and concise tone. This allows the emotion measurement unit to provide more appropriate responses based on the customer's emotions.

[0115] The analysis unit can perform more accurate analysis by referring to the customer's past inquiry history when analyzing customer inquiries. For example, the analysis unit can classify current inquiries based on past inquiries and extract information to generate appropriate responses. Furthermore, the analysis unit can analyze past inquiry history to build models for understanding customer needs and trends. In addition, the analysis unit can refer to past inquiry history to extract patterns in inquiries and create rules for generating appropriate responses. This allows the analysis unit to perform more accurate analysis by referring to past inquiry history.

[0116] The response generation unit can estimate the customer's emotions and adjust the content of the response based on those emotions. For example, if the customer is dissatisfied, the response generation unit can generate a specific response focused on problem solving. If the customer is relaxed, the response generation unit can also generate a response that includes general information and additional suggestions. Furthermore, if the customer is in a hurry, the response generation unit can generate a quick and concise response. This allows the response generation unit to provide more appropriate responses depending on the customer's emotions.

[0117] The reception desk can route customers to the most suitable agent by considering their geographical location. For example, if a customer is in a specific region, the reception desk can prioritize inquiries related to that region. Furthermore, the reception desk can route customers to the most suitable agent based on their geographical location. In addition, the reception desk can provide relevant information considering the customer's geographical location. This allows the reception desk to provide more appropriate service by considering the customer's geographical location.

[0118] The emotion measurement unit can estimate the customer's emotions and adjust the order in which it displays the emotion measurement results based on the estimated emotions. For example, if the customer is feeling dissatisfied, the emotion measurement unit can prioritize displaying the emotion measurement results. Conversely, if the customer is relaxed, the emotion measurement unit can display the emotion measurement results in the normal order. Furthermore, if the customer is in a hurry, the emotion measurement unit can quickly display the emotion measurement results. This allows the emotion measurement unit to respond more appropriately to the customer's emotions.

[0119] The analysis unit can analyze customer inquiries by analyzing the content and tone of their statements. For example, the analysis unit can analyze the content of customer statements and classify inquiries. Furthermore, the analysis unit can analyze the tone of customer statements to assess the urgency of inquiries. In addition, the analysis unit can comprehensively analyze the content and tone of customer statements to perform more accurate analysis. This allows the analysis unit to perform more accurate inquiry analysis by analyzing the content and tone of customer statements.

[0120] The response generation unit can estimate the customer's emotions and adjust the length of the response based on those emotions. For example, if the customer is dissatisfied, the response generation unit can generate a detailed response. Conversely, if the customer is relaxed, it can generate a response of normal length. Furthermore, if the customer is in a hurry, it can generate a concise response. This allows the response generation unit to provide more appropriate responses based on the customer's emotions.

[0121] The reception desk can analyze customers' social media activity and prioritize the processing of relevant inquiries. For example, it can analyze customers' social media activity and display relevant inquiries preferentially. It can also route customers to the most suitable agent based on their social media activity. Furthermore, it can provide relevant information considering customers' social media activity. This allows the reception desk to provide more appropriate responses by analyzing customers' social media activity.

[0122] The CRM integration department can estimate customer emotions and adjust the CRM integration method based on those estimated emotions. For example, if a customer is dissatisfied, the CRM integration department can perform a rapid CRM integration. Conversely, if a customer is relaxed, the CRM integration department can perform a standard CRM integration. Furthermore, if a customer is in a hurry, the CRM integration department can perform a rapid CRM integration. This allows the CRM integration department to perform more appropriate integrations according to customer emotions.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The reception desk receives customer inquiries. Customer inquiries include multiple communication channels such as voice, chat, and social media. For example, when receiving customer inquiries via voice channels, speech recognition technology is used to convert customer statements into text data. When receiving customer inquiries via chat channels, natural language processing technology is used to analyze customer messages. When receiving customer inquiries via social media channels, the social media platform's API is used to retrieve customer messages. Step 2: The emotion measurement unit measures emotions based on inquiries received by the reception unit. For example, it can estimate emotions from the content of customer statements using natural language processing technology. It can also estimate emotions from the tone and speed of customer voices using speech recognition technology. Furthermore, it can estimate emotions from customer facial expressions using facial recognition technology. Step 3: The analysis unit analyzes the inquiry based on the emotions measured by the emotion measurement unit. For example, natural language processing technology can be used to analyze the customer's inquiry and extract information to generate an appropriate response. Machine learning technology can also be used to classify the customer's inquiry and build a model to generate an appropriate response. Furthermore, data mining technology can be used to extract patterns from the customer's inquiry and create rules to generate an appropriate response. Step 4: The response generation unit generates an appropriate response based on the query analyzed by the analysis unit. For example, it can generate a response to a customer inquiry using natural language generation technology. Alternatively, it can generate a response based on a pre-prepared template using template-based response generation technology. Furthermore, it can generate a response through dialogue with the customer using a dialogue system.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the reception unit, emotion measurement unit, analysis unit, and response generation 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, when receiving customer inquiries through a voice channel, uses speech recognition technology to convert customer statements into text data. The emotion measurement unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses natural language processing technology to estimate emotions from the content of customer statements. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses natural language processing technology to analyze the content of customer inquiries and extract information for generating appropriate responses. The response generation unit is implemented by the control unit 46A of the smart device 14 and uses natural language generation technology to generate responses to customer inquiries. 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.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the reception unit, emotion measurement unit, analysis unit, and response generation unit, is implemented 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 analyzes customer messages using natural language processing technology when receiving customer inquiries through a chat channel. The emotion measurement unit is implemented by the identification processing unit 290 of the data processing unit 12 and estimates emotions from the tone and speed of the customer's voice using speech recognition technology. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and builds a model for classifying customer inquiries and generating appropriate responses using machine learning technology. The response generation unit is implemented by the control unit 46A of the smart glasses 214 and generates responses based on pre-prepared templates using template-based response generation technology. 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.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the reception unit, emotion measurement unit, analysis unit, and response generation 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 retrieves customer messages using the API of a social media platform when receiving customer inquiries through social media channels. The emotion measurement unit is implemented by the identification processing unit 290 of the data processing unit 12 and estimates emotions from the customer's facial expressions using facial recognition technology. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and extracts patterns from the customer's inquiry using data mining technology and creates rules for generating appropriate responses. The response generation unit is implemented by the control unit 46A of the headset terminal 314 and generates responses through dialogue with the customer using a dialogue system. 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.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the reception unit, emotion measurement unit, analysis unit, and response generation unit, is implemented in 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, when receiving customer inquiries through a voice channel, uses speech recognition technology to convert customer statements into text data. The emotion measurement unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses natural language processing technology to estimate emotions from the content of customer statements. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses natural language processing technology to analyze the content of customer inquiries and extract information for generating appropriate responses. The response generation unit is implemented by the control unit 46A of the robot 414 and uses natural language generation technology to generate responses to customer inquiries. 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) The reception department handles customer inquiries, An emotion measurement unit that measures emotions based on inquiries received by the reception unit, An analysis unit analyzes the inquiry based on the emotion measured by the emotion measurement unit, The system includes a response generation unit that generates an appropriate response based on the query analyzed by the analysis unit. A system characterized by the following features. (Note 2) Features a CRM integration unit that provides seamless CRM integration and data-driven insights. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a multilingual support unit that provides multilingual support. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is We accept customer inquiries through multiple communication channels, including voice, chat, and social media. The system described in Appendix 1, characterized by the features described herein. (Note 5) The emotion measurement unit is Measuring customer emotions The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Analyze customer inquiries The system described in Appendix 1, characterized by the features described herein. (Note 7) The response generation unit, Generate an appropriate response The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We estimate customer emotions and adjust how inquiries are handled based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) The aforementioned reception unit is When receiving an inquiry, filtering is performed based on the customer's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is The system estimates customer emotions and prioritizes inquiries based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving inquiries, the system prioritizes inquiries that are highly relevant, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When receiving an inquiry, the system analyzes the customer's social media activity and selects relevant inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 14) The emotion measurement unit is We estimate customer emotions and adjust the accuracy of emotion measurement based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The emotion measurement unit is When measuring emotions, the measurement algorithm is optimized by referring to the customer's past emotional data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The emotion measurement unit is During emotion measurement, we analyze the customer's statements and tone to determine their emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The emotion measurement unit is It estimates customer emotions and adjusts the order in which the emotion measurement results are displayed based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The emotion measurement unit is When measuring emotions, consider the geographical distribution of customers. The system described in Appendix 1, characterized by the features described herein. (Note 19) The emotion measurement unit is When measuring sentiment, we improve the accuracy of sentiment measurement by referring to relevant literature on the customer. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, We estimate customer emotions and adjust the analysis criteria based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to the customer's past inquiry data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During the analysis, the content and tone of customer statements are analyzed to interpret inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, It estimates customer emotions and adjusts the order in which the analysis results are displayed based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, During the analysis, the geographical distribution of customers will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, During analysis, we refer to relevant customer literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 26) The response generation unit, It estimates the customer's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The response generation unit, When generating a response, adjust the level of detail in the response based on the importance of the query. The system described in Appendix 1, characterized by the features described herein. (Note 28) The response generation unit, When generating a response, apply different response algorithms depending on the query category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The response generation unit, It estimates the customer's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The response generation unit, When generating a response, the priority of the response is determined based on when the inquiry was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 31) The response generation unit, When generating responses, the order of responses is adjusted based on the relevance of the queries. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned CRM integration unit, Estimate customer sentiment and adjust CRM integration methods based on estimated customer sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned CRM integration unit, When integrating CRM, the optimal integration method is selected by referring to the customer's past inquiry history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned CRM integration unit, Estimate customer sentiment and prioritize CRM integrations based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned CRM integration unit, When integrating CRM, the optimal integration method is selected by considering the geographical location of customers. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned multilingual support unit is We estimate customer emotions and adjust our multilingual support methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned multilingual support unit is When providing multilingual support, the system selects the most appropriate response method by referring to the customer's past inquiry history. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned multilingual support unit is The system estimates customer emotions and prioritizes multilingual support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned multilingual support unit is When providing multilingual support, the optimal support method is selected by considering the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0197] 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. The reception department handles customer inquiries, An emotion measurement unit that measures emotions based on inquiries received by the reception unit, An analysis unit analyzes the inquiry based on the emotion measured by the emotion measurement unit, The system includes a response generation unit that generates an appropriate response based on the query analyzed by the analysis unit. A system characterized by the following features.

2. Features a CRM integration unit that provides seamless CRM integration and data-driven insights. The system according to feature 1.

3. It features a multilingual support unit that provides multilingual support. The system according to feature 1.

4. The aforementioned reception unit is We accept customer inquiries through multiple communication channels, including voice, chat, and social media. The system according to feature 1.

5. The emotion measurement unit is Measuring customer emotions The system according to feature 1.

6. The aforementioned analysis unit, Analyze customer inquiries The system according to feature 1.

7. The response generation unit, Generate an appropriate response The system according to feature 1.

8. The aforementioned reception unit is We estimate customer emotions and adjust how inquiries are handled based on those estimated emotions. The system according to feature 1.

9. 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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