System

Abstract

An object of the system according to the embodiment is to handle telephone calls 24 hours a day, 365 days a year, and to prevent missed calls.SOLUTION: A system according to an embodiment includes a call reception unit, a response generation unit, and a learning unit. The telephone reception unit receives a telephone call 24 hours a day, 365 days a year. The response generation unit generates an appropriate answer to the call received by the call reception unit. The learning unit increases the response content based on the answer generated by the response generation 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] With conventional technology, it is difficult to respond to calls outside of business hours or when the line is busy, and there is a risk that calls will be missed.

[0005] The system according to the embodiment aims to respond to calls 24 hours a day, 365 days a year, and to prevent missed calls. [Means for solving the problem]

[0006] The system according to the embodiment includes a call receiving unit, a response generation unit, and a learning unit. The call receiving unit receives calls 24 hours a day, 365 days a year. The response generation unit generates appropriate responses to calls received by the call receiving unit. The learning unit increases the content of responses based on the responses generated by the response generation unit. [Effects of the Invention]

[0007] The system according to the embodiment responds to calls 24 hours a day, 365 days a year, preventing missed calls. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 ​​telephone number system according to an embodiment of the present invention uses AI to automate telephone responses, operating 24 hours a day, 365 days a year. This allows the AI ​​telephone number system to avoid missing calls outside of business hours or when calls are busy, and to turn these calls into sales, such as reservations. Furthermore, by using AI to automatically respond and learn, and increasing the number of responses it can make, people can spend almost no time answering calls, allowing them to focus on their actual work.

[0029] An AI telephone number system according to an embodiment includes a call reception unit, a response generation unit, and a learning unit. The call reception unit receives calls 24 hours a day, 365 days a year. For example, it can automatically receive calls outside of business hours or when the line is busy. The response generation unit generates appropriate responses to calls received by the call reception unit. For example, the generation AI uses speech recognition technology to understand what a customer is saying and generate appropriate responses. The generation AI uses text generation AI (e.g., LLM) to generate answers to customer questions. The generation AI can also use multimodal generation AI to generate answers to customer questions. The learning unit expands the response content based on the answers generated by the response generation unit. For example, the generation AI can learn frequently asked questions and information about new menus based on past inquiries, allowing it to respond quickly and accurately to future inquiries. As a result, the AI ​​telephone number system according to an embodiment can respond to calls 24 hours a day, 365 days a year and expand the response content.

[0030] The telephone reception unit can automatically accept calls even outside of business hours or when the line is busy. For example, AI can analyze the tone and speed of the customer's voice in real time to determine the level of urgency. This allows the unit to accept calls even outside of business hours or when the line is busy.

[0031] The response generation unit analyzes the tone and speed of the customer's voice, determines the level of urgency, and responds with priority. The response generation unit, for example, analyzes the tone and speed of the customer's voice in real time and determines the level of urgency. For example, if it detects a voice that sounds rushed or has a high tone, it determines that the level of urgency is high and responds with priority. This makes it possible to determine the level of urgency of the customer and respond with priority.

[0032] The response generation unit can refer to the call history and provide special treatment to repeat customers. The response generation unit can refer to the call history and provide special treatment to repeat customers, for example, by giving priority to repeat customers and providing them with special offers. This makes it possible to provide special treatment to repeat customers.

[0033] The telephone reception department can handle not only telephone calls but also SMS and chat apps. For example, if a customer cannot answer the phone, they can respond via SMS. This allows them to handle not only telephone calls but also SMS and chat apps.

[0034] The telephone reception unit can acquire location information of the customer and provide information on the nearest store. For example, the telephone reception unit acquires location information of the customer and provides information on the nearest store. For example, when the customer transmits their current location, the address and telephone number of the nearest store are provided. This allows the customer's location information to be acquired and information on the nearest store to be provided.

[0035] The learning unit can learn from FAQ databases in different industries to enable it to respond to a wide range of questions.The learning unit can, for example, learn from FAQ databases in different industries to enable it to respond to a wide range of questions.For example, it can learn from FAQ databases in not only the food and beverage industry, but also FAQs in the medical and education industries.This makes it possible to learn from FAQ databases in different industries to enable it to respond to a wide range of questions.

[0036] The learning unit can predict and prepare for the next inquiry based on the content of the customer's past inquiries. The learning unit, for example, predicts and prepares for the next inquiry based on the content of the customer's past inquiries. For example, the learning unit predicts the next inquiry based on the content of the customer's past inquiries. This makes it possible to predict and prepare for the next inquiry based on the content of the customer's past inquiries.

[0037] The learning unit can also automatically respond to emails and SNS messages, realizing integrated communication.The learning unit can also automatically respond to emails and SNS messages, realizing integrated communication.For example, if a customer makes an inquiry by email, it will automatically respond.This allows for automatic responses to emails and SNS messages, realizing integrated communication.

[0038] The learning unit can learn store inventory information in real time and respond according to the inventory situation. The learning unit, for example, can learn store inventory information in real time and respond according to the inventory situation. For example, for products that are low in stock, it can suggest alternative products. This allows the learning unit to learn store inventory information in real time and respond according to the inventory situation.

[0039] The response generation unit can propose the optimal reservation time based on the customer's past reservation history. The response generation unit can propose the optimal reservation time based on the customer's past reservation history, for example. For example, it can propose the next reservation time based on the time slots that the customer has made reservations in the past. This makes it possible to propose the optimal reservation time based on the customer's past reservation history.

[0040] The response generation unit can link with multiple reservation systems and check availability in real time. The response generation unit, for example, links with multiple reservation systems and checks availability in real time. For example, it integrates data from different reservation platforms and centrally manages availability. This allows linking with multiple reservation systems and checking availability in real time.

[0041] The response generation unit can have a function of giving priority to accepting reservations for events or special days. The response generation unit, for example, gives priority to accepting reservations for events or special days. For example, reservations for special days such as Christmas or Valentine's Day are given priority. This allows reservations for events or special days to be given priority.

[0042] The response generation unit can work in conjunction with the customer's calendar to suggest reservations based on the schedule. The response generation unit, for example, works in conjunction with the customer's calendar to suggest reservations based on the schedule. For example, it can suggest reservations based on the customer's available time slots. This makes it possible to work in conjunction with the customer's calendar to suggest reservations based on the schedule.

[0043] The learning unit can analyze the customer's purchase history and make personalized suggestions. The learning unit can, for example, analyze the customer's purchase history and make personalized suggestions. For example, it can suggest related products based on products that the customer has purchased in the past. This allows the customer's purchase history to be analyzed and personalized suggestions to be made.

[0044] The learning unit can analyze customer feedback and provide insights for improving services. The learning unit, for example, analyzes customer feedback and provides insights for improving services. For example, it identifies areas for improvement in services based on customer opinions. This makes it possible to analyze customer feedback and provide insights for improving services.

[0045] The learning unit can analyze the customer's social media activity and make suggestions based on their interests. The learning unit, for example, analyzes the customer's social media activity and makes suggestions based on their interests. For example, suggestions are made based on products that the customer mentioned on social media. This makes it possible to analyze the customer's social media activity and make suggestions based on their interests.

[0046] The learning department manages customer life events (birthdays, anniversaries, etc.) and can provide special offers. The learning department manages customer life events (birthdays, anniversaries, etc.) and can provide special offers. For example, a special discount can be provided on a customer's birthday. This makes it possible to manage customer life events and provide special offers.

[0047] The response generation unit learns the cultural background of each region and can use appropriate language and expressions. The response generation unit, for example, learns the cultural background of each region and can use appropriate language and expressions. For example, it generates a response that takes the culture of each region into consideration. This allows it to learn the cultural background of each region and can use appropriate language and expressions.

[0048] The response generation unit can understand the differences in nuance between different languages ​​and perform accurate translations. The response generation unit can, for example, understand the differences in nuance between different languages ​​and perform accurate translations. For example, it can perform translations that take into account the subtle nuances of each language. This allows the response generation unit to understand the differences in nuance between different languages ​​and perform accurate translations.

[0049] The response generation unit can respond not only to multiple languages ​​but also to multiple cultures, and can provide a response that takes cultural differences into consideration.The response generation unit can respond not only to multiple languages ​​but also to multiple cultures, and can provide a response that takes cultural differences into consideration.For example, if a customer has a specific cultural background, a response that takes that culture into consideration is provided.This makes it possible to provide not only multilingual support but also multicultural support, and can provide a response that takes cultural differences into consideration.

[0050] The response generation unit has a foreign language learning function and can respond according to the customer's language skill. The response generation unit, for example, has a foreign language learning function and can respond according to the customer's language skill. For example, if the customer speaks beginner's level English, the response will be in simple terms. This allows the foreign language learning function to respond according to the customer's language skill.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The AI ​​telephone answering system is also equipped with a voice recognition unit, which can analyze the characteristics of a customer's voice and provide individualized responses. For example, by analyzing the tone and accent of the customer's voice and using regional vocabulary and expressions, it can generate a more friendly response. It can also identify repeat customers by comparing their voice characteristics with past call history and provide special responses. Furthermore, the voice recognition unit can detect changes in the customer's voice and infer changes in their physical condition or mood, allowing it to provide appropriate responses.

[0053] The AI ​​telephone number system also has a location information acquisition unit that can provide information based on a customer's current location. For example, when a customer submits their current location, the system can provide the address and phone number of the nearest store. It can also provide information about special offers and events in each area based on the location information. Furthermore, the location information acquisition unit can analyze a customer's movement history and make personalized suggestions based on information about stores they have visited in the past. For example, it can encourage a customer to return by suggesting special offers from stores they have visited in the past.

[0054] The AI ​​telephone number system can also learn from FAQ databases of different industries, enabling it to respond to a wider range of questions. For example, by learning from FAQs not only for the food and beverage industry but also for healthcare and education, it can respond to inquiries from a variety of industries. It can also learn the terminology and trends of different industries and generate more specialized responses. Furthermore, it can integrate FAQ databases from different industries, enabling it to respond to complex questions that span multiple industries. For example, it can generate appropriate responses to questions related to healthcare and education.

[0055] The AI ​​telephone number system can also link with the customer's calendar to suggest appointments based on their schedule. For example, it can suggest appointments based on the customer's available time slots. It can also suggest suitable appointment times before or after important events or meetings based on the customer's calendar. Furthermore, by linking with the customer's calendar, it can automatically send reminder messages to encourage them to confirm or change their reservations. For example, by sending a reminder message the day before an appointment, it can help customers remember to make the appointment.

[0056] The AI ​​phone number system can also analyze customers' social media activity to make suggestions based on their interests. For example, suggestions can be made based on products that customers mention on social media. It can also suggest related products and services based on the brands and influencers that customers follow. Furthermore, analyzing customers' social media activity can identify trends and popular products and keep customers up to date with the latest information. For example, it can keep customers engaged by providing articles and news related to topics that interest them.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The telephone reception department accepts calls 24 hours a day, 365 days a year. For example, it can automatically accept calls outside of business hours or when the line is busy. Step 2: The response generation unit generates an appropriate response to the call received by the call reception unit. For example, the generation AI uses speech recognition technology to understand what the customer is saying and generate an appropriate response. The generation AI uses text generation AI (e.g., LLM) to generate responses to customer questions. The generation AI can also use multimodal generation AI to generate responses to customer questions. Step 3: The learning unit adds more response content based on the answers generated by the response generation unit. For example, the generation AI can learn frequently asked questions and information about new menu items based on past inquiries, allowing it to respond quickly and accurately to future inquiries.

[0059] (Example 2) The AI ​​telephone number system according to an embodiment of the present invention uses AI to automate telephone responses, operating 24 hours a day, 365 days a year. This allows the AI ​​telephone number system to avoid missing calls outside of business hours or when calls are busy, and to turn these calls into sales, such as reservations. Furthermore, by using AI to automatically respond and learn, and increasing the number of responses it can make, people can spend almost no time answering calls, allowing them to focus on their actual work.

[0060] An AI telephone number system according to an embodiment includes a call reception unit, a response generation unit, and a learning unit. The call reception unit receives calls 24 hours a day, 365 days a year. For example, it can automatically receive calls outside of business hours or when the line is busy. The response generation unit generates appropriate responses to calls received by the call reception unit. For example, the generation AI uses speech recognition technology to understand what a customer is saying and generate appropriate responses. The generation AI uses text generation AI (e.g., LLM) to generate answers to customer questions. The generation AI can also use multimodal generation AI to generate answers to customer questions. The learning unit expands the response content based on the answers generated by the response generation unit. For example, the generation AI can learn frequently asked questions and information about new menus based on past inquiries, allowing it to respond quickly and accurately to future inquiries. As a result, the AI ​​telephone number system according to an embodiment can respond to calls 24 hours a day, 365 days a year and expand the response content.

[0061] The telephone reception unit can automatically accept calls even outside of business hours or when the line is busy. For example, AI can analyze the tone and speed of the customer's voice in real time to determine the level of urgency. This allows the unit to accept calls even outside of business hours or when the line is busy.

[0062] The response generation unit analyzes the tone and speed of the customer's voice, determines the level of urgency, and responds with priority. The response generation unit, for example, analyzes the tone and speed of the customer's voice in real time and determines the level of urgency. For example, if it detects a voice that sounds rushed or has a high tone, it determines that the level of urgency is high and responds with priority. This makes it possible to determine the level of urgency of the customer and respond with priority.

[0063] The response generation unit can refer to the call history and provide special treatment to repeat customers. The response generation unit can refer to the call history and provide special treatment to repeat customers, for example, by giving priority to repeat customers and providing them with special offers. This makes it possible to provide special treatment to repeat customers.

[0064] The response generation unit can use the emotion estimation function to determine the emotional state of the customer and respond appropriately. The response generation unit, for example, uses the emotion estimation function to determine the emotional state of the customer in real time. For example, if the customer is angry, the response generation unit responds calmly. This allows the emotional state of the customer to be determined and an appropriate response to be taken.

[0065] The telephone reception department can handle not only telephone calls but also SMS and chat apps. For example, if a customer cannot answer the phone, they can respond via SMS. This allows them to handle not only telephone calls but also SMS and chat apps.

[0066] The telephone reception unit can acquire location information of the customer and provide information on the nearest store. For example, the telephone reception unit acquires location information of the customer and provides information on the nearest store. For example, when the customer transmits their current location, the address and telephone number of the nearest store are provided. This allows the customer's location information to be acquired and information on the nearest store to be provided.

[0067] The response generation unit can use the emotion estimation function to generate a response that will make the customer feel positive emotions. The response generation unit, for example, uses the emotion estimation function to generate a response that will make the customer feel positive emotions. For example, it uses language and a tone that will make the customer happy. This makes it possible to generate a response that will make the customer feel positive emotions.

[0068] The learning unit can learn from FAQ databases in different industries to enable it to respond to a wide range of questions.The learning unit can, for example, learn from FAQ databases in different industries to enable it to respond to a wide range of questions.For example, it can learn from FAQ databases in not only the food and beverage industry, but also FAQs in the medical and education industries.This makes it possible to learn from FAQ databases in different industries to enable it to respond to a wide range of questions.

[0069] The learning unit can predict and prepare for the next inquiry based on the content of the customer's past inquiries. The learning unit, for example, predicts and prepares for the next inquiry based on the content of the customer's past inquiries. For example, the learning unit predicts the next inquiry based on the content of the customer's past inquiries. This makes it possible to predict and prepare for the next inquiry based on the content of the customer's past inquiries.

[0070] The learning unit uses the emotion estimation function to learn responses according to the customer's emotions and can respond appropriately. The learning unit, for example, uses the emotion estimation function to learn responses according to the customer's emotions and can respond appropriately. For example, if the customer is angry, the learning unit learns a calm response. This allows the learning unit to learn responses according to the customer's emotions and can respond appropriately.

[0071] The learning unit can also automatically respond to emails and SNS messages, realizing integrated communication.The learning unit can also automatically respond to emails and SNS messages, realizing integrated communication.For example, if a customer makes an inquiry by email, it will automatically respond.This allows for automatic responses to emails and SNS messages, realizing integrated communication.

[0072] The learning unit can learn store inventory information in real time and respond according to the inventory situation. The learning unit, for example, can learn store inventory information in real time and respond according to the inventory situation. For example, for products that are low in stock, it can suggest alternative products. This allows the learning unit to learn store inventory information in real time and respond according to the inventory situation.

[0073] The learning unit can use the emotion estimation function to suggest promotions and campaigns based on the customer's emotions. The learning unit, for example, uses the emotion estimation function to suggest promotions and campaigns based on the customer's emotions. For example, if the customer is happy, the learning unit suggests a special offer. This makes it possible to suggest promotions and campaigns based on the customer's emotions.

[0074] The response generation unit can propose the optimal reservation time based on the customer's past reservation history. The response generation unit can propose the optimal reservation time based on the customer's past reservation history, for example. For example, it can propose the next reservation time based on the time slots that the customer has made reservations in the past. This makes it possible to propose the optimal reservation time based on the customer's past reservation history.

[0075] The response generation unit can link with multiple reservation systems and check availability in real time. The response generation unit, for example, links with multiple reservation systems and checks availability in real time. For example, it integrates data from different reservation platforms and centrally manages availability. This allows linking with multiple reservation systems and checking availability in real time.

[0076] The response generation unit can use the emotion estimation function to generate a reservation confirmation message that corresponds to the emotion of the customer. The response generation unit, for example, uses the emotion estimation function to generate a reservation confirmation message that corresponds to the emotion of the customer. For example, if the customer is happy, a positive message is sent. In this way, a reservation confirmation message that corresponds to the emotion of the customer can be generated.

[0077] The response generation unit can have a function of giving priority to accepting reservations for events or special days. The response generation unit, for example, gives priority to accepting reservations for events or special days. For example, reservations for special days such as Christmas or Valentine's Day are given priority. This allows reservations for events or special days to be given priority.

[0078] The response generation unit can work in conjunction with the customer's calendar to suggest reservations based on the schedule. The response generation unit, for example, works in conjunction with the customer's calendar to suggest reservations based on the schedule. For example, it can suggest reservations based on the customer's available time slots. This makes it possible to work in conjunction with the customer's calendar to suggest reservations based on the schedule.

[0079] The learning unit can analyze the customer's purchase history and make personalized suggestions. The learning unit can, for example, analyze the customer's purchase history and make personalized suggestions. For example, it can suggest related products based on products that the customer has purchased in the past. This allows the customer's purchase history to be analyzed and personalized suggestions to be made.

[0080] The learning unit can analyze customer feedback and provide insights for improving services. The learning unit, for example, analyzes customer feedback and provides insights for improving services. For example, it identifies areas for improvement in services based on customer opinions. This makes it possible to analyze customer feedback and provide insights for improving services.

[0081] The learning unit can use the emotion estimation function to make service improvement proposals based on the customer's emotions. The learning unit, for example, uses the emotion estimation function to make service improvement proposals based on the customer's emotions. For example, if a customer is dissatisfied, the learning unit identifies the cause and makes improvement proposals. This makes it possible to make service improvement proposals based on the customer's emotions.

[0082] The learning unit can analyze the customer's social media activity and make suggestions based on their interests. The learning unit, for example, analyzes the customer's social media activity and makes suggestions based on their interests. For example, suggestions are made based on products that the customer mentioned on social media. This makes it possible to analyze the customer's social media activity and make suggestions based on their interests.

[0083] The learning department manages customer life events (birthdays, anniversaries, etc.) and can provide special offers. The learning department manages customer life events (birthdays, anniversaries, etc.) and can provide special offers. For example, a special discount can be provided on a customer's birthday. This makes it possible to manage customer life events and provide special offers.

[0084] The learning unit can use the emotion estimation function to design a loyalty program based on the emotions of the customer. For example, the learning unit uses the emotion estimation function to design a loyalty program based on the emotions of the customer. For example, if the customer is satisfied, special points are awarded. In this way, it is possible to design a loyalty program based on the emotions of the customer.

[0085] The response generation unit learns the cultural background of each region and can use appropriate language and expressions. The response generation unit, for example, learns the cultural background of each region and can use appropriate language and expressions. For example, it generates a response that takes the culture of each region into consideration. This allows it to learn the cultural background of each region and can use appropriate language and expressions.

[0086] The response generation unit can understand the differences in nuance between different languages ​​and perform accurate translations. The response generation unit can, for example, understand the differences in nuance between different languages ​​and perform accurate translations. For example, it can perform translations that take into account the subtle nuances of each language. This allows the response generation unit to understand the differences in nuance between different languages ​​and perform accurate translations.

[0087] The response generation unit can use the emotion estimation function to understand emotional expressions in different languages ​​and respond appropriately. The response generation unit can use the emotion estimation function to understand emotional expressions in different languages ​​and respond appropriately. For example, if a customer expresses anger in English, the response generation unit can respond calmly. This makes it possible to understand emotional expressions in different languages ​​and respond appropriately.

[0088] The response generation unit can respond not only to multiple languages ​​but also to multiple cultures, and can provide a response that takes cultural differences into consideration.The response generation unit can respond not only to multiple languages ​​but also to multiple cultures, and can provide a response that takes cultural differences into consideration.For example, if a customer has a specific cultural background, a response that takes that culture into consideration is provided.This makes it possible to provide not only multilingual support but also multicultural support, and can provide a response that takes cultural differences into consideration.

[0089] The response generation unit has a foreign language learning function and can respond according to the customer's language skill. The response generation unit, for example, has a foreign language learning function and can respond according to the customer's language skill. For example, if the customer speaks beginner's level English, the response will be in simple terms. This allows the foreign language learning function to respond according to the customer's language skill.

[0090] The response generation unit can use the emotion estimation function to analyze emotional responses in different languages ​​and generate an optimal response. For example, the response generation unit uses the emotion estimation function to analyze emotional responses in different languages ​​and generate an optimal response. For example, if a customer expresses joy in French, a positive response is generated. This makes it possible to analyze emotional responses in different languages ​​and generate an optimal response.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The AI ​​telephone answering system is also equipped with a voice recognition unit, which can analyze the characteristics of a customer's voice and provide individualized responses. For example, by analyzing the tone and accent of the customer's voice and using regional vocabulary and expressions, it can generate a more friendly response. It can also identify repeat customers by comparing their voice characteristics with past call history and provide special responses. Furthermore, the voice recognition unit can detect changes in the customer's voice and infer changes in their physical condition or mood, allowing it to provide appropriate responses.

[0093] The AI ​​telephone answering system can also use emotion estimation to generate responses based on the customer's emotions. For example, if the customer is feeling anxious, it can use words and a tone that gives a sense of security. If the customer is happy, it can respond with empathy, thereby improving customer satisfaction. Furthermore, it can also use emotion estimation to suggest promotions and campaigns based on the customer's emotions. For example, if the customer has positive emotions, it can suggest special offers to encourage them to make a purchase.

[0094] The AI ​​telephone number system also has a location information acquisition unit that can provide information based on a customer's current location. For example, when a customer submits their current location, the system can provide the address and phone number of the nearest store. It can also provide information about special offers and events in each area based on the location information. Furthermore, the location information acquisition unit can analyze a customer's movement history and make personalized suggestions based on information about stores they have visited in the past. For example, it can encourage a customer to return by suggesting special offers from stores they have visited in the past.

[0095] The AI ​​telephone number system can also use its emotion estimation function to generate reservation confirmation messages based on the customer's emotions. For example, if the customer is happy, a positive message can be sent. If the customer is feeling anxious, a message that gives them a sense of security can be sent, thereby improving customer satisfaction. Furthermore, the emotion estimation function can also be used to generate reminder messages based on the customer's emotions. For example, if the customer is nervous, a message that encourages them to relax can be sent to reduce their anxiety.

[0096] The AI ​​telephone number system can also learn from FAQ databases of different industries, enabling it to respond to a wider range of questions. For example, by learning from FAQs not only for the food and beverage industry but also for healthcare and education, it can respond to inquiries from a variety of industries. It can also learn the terminology and trends of different industries and generate more specialized responses. Furthermore, it can integrate FAQ databases from different industries, enabling it to respond to complex questions that span multiple industries. For example, it can generate appropriate responses to questions related to healthcare and education.

[0097] The AI ​​telephone number system can also use its emotion estimation function to design loyalty programs based on customer emotions. For example, if a customer is satisfied, it can award them extra points. If a customer is dissatisfied, it can identify the cause and make suggestions for improvement, thereby improving customer satisfaction. Furthermore, it can also use the emotion estimation function to suggest special offers and campaigns based on customer emotions. For example, if a customer has positive emotions, it can increase loyalty by offering them extra discounts.

[0098] The AI ​​telephone number system can also link with the customer's calendar to suggest appointments based on their schedule. For example, it can suggest appointments based on the customer's available time slots. It can also suggest suitable appointment times before or after important events or meetings based on the customer's calendar. Furthermore, by linking with the customer's calendar, it can automatically send reminder messages to encourage them to confirm or change their reservations. For example, by sending a reminder message the day before an appointment, it can help customers remember to make the appointment.

[0099] The AI ​​telephone number system can also use its emotion estimation function to make suggestions for improving services based on customer emotions. For example, if a customer is dissatisfied, the system can identify the cause and make suggestions for improvement. If a customer is satisfied, the system can analyze the reasons for this and provide similar service to other customers, thereby improving overall customer satisfaction. Furthermore, the emotion estimation function can also be used to collect feedback based on customer emotions and identify areas for improvement in the service. For example, if a customer is dissatisfied with a particular service, specific suggestions can be made to improve that service.

[0100] The AI ​​phone number system can also analyze customers' social media activity to make suggestions based on their interests. For example, suggestions can be made based on products that customers mention on social media. It can also suggest related products and services based on the brands and influencers that customers follow. Furthermore, analyzing customers' social media activity can identify trends and popular products and keep customers up to date with the latest information. For example, it can keep customers engaged by providing articles and news related to topics that interest them.

[0101] The AI ​​telephone answering system can also use its emotion estimation function to analyze emotional responses in different languages ​​and generate optimal responses. For example, if a customer expresses joy in French, a positive response can be generated. On the other hand, if a customer expresses anger in English, a calm response can be generated to reduce the customer's dissatisfaction. Furthermore, the emotion estimation function can be used to understand emotional expressions in different languages ​​and generate responses that take cultural background into consideration. For example, if a customer has a specific cultural background, customer satisfaction can be improved by generating responses that take that culture into consideration.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The telephone reception department accepts calls 24 hours a day, 365 days a year. For example, it can automatically accept calls outside of business hours or when the line is busy. Step 2: The response generation unit generates an appropriate response to the call received by the call reception unit. For example, the generation AI uses speech recognition technology to understand what the customer is saying and generate an appropriate response. The generation AI uses text generation AI (e.g., LLM) to generate responses to customer questions. The generation AI can also use multimodal generation AI to generate responses to customer questions. Step 3: The learning unit adds more response content based on the answers generated by the response generation unit. For example, the generation AI can learn frequently asked questions and information about new menu items based on past inquiries, allowing it to respond quickly and accurately to future inquiries.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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 telephone reception department that accepts calls 24 hours a day, 365 days a year, a response generation unit that generates an appropriate response to the call received by the call reception unit; a learning unit that increases response content based on the answers generated by the response generation unit; A system characterized by:

2. The telephone reception unit In addition to phone support, we also provide support via SMS and chat apps. The system of claim 1 .

3. The response generation unit View call history and give special treatment to repeat callers The system of claim 1 .

4. The learning unit Predict and prepare for the next customer inquiry based on past inquiries The system of claim 1 .

5. The response generation unit Use emotion estimation to determine the customer's emotional state and respond appropriately The system of claim 1 .

6. The learning unit Using emotion estimation functionality, the system learns responses based on customer emotions and provides appropriate responses. The system of claim 1 .

7. The response generation unit Use emotion estimation to make suggestions that will make customers feel positive about booking The system of claim 1 .

8. The response generation unit Emotion estimation function to understand emotions expressed in different languages ​​and respond appropriately The system of claim 1 .

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