System

Abstract

An object of a system according to an embodiment is to provide a consistent reception according to a preference of a customer.SOLUTION: A system includes a storage unit, a reception unit, and a speed adjustment unit. The storage unit stores orders and histories. The reception unit makes a simple answer without honorific language based on the information stored in the storage unit. The speed adjustment unit adjusts the speed of reception performed by the reception unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has led to inconsistent response from call centers, leaving room for improvement in terms of increasing customer repeat rates.

[0005] The system according to the embodiment aims to provide a consistent response that matches the customer's preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a storage unit, a response unit, and a speed adjustment unit. The storage unit stores orders and history. The response unit responds simply without using polite language based on the information stored by the storage unit. The speed adjustment unit adjusts the speed of the response performed by the response unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a consistent response that matches the customer's preferences. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The AI ​​call center system according to the embodiment of the present invention memorizes orders and history, making customers feel as if the same person will be answering their calls next time. This allows the AI ​​call center system to increase repeat calls and reduce call volume and costs.

[0029] The AI ​​call center system according to the embodiment includes a memory unit, a response unit, and a speed adjustment unit. The memory unit stores orders and history. For example, the generation AI stores orders and past response history. The memory unit can also store, for example, previous order details and specific requests and reflect them in the next response. The response unit provides simple responses without using polite language based on the information stored by the memory unit. For example, the generation AI responds with a form such as "I understand" instead of "I get it." The response unit can also generate an appropriate response when the generation AI receives a prompt such as "Please summarize the main points of this sentence." The speed adjustment unit adjusts the speed of the response provided by the response unit. For example, the generation AI can adjust the response speed like YouTube. For example, the speed adjustment unit can respond at 1.5 times the normal speed for customers who request a response at that speed. This enables the AI ​​call center system according to the embodiment to provide personalized responses tailored to customer preferences, increasing repeat calls and reducing call volume and costs. For example, simple responses that do not use honorific language and adjusting the response speed allow customers to use the service more comfortably.

[0030] The memory unit can predict a customer's purchasing patterns and make optimal suggestions the next time they visit. For example, the memory unit's generative AI analyzes a customer's past order history and develops an algorithm to predict purchasing patterns. For example, for a customer who regularly purchases a specific product, new products related to that product will be suggested. This makes it possible to make suggestions based on the customer's purchasing patterns, improving customer satisfaction and purchase rates.

[0031] The memory unit can analyze customer preferences and trends and generate personalized marketing messages. For example, the memory unit's generation AI analyzes a customer's past order history and develops an algorithm to identify the customer's preferences and trends. For example, for a customer who prefers a particular brand or category of products, it generates marketing messages centered on products from that brand or category. This makes it possible to deliver marketing messages based on the customer's preferences and trends, improving customer satisfaction and purchase rates.

[0032] The customer service department can generate optimal simple responses based on the customer's past response history, improving customer satisfaction. For example, the customer service department's generation AI analyzes the customer's past response history and develops an algorithm to generate optimal simple responses. For example, the next response content can be adjusted based on the customer's preferred language and tone in the past. This makes it possible to provide simple responses based on the customer's past response history, improving customer satisfaction.

[0033] The customer service department can analyze customer preferences and trends and increase the variety of simple answers. For example, the customer service department's generative AI analyzes a customer's past order history and develops an algorithm to identify the customer's preferences and trends. For example, for a customer who prefers a specific language or tone, simple answers are generated based on that language or tone. This makes it possible to provide simple answers based on the customer's preferences and trends, improving customer satisfaction.

[0034] The speed adjustment unit predicts the optimal response speed based on the customer's past response history, thereby improving response efficiency. For example, the speed adjustment unit uses a generation AI to analyze the customer's past response history and develop an algorithm to predict the optimal response speed. For example, the next response content is adjusted based on the customer's preferred response speed in the past. This makes it possible to adjust the response speed based on the customer's past response history, improving response efficiency.

[0035] The speed adjustment unit can analyze customer preferences and trends and increase the variety of response speeds. For example, the speed adjustment unit uses a generative AI to analyze a customer's past order history and develop an algorithm to identify customer preferences and trends. For example, for customers who prefer a specific response speed, the unit will focus on that speed. This makes it possible to adjust the response speed based on customer preferences and trends, improving customer satisfaction.

[0036] The speed adjustment unit can provide a customized response according to the customer's preferences. For example, the speed adjustment unit constructs a system in which a generation AI analyzes a customer's past order history and provides a customized response speed according to the customer's preferences. For example, for a customer who prefers a specific response speed, the response will be centered around that speed. This makes it possible to provide a customized response based on the customer's preferences, improving customer satisfaction.

[0037] The speed adjustment unit can provide consistent response across different channels. For example, the speed adjustment unit uses a generation AI to analyze a customer's past order history and build a system that provides consistent response speed across different channels (telephone, chat, video call). For example, the same response can be given to a customer's inquiry over the phone via chat or video call. This enables consistent response across different channels and improves customer satisfaction.

[0038] The customer service department can improve customer satisfaction by generating explanations in the most appropriate genre based on the customer's past response history. For example, the customer service department develops an algorithm in which the generation AI analyzes the customer's past response history and generates explanations in the most appropriate genre. For example, the content of the next explanation can be adjusted based on the genre that the customer has preferred in the past. This makes it possible to provide explanations in the most appropriate genre based on the customer's past response history, improving customer satisfaction.

[0039] The customer service department can analyze customer preferences and trends and increase the variety of examples. For example, the customer service department's generative AI analyzes a customer's past order history and develops an algorithm to identify the customer's preferences and trends. For example, for a customer who likes a specific genre, it generates examples centered around that genre. This makes it possible to vary examples based on the customer's preferences and trends, improving customer satisfaction.

[0040] When explaining by genre, the reception department can provide a customized response according to the customer's preferences. For example, the reception department will build a system in which a generation AI analyzes a customer's past order history and provides an explanation by comparing it to a customized genre according to the customer's preferences. For example, for a customer who likes a specific genre, the explanation will focus on that genre. This makes it possible to provide a customized response based on the customer's preferences, improving customer satisfaction.

[0041] The reception unit can provide responses that correspond to different languages ​​and cultures when explaining using genre analogies. For example, the reception unit constructs a system in which the generation AI provides explanations using genre analogies that correspond to different languages ​​and cultures. For example, if the customer speaks English, the explanation will be given in English using genre analogies. This makes it possible to provide responses that correspond to different languages ​​and cultures, improving customer satisfaction.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The AI ​​call center system can also be equipped with a proposal unit. The proposal unit can analyze a customer's past order history and purchasing patterns to make optimal proposals the next time they contact the customer. For example, for a customer who regularly purchases a particular product, it can propose new products related to that product. It can also make customized proposals based on products and services in which the customer has shown interest in the past. This makes it possible to make proposals based on the customer's purchasing patterns, thereby improving customer satisfaction and purchase rates.

[0044] The AI ​​call center system can further include a feedback collection unit. The feedback collection unit can collect feedback from customers after they have been served and analyze the data. For example, it can send a survey to customers to evaluate whether they are satisfied with the service and store the results in the storage unit. It can also improve the content of the service based on customer feedback. This makes it possible to improve the service based on customer feedback and increase customer satisfaction.

[0045] An AI call center system can also be equipped with a learning unit. This learning unit can continuously learn and improve the response algorithm based on customer response history and feedback. For example, if a customer expresses high satisfaction with a particular response, that response method can be applied to other customers. It can also analyze customer dissatisfaction points and learn how to improve them. This enables continuous learning and improvement, thereby increasing customer satisfaction.

[0046] The AI ​​call center system can further include a multilingual support unit. The multilingual support unit can provide responses in different languages. For example, it can respond according to the customer's language, such as English, Spanish, or Chinese. It can also provide responses that are culturally appropriate. This makes it possible to respond in different languages ​​and cultures, thereby improving customer satisfaction.

[0047] An AI call center system can also be equipped with a data analysis unit. This unit can analyze customer response history and purchasing patterns to optimize marketing strategies. For example, it can analyze the best-selling periods for specific products and customer purchasing trends, and then use that data to implement marketing campaigns. It can also identify areas for improvement in products and services based on customer feedback. This makes it possible to optimize marketing strategies based on data, thereby improving customer satisfaction and sales.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The memory unit stores orders and history. For example, the generation AI stores orders and past customer service history. The memory unit also stores the details of previous orders and specific requests, allowing it to reflect these the next time the customer is served. Step 2: The response unit responds with a simple, non-polite response based on the information stored in the memory unit. For example, the generation AI responds with "I got it" rather than "I understand." The response unit can also generate an appropriate response when the generation AI receives a prompt such as "Please summarize the main points of this sentence." Step 3: The speed adjustment unit adjusts the speed of the response provided by the response unit. For example, the generation AI can adjust the response speed to resemble YouTube. In addition, the speed adjustment unit can respond at 1.5 times the normal speed for customers who request it.

[0050] (Example 2) The AI ​​call center system according to the embodiment of the present invention memorizes orders and history, making customers feel as if the same person will be answering their calls next time. This allows the AI ​​call center system to increase repeat calls and reduce call volume and costs.

[0051] The AI ​​call center system according to the embodiment includes a memory unit, a response unit, and a speed adjustment unit. The memory unit stores orders and history. For example, the generation AI stores orders and past response history. The memory unit can also store, for example, previous order details and specific requests and reflect them in the next response. The response unit provides simple responses without using polite language based on the information stored by the memory unit. For example, the generation AI responds with a form such as "I understand" instead of "I get it." The response unit can also generate an appropriate response when the generation AI receives a prompt such as "Please summarize the main points of this sentence." The speed adjustment unit adjusts the speed of the response provided by the response unit. For example, the generation AI can adjust the response speed like YouTube. For example, the speed adjustment unit can respond at 1.5 times the normal speed for customers who request a response at that speed. This enables the AI ​​call center system according to the embodiment to provide personalized responses tailored to customer preferences, increasing repeat calls and reducing call volume and costs. For example, simple responses that do not use honorific language and adjusting the response speed allow customers to use the service more comfortably.

[0052] The memory unit can analyze the customer's emotional state in real time and store that data together. For example, when the generation AI memorizes a customer's order or history, the memory unit can analyze the customer's emotional state in real time and store that data together. For example, if a customer was dissatisfied with the previous interaction, the next interaction will take steps to resolve that dissatisfaction. This makes it possible to respond based on the customer's emotional state, improving customer satisfaction.

[0053] The memory unit can predict a customer's purchasing patterns and make optimal suggestions the next time they visit. For example, the memory unit's generative AI analyzes a customer's past order history and develops an algorithm to predict purchasing patterns. For example, for a customer who regularly purchases a specific product, new products related to that product will be suggested. This makes it possible to make suggestions based on the customer's purchasing patterns, improving customer satisfaction and purchase rates.

[0054] The memory unit can analyze customer preferences and trends and generate personalized marketing messages. For example, the memory unit's generation AI analyzes a customer's past order history and develops an algorithm to identify the customer's preferences and trends. For example, for a customer who prefers a particular brand or category of products, it generates marketing messages centered on products from that brand or category. This makes it possible to deliver marketing messages based on the customer's preferences and trends, improving customer satisfaction and purchase rates.

[0055] The reception department can analyze the emotional state of the customer in real time and provide simple responses without using honorific language depending on that emotion. For example, the reception department will build a system in which a generative AI analyzes the emotional state of the customer in real time and provides simple responses without using honorific language depending on the emotion. For example, if the customer is relaxed, the reception department will respond using casual language. This makes it possible to provide simple responses based on the customer's emotional state, improving customer satisfaction.

[0056] The customer service department can generate optimal simple responses based on the customer's past response history, improving customer satisfaction. For example, the customer service department's generation AI analyzes the customer's past response history and develops an algorithm to generate optimal simple responses. For example, the next response content can be adjusted based on the customer's preferred language and tone in the past. This makes it possible to provide simple responses based on the customer's past response history, improving customer satisfaction.

[0057] The customer service department can analyze customer preferences and trends and increase the variety of simple answers. For example, the customer service department's generative AI analyzes a customer's past order history and develops an algorithm to identify the customer's preferences and trends. For example, for a customer who prefers a specific language or tone, simple answers are generated based on that language or tone. This makes it possible to provide simple answers based on the customer's preferences and trends, improving customer satisfaction.

[0058] The speed adjustment unit can analyze the emotional state of the customer in real time and adjust the response speed according to that emotion. For example, the speed adjustment unit constructs a system in which a generation AI analyzes the emotional state of the customer in real time and adjusts the response speed according to the emotion. For example, if the customer is in a hurry, the response speed will be increased. This makes it possible to adjust the response speed based on the customer's emotional state, improving customer satisfaction.

[0059] The speed adjustment unit predicts the optimal response speed based on the customer's past response history, thereby improving response efficiency. For example, the speed adjustment unit uses a generation AI to analyze the customer's past response history and develop an algorithm to predict the optimal response speed. For example, the next response content is adjusted based on the customer's preferred response speed in the past. This makes it possible to adjust the response speed based on the customer's past response history, improving response efficiency.

[0060] The speed adjustment unit can analyze customer preferences and trends and increase the variety of response speeds. For example, the speed adjustment unit uses a generative AI to analyze a customer's past order history and develop an algorithm to identify customer preferences and trends. For example, for customers who prefer a specific response speed, the unit will focus on that speed. This makes it possible to adjust the response speed based on customer preferences and trends, improving customer satisfaction.

[0061] The speed adjustment unit uses the emotion estimation function to adjust the response speed according to the customer's emotions, thereby increasing the flexibility of the response. For example, the speed adjustment unit constructs a system in which the generation AI analyzes the customer's emotional state in real time and adjusts the response speed according to the emotion. For example, if the customer is in a hurry, the response speed will be increased. This makes it possible to adjust the response speed based on the customer's emotions, increasing the flexibility of the response.

[0062] The speed adjustment unit can provide a customized response according to the customer's preferences. For example, the speed adjustment unit constructs a system in which a generation AI analyzes a customer's past order history and provides a customized response speed according to the customer's preferences. For example, for a customer who prefers a specific response speed, the response will be centered around that speed. This makes it possible to provide a customized response based on the customer's preferences, improving customer satisfaction.

[0063] The speed adjustment unit can provide consistent response across different channels. For example, the speed adjustment unit uses a generation AI to analyze a customer's past order history and build a system that provides consistent response speed across different channels (telephone, chat, video call). For example, the same response can be given to a customer's inquiry over the phone via chat or video call. This enables consistent response across different channels and improves customer satisfaction.

[0064] The reception department can analyze the emotional state of the customer in real time and provide an explanation in the most appropriate genre based on that emotion. For example, the reception department will build a system in which a generative AI analyzes the emotional state of the customer in real time and provides an explanation in the most appropriate genre based on that emotion. For example, if the customer is relaxed, the explanation will be in the casual genre. This makes it possible to provide an explanation in the genre based on the customer's emotional state, improving customer satisfaction.

[0065] The customer service department can improve customer satisfaction by generating explanations in the most appropriate genre based on the customer's past response history. For example, the customer service department develops an algorithm in which the generation AI analyzes the customer's past response history and generates explanations in the most appropriate genre. For example, the content of the next explanation can be adjusted based on the genre that the customer has preferred in the past. This makes it possible to provide explanations in the most appropriate genre based on the customer's past response history, improving customer satisfaction.

[0066] The customer service department can analyze customer preferences and trends and increase the variety of examples. For example, the customer service department's generative AI analyzes a customer's past order history and develops an algorithm to identify the customer's preferences and trends. For example, for a customer who likes a specific genre, it generates examples centered around that genre. This makes it possible to vary examples based on the customer's preferences and trends, improving customer satisfaction.

[0067] The reception department can use the emotion estimation function to explain things by comparing them to genres that correspond to the customer's emotions, thereby increasing the flexibility of the reception. For example, the reception department will build a system in which a generation AI analyzes the customer's emotional state in real time and explains things by comparing them to genres that correspond to the emotions. For example, if the customer is relaxed, the reception department will explain things by comparing them to casual genres. This makes it possible to explain things by comparing them to genres based on the customer's emotions, increasing the flexibility of the reception.

[0068] When explaining by genre, the reception department can provide a customized response according to the customer's preferences. For example, the reception department will build a system in which a generation AI analyzes a customer's past order history and provides an explanation by comparing it to a customized genre according to the customer's preferences. For example, for a customer who likes a specific genre, the explanation will focus on that genre. This makes it possible to provide a customized response based on the customer's preferences, improving customer satisfaction.

[0069] The reception unit can provide responses that correspond to different languages ​​and cultures when explaining using genre analogies. For example, the reception unit constructs a system in which the generation AI provides explanations using genre analogies that correspond to different languages ​​and cultures. For example, if the customer speaks English, the explanation will be given in English using genre analogies. This makes it possible to provide responses that correspond to different languages ​​and cultures, improving customer satisfaction.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The AI ​​call center system can also be equipped with a speech recognition unit. The speech recognition unit can analyze the tone and speed of a customer's voice to estimate the customer's emotional state. For example, if a customer is angry, the staff can respond in a calm and collected tone. On the other hand, if the customer is relaxed, the staff can respond in a casual tone. This makes it possible to respond based on the customer's emotional state, thereby improving customer satisfaction.

[0072] The AI ​​call center system can also be equipped with a proposal unit. The proposal unit can analyze a customer's past order history and purchasing patterns to make optimal proposals the next time they contact the customer. For example, for a customer who regularly purchases a particular product, it can propose new products related to that product. It can also make customized proposals based on products and services in which the customer has shown interest in the past. This makes it possible to make proposals based on the customer's purchasing patterns, thereby improving customer satisfaction and purchase rates.

[0073] The AI ​​call center system can further include a feedback collection unit. The feedback collection unit can collect feedback from customers after they have been served and analyze the data. For example, it can send a survey to customers to evaluate whether they are satisfied with the service and store the results in the storage unit. It can also improve the content of the service based on customer feedback. This makes it possible to improve the service based on customer feedback and increase customer satisfaction.

[0074] An AI call center system can also be equipped with a learning unit. This learning unit can continuously learn and improve the response algorithm based on customer response history and feedback. For example, if a customer expresses high satisfaction with a particular response, that response method can be applied to other customers. It can also analyze customer dissatisfaction points and learn how to improve them. This enables continuous learning and improvement, thereby increasing customer satisfaction.

[0075] The AI ​​call center system can also use its emotion estimation function to provide music that matches the customer's emotions. For example, it can provide music that helps customers relax while they wait. Also, if a customer is feeling stressed, it can provide music with a relaxing effect. This makes it possible to provide music based on the customer's emotional state, thereby improving customer satisfaction.

[0076] The AI ​​call center system can further include a multilingual support unit. The multilingual support unit can provide responses in different languages. For example, it can respond according to the customer's language, such as English, Spanish, or Chinese. It can also provide responses that are culturally appropriate. This makes it possible to respond in different languages ​​and cultures, thereby improving customer satisfaction.

[0077] The AI ​​call center system can also use its emotion estimation function to generate a response script that matches the customer's emotions. For example, if the customer is angry, it can generate a response script with a calm and collected tone. On the other hand, if the customer is relaxed, it can generate a response script with a casual tone. This makes it possible to generate a response script based on the customer's emotional state, thereby improving customer satisfaction.

[0078] The AI ​​call center system can also use its emotion estimation function to suggest products based on the customer's emotions. For example, if a customer is relaxed, it can suggest products that have a relaxing effect. Or, if a customer is feeling stressed, it can suggest products that have a stress-relieving effect. This makes it possible to suggest products based on the customer's emotional state, thereby improving customer satisfaction and purchase rates.

[0079] The AI ​​call center system can also use its emotion estimation function to send follow-up messages based on the customer's emotions. For example, if a customer is dissatisfied after a call, a follow-up message can be sent to address the customer's dissatisfaction. If the customer is satisfied, a thank-you message can be sent. This makes it possible to follow up based on the customer's emotional state, thereby improving customer satisfaction.

[0080] An AI call center system can also be equipped with a data analysis unit. This unit can analyze customer response history and purchasing patterns to optimize marketing strategies. For example, it can analyze the best-selling periods for specific products and customer purchasing trends, and then use that data to implement marketing campaigns. It can also identify areas for improvement in products and services based on customer feedback. This makes it possible to optimize marketing strategies based on data, thereby improving customer satisfaction and sales.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The memory unit stores orders and history. For example, the generation AI stores orders and past customer service history. The memory unit also stores the details of previous orders and specific requests, allowing it to reflect these the next time the customer is served. Step 2: The response unit responds with a simple, non-polite response based on the information stored in the memory unit. For example, the generation AI responds with "I got it" rather than "I understand." The response unit can also generate an appropriate response when the generation AI receives a prompt such as "Please summarize the main points of this sentence." Step 3: The speed adjustment unit adjusts the speed of the response provided by the response unit. For example, the generation AI can adjust the response speed to resemble YouTube. In addition, the speed adjustment unit can respond at 1.5 times the normal speed for customers who request it.

[0083] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] 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.

[0142] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a storage unit for storing orders and history; a response unit that makes a simple response without using honorific language based on the information stored in the storage unit; A speed adjusting unit that adjusts the speed of the response performed by the response unit. A system characterized by:

2. The storage unit Analyze customer emotional states in real time and store the data together 2. The system of claim 1.

3. The storage unit Predict customer purchasing patterns and make optimal offers the next time you interact with them 2. The system of claim 1.

4. The reception department Analyze the customer's emotional state in real time and respond with simple, non-polite responses based on that emotion.

2. The system of claim 1.

5. The speed adjustment unit Analyze the customer's emotional state in real time and adjust the response speed accordingly 2. The system of claim 1.

Images