System

Abstract

An object of a system according to an embodiment is to increase opportunities for customer service practice and to support self-skill improvement.SOLUTION: A system according to an embodiment includes an avatar, a user input unit, and a response generation unit. The avatar carries the generated AI. The user input unit receives an input from a user. The response generation unit generates a response on the basis of the input received by the user input 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 had the problem of providing few opportunities to practice customer service skills and lacking tools to improve one's skills.

[0005] The system according to the embodiment aims to increase opportunities for customer service practice and support the improvement of one's skills. [Means for solving the problem]

[0006] A system according to an embodiment includes an avatar, a user input unit, and a response generation unit. The avatar is equipped with a generation AI. The user input unit accepts input from a user. The response generation unit generates a response based on the input accepted by the user input unit. [Effects of the Invention]

[0007] The system according to the embodiment can increase opportunities for practicing customer service and help improve one's skills. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) A customer service role-playing system according to an embodiment of the present invention is a system that provides practice opportunities for customer service skills by using an avatar equipped with a generation AI. This allows the customer service role-playing system to increase practice opportunities for customer service skills and improve customer service skills.

[0029] A customer service role-playing system according to an embodiment includes an avatar equipped with a generation AI, a user input unit, and a response generation unit. The generation AI avatar includes a user input unit that accepts input from a user and a response generation unit that generates a response based on the input accepted by the user input unit. The user input unit can accept, for example, text input. The user input unit can also accept, for example, voice input. The response generation unit generates a response using natural language generation technology based on the user input, for example. For example, the generation AI generates a response using an AI model such as GPT-3 or BERT. The response generation unit can also evaluate the quality of responses based on the user input and select an optimal response, for example. This allows the customer service role-playing system to provide more opportunities for customer service practice and improve customer service skills.

[0030] Avatars equipped with generative AI allow users to customize their appearance and voice, allowing them to practice customer service skills for specific customer segments. Avatars equipped with generative AI allow users to customize the appearance of their avatar to practice for specific customer segments. For example, users can set an avatar for elderly customers to practice customer service skills for elderly customers. Avatars equipped with generative AI also allow users to change the avatar's voice to cater to different customer segments. For example, users can set a casual voice for young people or a formal voice for business people. Avatars equipped with generative AI also allow users to change the avatar's clothing and accessories to practice for specific situations. For example, users can set an avatar to suit business or casual settings. This allows users to practice customer service skills for specific customer segments.

[0031] An avatar equipped with generative AI can learn from past interaction history, track the progress of the user's customer service skills, and provide individualized feedback. For example, an avatar equipped with generative AI can analyze a user's past interaction history and evaluate the progress of their customer service skills. For example, it can identify mistakes and areas for improvement in past interactions and provide feedback. An avatar equipped with generative AI can also visually display the progress of the user's customer service skills in graphs and charts and suggest specific areas for improvement. For example, it can evaluate the flow of customer service and the quality of responses. An avatar equipped with generative AI can also create an individual training plan based on the user's interaction history. For example, it can suggest practice that focuses on a specific skill (e.g., handling complaints). This makes it possible to track the progress of the user's customer service skills and provide individualized feedback.

[0032] Avatars equipped with generative AI support multiple languages, allowing users to practice customer service scenarios in different languages. Avatars equipped with generative AI support multiple languages, allowing users to practice customer service scenarios in different languages. For example, practicing customer service scenarios in English, Chinese, Spanish, etc. Avatars equipped with generative AI also generate responses in different languages, allowing users to improve their customer service skills with international customers. For example, practicing customer service scenarios for foreign tourists. Avatars equipped with generative AI also allow users to evaluate customer service skills in a specific language, and the generative AI provides feedback based on that evaluation. For example, it evaluates the accuracy of pronunciation and grammar. This allows users to practice customer service scenarios in different languages.

[0033] Avatars equipped with generative AI provide customer service scenarios specialized for different industries, allowing users to practice customer service skills tailored to specific industries. For example, avatars equipped with generative AI can provide customer service scenarios specialized for the medical industry, allowing medical professionals to practice patient interaction skills. For example, they can practice a scenario for listening to a patient's symptoms. Avatars equipped with generative AI can also provide customer service scenarios specialized for the education industry, allowing teachers to practice parent-teacher interaction skills. For example, they can practice a scenario for parent-teacher conferences. Avatars equipped with generative AI can also generate customer service scenarios suitable for specific industries, taking into account the characteristics of different industries. For example, they can practice a scenario for handling complaints in the food and beverage industry. This allows users to practice customer service skills tailored to specific industries.

[0034] The generative AI can evaluate the user's customer service skills and suggest specific areas for improvement. For example, the generative AI evaluates the user's customer service skills and suggests specific areas for improvement. For example, it provides feedback such as, "Smile more when serving customers" or "Provide specific answers to customer questions." The generative AI also introduces a scoring system to evaluate the user's customer service skills and suggests areas for improvement based on the score. For example, it makes specific suggestions such as, "The flow of customer service is not smooth." The generative AI also evaluates the user's customer service skills and creates an individual training plan. For example, it makes suggestions such as, "In your next practice session, focus on listening more closely to the customer's needs." This allows the user's customer service skills to be evaluated and specific areas for improvement to be suggested.

[0035] The generation AI can provide customizable scenarios so that users can focus on practicing specific skills. For example, the generation AI can provide customizable scenarios that focus on specific skills according to the user's needs. For example, the user can select a complaint handling scenario and practice. The generation AI can also generate customizable scenarios so that users can identify their weaknesses and focus on practicing those skills. For example, it can provide scenarios to improve product description skills. The generation AI can also suggest scenarios that focus on specific skills based on the user's past practice history. For example, it can suggest, "Let's practice complaint handling again, which was difficult in the last practice." This allows users to focus on practicing specific skills.

[0036] The generation AI can add a community function that allows users to share customer service scenarios they have practiced with other users and receive feedback. For example, the generation AI allows users to share customer service scenarios they have practiced on an online platform and receive feedback from other users. For example, other users may provide comments and ratings. The generation AI also allows users to share customer service scenarios with each other through the community function and provide each other with advice. For example, other users may point out areas for improvement and provide specific advice. The generation AI also analyzes users' customer service scenarios and suggests areas for improvement based on feedback from other users. For example, it may make suggestions such as, "Based on feedback from other users, pay attention to this point in your next practice session." This allows users to share customer service scenarios with each other and receive feedback.

[0037] The generation AI can add a function that allows users to save customer service scenarios they have practiced in video format and review them later for self-evaluation. For example, the generation AI can save customer service scenarios they have practiced in video format and review them later for self-evaluation. For example, the generation AI can objectively evaluate their own customer service skills and identify areas for improvement. The generation AI can also play customer service scenarios saved in video format and provide specific feedback. For example, it can give advice such as, "It would be good if you handled this part with a smile." The generation AI can also share customer service scenarios saved in video format with other users and receive feedback. For example, other users can comment and rate the scenarios and point out specific areas for improvement. This allows users to save customer service scenarios they have practiced in video format and review them later for self-evaluation.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The customer service role-playing system can add a community function that enables users to share customer service scenarios they have practiced with other users and receive feedback. For example, a user can share a customer service scenario they have practiced on an online platform and receive feedback from other users. For example, other users can provide comments and ratings. Furthermore, through the community function, users can share customer service scenarios and provide each other with advice. For example, other users can point out areas for improvement and provide specific advice. Furthermore, the system can analyze the user's customer service scenario and suggest areas for improvement based on feedback from other users. For example, it can make suggestions such as, "Based on feedback from other users, pay attention to this point in your next practice." This allows users to share customer service scenarios with each other and receive feedback.

[0040] The customer service role-playing system supports multiple languages, allowing users to practice customer service scenarios in different languages. For example, the generation AI supports multiple languages, allowing users to practice customer service scenarios in different languages. For example, customer service practice in English, Chinese, Spanish, etc. In addition, an avatar generates responses in different languages, allowing users to improve their customer service skills with international customers. For example, practicing customer service scenarios for foreign tourists. Furthermore, the user can evaluate customer service skills in a specific language, and the generation AI can provide feedback based on that evaluation. For example, it evaluates the accuracy of pronunciation and grammar. This allows users to practice customer service scenarios in different languages.

[0041] The customer service role-playing system can provide customizable scenarios that allow users to focus on practicing specific skills. For example, it provides customizable scenarios that focus on specific skills according to the user's needs. For example, a complaint handling scenario can be selected and practiced. It also generates customizable scenarios that allow users to identify their weaknesses and focus on practicing those skills. For example, it provides scenarios to improve product description skills. It also suggests scenarios that focus on specific skills based on the user's past practice history. For example, it makes a suggestion such as, "Let's practice complaint handling again, which was difficult in the last practice." This allows for focused practice of specific skills.

[0042] The customer service role-playing system provides customer service scenarios specialized for different industries, allowing users to practice customer service skills tailored to that industry. For example, the generation AI provides customer service scenarios specialized for the medical industry, allowing medical professionals to practice patient-facing skills, such as practicing a scenario for listening to a patient's symptoms. It also provides customer service scenarios specialized for the education industry, allowing teachers to practice parent-teacher interaction skills, such as practicing a parent-teacher conference scenario. Furthermore, the generation AI takes into account the characteristics of different industries and generates customer service scenarios suitable for specific industries, such as practicing a complaint handling scenario in the food and beverage industry. This allows users to practice customer service skills tailored to a specific industry.

[0043] The customer service role-playing system can evaluate the user's customer service skills and suggest specific areas for improvement. For example, it can evaluate the user's customer service skills and suggest specific areas for improvement. For example, it can provide feedback such as, "Smile more when serving customers" or "Provide specific answers to customer questions." It can also introduce a scoring system to evaluate the user's customer service skills and suggest areas for improvement based on the score. For example, it can make specific suggestions such as, "The flow of customer service is not smooth." It can also evaluate the user's customer service skills and create an individual training plan. For example, it can make suggestions such as, "In your next practice session, focus on listening more closely to the customer's needs." This makes it possible to evaluate the user's customer service skills and suggest specific areas for improvement.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The user input unit accepts input from the user. For example, it can accept text input or voice input. Step 2: The response generation unit generates a response based on the input received by the user input unit. The generation AI generates a response using natural language generation technology, for example, using an AI model such as GPT-3 or BERT. The response generation unit can also evaluate the quality of the response based on the user's input and select the optimal response.

[0046] (Example 2) A customer service role-playing system according to an embodiment of the present invention is a system that provides practice opportunities for customer service skills by using an avatar equipped with a generation AI. This allows the customer service role-playing system to increase practice opportunities for customer service skills and improve customer service skills.

[0047] A customer service role-playing system according to an embodiment includes an avatar equipped with a generation AI, a user input unit, and a response generation unit. The generation AI avatar includes a user input unit that accepts input from a user and a response generation unit that generates a response based on the input accepted by the user input unit. The user input unit can accept, for example, text input. The user input unit can also accept, for example, voice input. The response generation unit generates a response using natural language generation technology based on the user input, for example. For example, the generation AI generates a response using an AI model such as GPT-3 or BERT. The response generation unit can also evaluate the quality of responses based on the user input and select an optimal response, for example. This allows the customer service role-playing system to provide more opportunities for customer service practice and improve customer service skills.

[0048] The response generation unit can estimate a customer's emotions in real time and generate a response based on those emotions. For example, the generation AI analyzes the customer's facial expressions and tone of voice to estimate emotions in real time. For example, if a customer looks dissatisfied, the avatar generates a response such as, "Is there anything I can help you with?" The response generation unit also selects an appropriate response based on the customer's emotional data. For example, if a customer is happy, it generates a response such as, "That's great! Any other questions?" The response generation unit also tracks changes in the customer's emotions in real time and adjusts the response accordingly. For example, if a customer shows no interest at first but becomes interested later, it generates a response such as, "Shall I explain in more detail?" This makes it possible to provide more realistic customer service scenarios.

[0049] Avatars equipped with generative AI allow users to customize their appearance and voice, allowing them to practice customer service skills for specific customer segments. Avatars equipped with generative AI allow users to customize the appearance of their avatar to practice for specific customer segments. For example, users can set an avatar for elderly customers to practice customer service skills for elderly customers. Avatars equipped with generative AI also allow users to change the avatar's voice to cater to different customer segments. For example, users can set a casual voice for young people or a formal voice for business people. Avatars equipped with generative AI also allow users to change the avatar's clothing and accessories to practice for specific situations. For example, users can set an avatar to suit business or casual settings. This allows users to practice customer service skills for specific customer segments.

[0050] An avatar equipped with generative AI can learn from past interaction history, track the progress of the user's customer service skills, and provide individualized feedback. For example, an avatar equipped with generative AI can analyze a user's past interaction history and evaluate the progress of their customer service skills. For example, it can identify mistakes and areas for improvement in past interactions and provide feedback. An avatar equipped with generative AI can also visually display the progress of the user's customer service skills in graphs and charts and suggest specific areas for improvement. For example, it can evaluate the flow of customer service and the quality of responses. An avatar equipped with generative AI can also create an individual training plan based on the user's interaction history. For example, it can suggest practice that focuses on a specific skill (e.g., handling complaints). This makes it possible to track the progress of the user's customer service skills and provide individualized feedback.

[0051] Avatars equipped with generative AI support multiple languages, allowing users to practice customer service scenarios in different languages. Avatars equipped with generative AI support multiple languages, allowing users to practice customer service scenarios in different languages. For example, practicing customer service scenarios in English, Chinese, Spanish, etc. Avatars equipped with generative AI also generate responses in different languages, allowing users to improve their customer service skills with international customers. For example, practicing customer service scenarios for foreign tourists. Avatars equipped with generative AI also allow users to evaluate customer service skills in a specific language, and the generative AI provides feedback based on that evaluation. For example, it evaluates the accuracy of pronunciation and grammar. This allows users to practice customer service scenarios in different languages.

[0052] An avatar equipped with generative AI can estimate a user's emotions, detect stress or anxiety felt during customer service in real time, and provide advice to help them relax. For example, a generative AI avatar can analyze a user's facial expressions and tone of voice to detect stress or anxiety in real time. For example, if a user feels tense, it can provide advice such as, "Take a deep breath and relax." The avatar can also monitor the user's stress level and suggest specific ways to relax. For example, it can suggest, "Take a short break" or "Listen to relaxing music." The avatar can also provide feedback to help reduce the user's stress and anxiety. For example, it can display encouraging messages such as, "Your customer service skills are excellent. Have confidence." This can help reduce the user's stress and anxiety and provide advice to help them relax.

[0053] Avatars equipped with generative AI provide customer service scenarios specialized for different industries, allowing users to practice customer service skills tailored to specific industries. For example, avatars equipped with generative AI can provide customer service scenarios specialized for the medical industry, allowing medical professionals to practice patient interaction skills. For example, they can practice a scenario for listening to a patient's symptoms. Avatars equipped with generative AI can also provide customer service scenarios specialized for the education industry, allowing teachers to practice parent-teacher interaction skills. For example, they can practice a scenario for parent-teacher conferences. Avatars equipped with generative AI can also generate customer service scenarios suitable for specific industries, taking into account the characteristics of different industries. For example, they can practice a scenario for handling complaints in the food and beverage industry. This allows users to practice customer service skills tailored to specific industries.

[0054] The generative AI can evaluate the user's customer service skills and suggest specific areas for improvement. For example, the generative AI evaluates the user's customer service skills and suggests specific areas for improvement. For example, it provides feedback such as, "Smile more when serving customers" or "Provide specific answers to customer questions." The generative AI also introduces a scoring system to evaluate the user's customer service skills and suggests areas for improvement based on the score. For example, it makes specific suggestions such as, "The flow of customer service is not smooth." The generative AI also evaluates the user's customer service skills and creates an individual training plan. For example, it makes suggestions such as, "In your next practice session, focus on listening more closely to the customer's needs." This allows the user's customer service skills to be evaluated and specific areas for improvement to be suggested.

[0055] The generation AI can provide customizable scenarios so that users can focus on practicing specific skills. For example, the generation AI can provide customizable scenarios that focus on specific skills according to the user's needs. For example, the user can select a complaint handling scenario and practice. The generation AI can also generate customizable scenarios so that users can identify their weaknesses and focus on practicing those skills. For example, it can provide scenarios to improve product description skills. The generation AI can also suggest scenarios that focus on specific skills based on the user's past practice history. For example, it can suggest, "Let's practice complaint handling again, which was difficult in the last practice." This allows users to focus on practicing specific skills.

[0056] Generative AI can maintain motivation by estimating a user's emotions and providing positive feedback. For example, generative AI can analyze a user's emotions in real time and provide positive feedback. For example, it can display an encouraging message such as, "That was a great response! Keep it up!". Generative AI can also provide specific advice to maintain motivation based on the user's emotional data. For example, it can make suggestions such as, "Take a short break" or "Do your best in the next practice session." Generative AI can also track changes in the user's emotions and provide timely positive feedback. For example, if a user feels tired during practice, it can display a message such as, "You're almost done. Keep it up!" This helps maintain the user's motivation.

[0057] The generation AI can add a community function that allows users to share customer service scenarios they have practiced with other users and receive feedback. For example, the generation AI allows users to share customer service scenarios they have practiced on an online platform and receive feedback from other users. For example, other users may provide comments and ratings. The generation AI also allows users to share customer service scenarios with each other through the community function and provide each other with advice. For example, other users may point out areas for improvement and provide specific advice. The generation AI also analyzes users' customer service scenarios and suggests areas for improvement based on feedback from other users. For example, it may make suggestions such as, "Based on feedback from other users, pay attention to this point in your next practice session." This allows users to share customer service scenarios with each other and receive feedback.

[0058] Generative AI can analyze the user's emotions and suggest the optimal practice method. For example, generative AI can analyze the user's emotions in real time and suggest the optimal practice method. For example, if the user is nervous, it can provide advice such as, "Take a deep breath to relax." Generative AI can also adjust the progress of practice based on the user's emotional data. For example, if the user is tired, it can suggest, "Take a short break." Generative AI can also track changes in the user's emotions and suggest specific ways to maximize the effectiveness of practice. For example, it can provide advice such as, "Try to be more relaxed next time you practice." This makes it possible to analyze the user's emotions and suggest the optimal practice method.

[0059] The generation AI can add a function that allows users to save customer service scenarios they have practiced in video format and review them later for self-evaluation. For example, the generation AI can save customer service scenarios they have practiced in video format and review them later for self-evaluation. For example, the generation AI can objectively evaluate their own customer service skills and identify areas for improvement. The generation AI can also play customer service scenarios saved in video format and provide specific feedback. For example, it can give advice such as, "It would be good if you handled this part with a smile." The generation AI can also share customer service scenarios saved in video format with other users and receive feedback. For example, other users can comment and rate the scenarios and point out specific areas for improvement. This allows users to save customer service scenarios they have practiced in video format and review them later for self-evaluation.

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

[0061] The customer service role-playing system can estimate the user's emotions, monitor the user's stress level based on the estimated emotions, and suggest specific ways to relax. For example, if the user is tense, it can provide advice such as "Take a deep breath and relax." It can also monitor the user's stress level and suggest specific ways to relax. For example, it can make suggestions such as "Take a short break" or "Listen to relaxing music." It can also provide feedback to reduce the user's stress and anxiety. For example, it can display an encouraging message such as "Your customer service skills are excellent. Have confidence." This can reduce the user's stress and anxiety and provide advice to help them relax.

[0062] The customer service role-playing system can estimate the user's emotions and provide positive feedback based on the estimated emotions to maintain the user's motivation. For example, it can analyze the user's emotions in real time and display an encouraging message such as, "That was a great service! Keep it up!". It can also provide specific advice to maintain motivation based on the user's emotional data. For example, it can make suggestions such as, "Take a short break" or "Do your best in the next practice session." It can also track changes in the user's emotions and provide timely positive feedback. For example, if the user is tired during practice, it can display a message such as, "You're almost done. Keep it up!". This helps maintain the user's motivation.

[0063] The customer service role-playing system can estimate the user's emotions and suggest optimal practice methods based on the estimated emotions. For example, it can analyze the user's emotions in real time and provide advice such as "Take a deep breath to relax" if the user is nervous. It can also adjust the progress of practice based on the user's emotional data. For example, if the user is tired, it can suggest "Take a short break." It can also track changes in the user's emotions and suggest specific methods to maximize the effectiveness of practice. For example, it can provide advice such as "Try to relax more next time you practice." This makes it possible to analyze the user's emotions and suggest optimal practice methods.

[0064] A customer service role-playing system can estimate a user's emotions and, based on the estimated emotions, track the progress of the user's customer service skills and provide personalized feedback. For example, the generation AI analyzes the user's past interaction history to evaluate the progress of the user's customer service skills. For example, it identifies mistakes and areas for improvement in past interactions and provides feedback. The generation AI also visually displays the progress of the user's customer service skills in graphs and charts and suggests specific areas for improvement. For example, it evaluates the flow of customer service and the quality of responses. Furthermore, the generation AI creates an individual training plan based on the user's interaction history. For example, it suggests practice that focuses on specific skills (e.g., handling complaints). This makes it possible to track the progress of the user's customer service skills and provide personalized feedback.

[0065] The customer service role-playing system can add a function to estimate a user's emotions, save a customer service scenario that the user has practiced based on the estimated emotions in video format, and later review it for self-evaluation. For example, a user can save a customer service scenario that they have practiced in video format and review it later for self-evaluation. For example, they can objectively evaluate their own customer service skills and identify areas for improvement. The system can also play back the customer service scenario saved in video format and provide specific feedback. For example, it can give advice such as, "It would be good if you handled this part with a smile." Furthermore, the user can share the customer service scenario saved in video format with other users and receive feedback. For example, other users can comment and rate the scenario, pointing out specific areas for improvement. This allows the user to save the customer service scenario that they have practiced in video format and review it later for self-evaluation.

[0066] The customer service role-playing system can add a community function that enables users to share customer service scenarios they have practiced with other users and receive feedback. For example, a user can share a customer service scenario they have practiced on an online platform and receive feedback from other users. For example, other users can provide comments and ratings. Furthermore, through the community function, users can share customer service scenarios and provide each other with advice. For example, other users can point out areas for improvement and provide specific advice. Furthermore, the system can analyze the user's customer service scenario and suggest areas for improvement based on feedback from other users. For example, it can make suggestions such as, "Based on feedback from other users, pay attention to this point in your next practice." This allows users to share customer service scenarios with each other and receive feedback.

[0067] The customer service role-playing system supports multiple languages, allowing users to practice customer service scenarios in different languages. For example, the generation AI supports multiple languages, allowing users to practice customer service scenarios in different languages. For example, customer service practice in English, Chinese, Spanish, etc. In addition, an avatar generates responses in different languages, allowing users to improve their customer service skills with international customers. For example, practicing customer service scenarios for foreign tourists. Furthermore, the user can evaluate customer service skills in a specific language, and the generation AI can provide feedback based on that evaluation. For example, it evaluates the accuracy of pronunciation and grammar. This allows users to practice customer service scenarios in different languages.

[0068] The customer service role-playing system can provide customizable scenarios that allow users to focus on practicing specific skills. For example, it provides customizable scenarios that focus on specific skills according to the user's needs. For example, a complaint handling scenario can be selected and practiced. It also generates customizable scenarios that allow users to identify their weaknesses and focus on practicing those skills. For example, it provides scenarios to improve product description skills. It also suggests scenarios that focus on specific skills based on the user's past practice history. For example, it makes a suggestion such as, "Let's practice complaint handling again, which was difficult in the last practice." This allows for focused practice of specific skills.

[0069] The customer service role-playing system provides customer service scenarios specialized for different industries, allowing users to practice customer service skills tailored to that industry. For example, the generation AI provides customer service scenarios specialized for the medical industry, allowing medical professionals to practice patient-facing skills, such as practicing a scenario for listening to a patient's symptoms. It also provides customer service scenarios specialized for the education industry, allowing teachers to practice parent-teacher interaction skills, such as practicing a parent-teacher conference scenario. Furthermore, the generation AI takes into account the characteristics of different industries and generates customer service scenarios suitable for specific industries, such as practicing a complaint handling scenario in the food and beverage industry. This allows users to practice customer service skills tailored to a specific industry.

[0070] The customer service role-playing system can evaluate the user's customer service skills and suggest specific areas for improvement. For example, it can evaluate the user's customer service skills and suggest specific areas for improvement. For example, it can provide feedback such as, "Smile more when serving customers" or "Provide specific answers to customer questions." It can also introduce a scoring system to evaluate the user's customer service skills and suggest areas for improvement based on the score. For example, it can make specific suggestions such as, "The flow of customer service is not smooth." It can also evaluate the user's customer service skills and create an individual training plan. For example, it can make suggestions such as, "In your next practice session, focus on listening more closely to the customer's needs." This makes it possible to evaluate the user's customer service skills and suggest specific areas for improvement.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The user input unit accepts input from the user. For example, it can accept text input or voice input. Step 2: The response generation unit generates a response based on the input received by the user input unit. The generation AI generates a response using natural language generation technology, for example, using an AI model such as GPT-3 or BERT. The response generation unit can also evaluate the quality of the response based on the user's input and select the optimal response.

[0073] 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.

[0074] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0086] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0101] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 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. Avatars equipped with AI generation, a user input unit that accepts input from a user; a response generation unit that generates a response based on the input received by the user input unit. A system characterized by:

2. The response generation unit Infer customer sentiment in real time and generate responses based on that sentiment 2. The system of claim 1.

3. The avatar equipped with the above-mentioned generation AI is Customizable appearance and voice, allowing users to practice customer service skills with specific customer segments 2. The system of claim 1.

4. The avatar equipped with the above-mentioned generation AI is Learn from past interaction history, track the progress of the user's customer service skills, and provide personalized feedback 2. The system of claim 1.

5. The avatar equipped with the above-mentioned generation AI is Supports multiple languages, allowing users to practice customer service scenarios in different languages 2. The system of claim 1.

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