System

Abstract

An object of a system according to an embodiment is to efficiently practice customer service and reception and to centrally manage the situation thereof.SOLUTION: A system according to an embodiment includes a user, a generation AI, and an administrator. The user uses the generated AI to practice customer service and reception. The generation AI generates an appropriate response to the user's input. The administrator checks the user's training status in real time and provides feedback.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 was difficult to efficiently practice customer service and customer service skills and to centrally manage the situation.

[0005] The system according to the embodiment aims to efficiently practice customer service and customer service skills and to centrally manage the situation. [Means for solving the problem]

[0006] The system according to the embodiment includes a user, a generation AI, and an administrator. The user practices customer service and customer service using the generation AI. The generation AI generates appropriate responses to the user's input. The administrator monitors the user's practice status in real time and provides feedback. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently practice customer service and customer service skills and can centrally manage the situation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A customer service and customer service practice system according to an embodiment of the present invention is a system that uses a generative AI to easily practice customer service and customer service on an app. In this system, the generative AI generates appropriate responses in response to user input, allowing the user to simulate actual customer service and customer service. Furthermore, a manager can check each user's practice status in real time through the app and provide feedback. This allows the customer service and customer service practice system to allow users to easily practice customer service and customer service on the app, while the manager can centrally manage the status and provide appropriate feedback.

[0029] A customer service training system according to an embodiment includes a generation AI, a user, and an administrator. The generation AI generates an appropriate response in response to a user's input. For example, if a user inputs, "Welcome, what are you looking for?", the generation AI generates a response such as, "Hello, I'm looking for new shoes today." The generation AI can also analyze the user's tone and speed of voice and generate a response based on that data. For example, if the user speaks slowly, the generation AI will respond slowly as well. The generation AI can also learn the user's past practice history and generate individually optimized responses. For example, it can generate more appropriate responses for customer service skills that the user has previously struggled with. Furthermore, the generation AI can use an emotion estimation function to generate responses based on the user's emotional state. For example, if the user is nervous, the generation AI can generate a response that relaxes the user. A user uses the generation AI to practice customer service skills. For example, the user launches an app and begins practicing customer service skills. An administrator can monitor each user's practice status in real time through the app and provide feedback. For example, the administrator can check the user's practice content and number of times, the history of interactions with the generating AI, etc., and provide appropriate feedback. This allows the customer service and reception practice system to have the user practice customer service and reception using the generating AI, and the administrator can check the practice status in real time and provide feedback.

[0030] The generation AI can analyze the tone or speed of the user's voice and generate a response based on that data. For example, the generation AI can analyze the tone and speed of the user's voice in real time and generate a response based on that data. For example, if the user speaks slowly, the generation AI will respond slowly as well. The generation AI can also analyze the tone and speed of the user's voice and generate a response based on that data. For example, if the user speaks in a high-pitched voice, the generation AI will respond in a similarly high-pitched voice. This allows for the generation of more natural responses based on the user's tone and speed of voice.

[0031] The generation AI can learn the user's past practice history and generate individually optimized responses. The generation AI can, for example, learn the user's past practice history and generate individually optimized responses. For example, it can generate more appropriate responses for responses that the user has had difficulty with in the past. The generation AI can also learn the user's past practice history and generate individually optimized responses. For example, it can generate more advanced responses for responses that the user has had difficulty with in the past. This makes it possible to generate individually optimized responses based on the user's past practice history.

[0032] Generative AI can support customer service practice in different languages. For example, it can generate responses in multiple languages, such as English and Chinese, to help users improve their multilingual skills. Generative AI can also support customer service practice in different languages. For example, it can generate responses in languages ​​such as Spanish and French, to help users improve their multilingual skills. This can support customer service practice in different languages.

[0033] The generation AI can analyze the user's gestures or facial expressions and generate a response accordingly. For example, the generation AI can analyze the user's gestures and facial expressions in real time and generate a response based on that data. For example, if the user speaks with a smile, the generation AI will also generate a response that matches the smile. The generation AI can also analyze the user's gestures and facial expressions and generate a response accordingly. For example, if the user waves their hand, the generation AI will also generate a response that looks like they are waving their hand. This makes it possible to generate a response that matches the user's gestures and facial expressions.

[0034] The administrator can analyze the user's practice data and automatically generate an individual training plan. The administrator, for example, builds a system that analyzes the user's practice data and automatically generates an individual training plan. For example, the administrator can propose an optimal training plan based on the user's past practice history. The administrator can also analyze the user's practice data and automatically generate an individual training plan. For example, the administrator can propose a plan based on the user's weaknesses. This allows the administrator to automatically generate an individual training plan based on the user's practice data.

[0035] The administrator can monitor the user's practice status in real time and provide instant feedback. The administrator can, for example, build a system that monitors the user's practice status in real time and provides instant feedback. For example, if the user is unable to make an appropriate response during practice, the administrator can provide advice on the spot. The administrator can also monitor the user's practice status in real time and provide instant feedback. For example, if the user performs well during practice, the administrator can praise the user on the spot. In this way, the administrator can monitor the user's practice status in real time and provide instant feedback.

[0036] Administrators can compare practice data from different user groups and evaluate the training effectiveness of each group. For example, administrators can build a system that compares practice data from different user groups and evaluates the training effectiveness of each group. For example, the practice data from a group of new employees and a group of existing employees can be compared. Administrators can also compare practice data from different user groups and evaluate the training effectiveness of each group. For example, groups by age or occupation can be compared. This allows the practice data from different user groups to be compared and the training effectiveness of each group to be evaluated.

[0037] The administrator can integrate the practice data with other business data to perform a comprehensive performance evaluation. The administrator, for example, builds a system that integrates the practice data with other business data to perform a comprehensive performance evaluation. For example, the administrator integrates the practice data with sales data or customer satisfaction data. The administrator can also integrate the practice data with other business data to perform a comprehensive performance evaluation. For example, the administrator can evaluate the efficiency and effectiveness of the entire business. This allows the administrator to integrate the practice data with other business data to perform a comprehensive performance 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 and customer service practice system can automatically generate specific scenarios based on the user's practice content. For example, it can recreate scenarios that the user found difficult in the past and practice them. The generation AI can also generate more advanced scenarios based on the user's practice history. For example, it can provide more difficult scenarios for customer service that the user excels at. This allows users to practice with a variety of scenarios and improve their skills.

[0040] The customer service training system can provide individualized feedback based on the user's practice data. For example, if a user makes a mistake in a particular customer service interaction, it will suggest specific areas for improvement. The generative AI can also analyze the user's practice data and visualize their practice progress. For example, it can display a graph showing how much the user has improved their skills. This allows the user to continue practicing while getting a sense of their own improvement.

[0041] The customer service training system can compare a user's practice data with other users. For example, a user can compare their practice data with that of other users in the same workplace to see how much their skills have improved. The generative AI can also compare a user's practice data with the industry average. For example, it evaluates how well a user's skills compare to the industry average. This allows the user to objectively understand their own skill level.

[0042] The customer service training system can recommend specific training modules based on the user's practice data. For example, if a user feels they are not good at a particular customer service skill, it can provide a training module specialized for that skill. The generative AI can also set practice priorities based on the user's practice data. For example, it can suggest the customer service skill the user should work on first. This allows users to improve their skills efficiently.

[0043] The customer service and customer service practice system can optimize the frequency and duration of practice based on the user's practice data. For example, it can automatically generate a practice schedule so that the user can practice effectively in a short amount of time. The generation AI can also suggest practice timing based on the user's practice data. For example, it can set the practice time to be when the user can concentrate best. This allows the user to practice efficiently.

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

[0045] Step 1: The user uses the generative AI to practice customer service. For example, the user launches the app and begins practicing customer service. Step 2: The generation AI generates an appropriate response to the user's input. For example, if the user types, "Welcome, what are you looking for?", the generation AI generates a response such as, "Hello, I'm looking for new shoes today." The generation AI can also analyze the user's tone and speed of voice and generate a response based on that data. Furthermore, the generation AI can learn the user's past practice history and generate individually optimized responses. For example, it can generate a more appropriate response for a response that the user has had difficulty with in the past. Furthermore, the generation AI can use its emotion estimation function to generate a response that corresponds to the user's emotional state. For example, if the user is nervous, the generation AI can generate a response that will relax them. Step 3: The administrator checks each user's practice status in real time through the app and provides feedback. For example, the administrator can check the user's practice content, number of times, and interaction history with the generating AI, and provide appropriate feedback.

[0046] (Example 2) A customer service and customer service practice system according to an embodiment of the present invention is a system that uses a generative AI to easily practice customer service and customer service on an app. In this system, the generative AI generates appropriate responses in response to user input, allowing the user to simulate actual customer service and customer service. Furthermore, a manager can check each user's practice status in real time through the app and provide feedback. This allows the customer service and customer service practice system to allow users to easily practice customer service and customer service on the app, while the manager can centrally manage the status and provide appropriate feedback.

[0047] A customer service training system according to an embodiment includes a generation AI, a user, and an administrator. The generation AI generates an appropriate response in response to a user's input. For example, if a user inputs, "Welcome, what are you looking for?", the generation AI generates a response such as, "Hello, I'm looking for new shoes today." The generation AI can also analyze the user's tone and speed of voice and generate a response based on that data. For example, if the user speaks slowly, the generation AI will respond slowly as well. The generation AI can also learn the user's past practice history and generate individually optimized responses. For example, it can generate more appropriate responses for customer service skills that the user has previously struggled with. Furthermore, the generation AI can use an emotion estimation function to generate responses based on the user's emotional state. For example, if the user is nervous, the generation AI can generate a response that relaxes the user. A user uses the generation AI to practice customer service skills. For example, the user launches an app and begins practicing customer service skills. An administrator can monitor each user's practice status in real time through the app and provide feedback. For example, the administrator can check the user's practice content and number of times, the history of interactions with the generating AI, etc., and provide appropriate feedback. This allows the customer service and reception practice system to have the user practice customer service and reception using the generating AI, and the administrator can check the practice status in real time and provide feedback.

[0048] The generation AI can analyze the tone or speed of the user's voice and generate a response based on that data. For example, the generation AI can analyze the tone and speed of the user's voice in real time and generate a response based on that data. For example, if the user speaks slowly, the generation AI will respond slowly as well. The generation AI can also analyze the tone and speed of the user's voice and generate a response based on that data. For example, if the user speaks in a high-pitched voice, the generation AI will respond in a similarly high-pitched voice. This allows for the generation of more natural responses based on the user's tone and speed of voice.

[0049] The generation AI can learn the user's past practice history and generate individually optimized responses. The generation AI can, for example, learn the user's past practice history and generate individually optimized responses. For example, it can generate more appropriate responses for responses that the user has had difficulty with in the past. The generation AI can also learn the user's past practice history and generate individually optimized responses. For example, it can generate more advanced responses for responses that the user has had difficulty with in the past. This makes it possible to generate individually optimized responses based on the user's past practice history.

[0050] The generation AI can use the emotion estimation function to generate a response that corresponds to the user's emotional state. For example, the generation AI can use the emotion estimation function to analyze the user's emotional state in real time and generate a response based on that data. For example, if the user is nervous, the generation AI can generate a response that relaxes the user. The generation AI can also use the emotion estimation function to generate a response that corresponds to the user's emotional state. For example, if the user is happy, the generation AI can generate a response that corresponds to that joy. This makes it possible to generate a response that corresponds to the user's emotional state.

[0051] Generative AI can support customer service practice in different languages. For example, it can generate responses in multiple languages, such as English and Chinese, to help users improve their multilingual skills. Generative AI can also support customer service practice in different languages. For example, it can generate responses in languages ​​such as Spanish and French, to help users improve their multilingual skills. This can support customer service practice in different languages.

[0052] The generation AI can analyze the user's gestures or facial expressions and generate a response accordingly. For example, the generation AI can analyze the user's gestures and facial expressions in real time and generate a response based on that data. For example, if the user speaks with a smile, the generation AI will also generate a response that matches the smile. The generation AI can also analyze the user's gestures and facial expressions and generate a response accordingly. For example, if the user waves their hand, the generation AI will also generate a response that looks like they are waving their hand. This makes it possible to generate a response that matches the user's gestures and facial expressions.

[0053] The generation AI can use the emotion estimation function to monitor the stress level felt by the user during practice and provide advice to relax at the appropriate time. The generation AI, for example, uses the emotion estimation function to monitor the stress level felt by the user during practice in real time. For example, it analyzes the user's facial expressions and voice to calculate the stress level. The generation AI can also use the emotion estimation function to monitor the stress level felt by the user during practice and provide advice to relax at the appropriate time. For example, if the user is feeling high stress, it provides advice to relax. This makes it possible to monitor the user's stress level and provide advice to relax at the appropriate time.

[0054] The administrator can analyze the user's practice data and automatically generate an individual training plan. The administrator, for example, builds a system that analyzes the user's practice data and automatically generates an individual training plan. For example, the administrator can propose an optimal training plan based on the user's past practice history. The administrator can also analyze the user's practice data and automatically generate an individual training plan. For example, the administrator can propose a plan based on the user's weaknesses. This allows the administrator to automatically generate an individual training plan based on the user's practice data.

[0055] The administrator can monitor the user's practice status in real time and provide instant feedback. The administrator can, for example, build a system that monitors the user's practice status in real time and provides instant feedback. For example, if the user is unable to make an appropriate response during practice, the administrator can provide advice on the spot. The administrator can also monitor the user's practice status in real time and provide instant feedback. For example, if the user performs well during practice, the administrator can praise the user on the spot. In this way, the administrator can monitor the user's practice status in real time and provide instant feedback.

[0056] The administrator can use the emotion estimation function to record the user's emotional changes during practice and evaluate the user's emotional growth. For example, the administrator can use the emotion estimation function to build a system that records the user's emotional changes during practice in real time. For example, the administrator can analyze the user's facial expressions and voice and calculate an emotion score. The administrator can also use the emotion estimation function to record the user's emotional changes during practice and evaluate the user's emotional growth. For example, the administrator can evaluate how the user's emotions changed before and after practice. This allows the administrator to record the user's emotional changes during practice and evaluate the user's emotional growth.

[0057] Administrators can compare practice data from different user groups and evaluate the training effectiveness of each group. For example, administrators can build a system that compares practice data from different user groups and evaluates the training effectiveness of each group. For example, the practice data from a group of new employees and a group of existing employees can be compared. Administrators can also compare practice data from different user groups and evaluate the training effectiveness of each group. For example, groups by age or occupation can be compared. This allows the practice data from different user groups to be compared and the training effectiveness of each group to be evaluated.

[0058] The administrator can integrate the practice data with other business data to perform a comprehensive performance evaluation. The administrator, for example, builds a system that integrates the practice data with other business data to perform a comprehensive performance evaluation. For example, the administrator integrates the practice data with sales data or customer satisfaction data. The administrator can also integrate the practice data with other business data to perform a comprehensive performance evaluation. For example, the administrator can evaluate the efficiency and effectiveness of the entire business. This allows the administrator to integrate the practice data with other business data to perform a comprehensive performance evaluation.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The customer service and customer service practice system can automatically generate specific scenarios based on the user's practice content. For example, it can recreate scenarios that the user found difficult in the past and practice them. The generation AI can also generate more advanced scenarios based on the user's practice history. For example, it can provide more difficult scenarios for customer service that the user excels at. This allows users to practice with a variety of scenarios and improve their skills.

[0061] The customer service training system can provide individualized feedback based on the user's practice data. For example, if a user makes a mistake in a particular customer service interaction, it will suggest specific areas for improvement. The generative AI can also analyze the user's practice data and visualize their practice progress. For example, it can display a graph showing how much the user has improved their skills. This allows the user to continue practicing while getting a sense of their own improvement.

[0062] The customer service training system can compare a user's practice data with other users. For example, a user can compare their practice data with that of other users in the same workplace to see how much their skills have improved. The generative AI can also compare a user's practice data with the industry average. For example, it evaluates how well a user's skills compare to the industry average. This allows the user to objectively understand their own skill level.

[0063] The customer service training system can recommend specific training modules based on the user's practice data. For example, if a user feels they are not good at a particular customer service skill, it can provide a training module specialized for that skill. The generative AI can also set practice priorities based on the user's practice data. For example, it can suggest the customer service skill the user should work on first. This allows users to improve their skills efficiently.

[0064] The customer service and customer service practice system can optimize the frequency and duration of practice based on the user's practice data. For example, it can automatically generate a practice schedule so that the user can practice effectively in a short amount of time. The generation AI can also suggest practice timing based on the user's practice data. For example, it can set the practice time to be when the user can concentrate best. This allows the user to practice efficiently.

[0065] The customer service training system can estimate the user's emotions and provide feedback to maintain motivation during practice. For example, if the user feels tired during practice, it can display an encouraging message. The generative AI can also estimate the user's emotions and provide feedback according to the progress of practice. For example, if the user feels anxious in the early stages of practice, it can provide advice to help them relax. This allows the user to continue practicing while maintaining their motivation.

[0066] The customer service training system can estimate the user's emotions and adjust the difficulty of the training. For example, if the user feels stressed during training, the system can lower the difficulty to help them relax. The generative AI can also estimate the user's emotions and adjust the difficulty according to the progress of the training. For example, if the user becomes accustomed to the training, the system can increase the difficulty and provide a more challenging scenario. This allows the user to practice at an appropriate level of difficulty.

[0067] The customer service training system can estimate the user's emotions and customize the feedback based on their emotions. For example, if the user feels confident during practice, it can provide positive feedback. The generative AI can also estimate the user's emotions and adjust the content of the feedback. For example, if the user feels anxious during practice, it can provide an encouraging message. This allows the user to receive feedback that is appropriate to their emotions.

[0068] The customer service training system can estimate the user's emotions and suggest breaks based on the progress of the training. For example, if the user feels tired during training, it will suggest taking a break. The generative AI can also estimate the user's emotions and adjust the timing of breaks based on the progress of the training. For example, if the user is losing concentration, it will suggest taking a short break. This allows the user to continue training while taking breaks at the appropriate time.

[0069] The customer service training system can estimate the user's emotions and provide a summary based on their emotions at the end of the practice session. For example, if the user feels satisfied after the practice session, the system can provide a summary that reinforces that emotion. The generative AI can also estimate the user's emotions and provide advice based on their emotions at the end of the practice session. For example, if the user feels anxious after the practice session, the system can provide positive advice for the next practice session. This allows the user to receive a summary based on their emotions.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The user uses the generative AI to practice customer service. For example, the user launches the app and begins practicing customer service. Step 2: The generation AI generates an appropriate response to the user's input. For example, if the user types, "Welcome, what are you looking for?", the generation AI generates a response such as, "Hello, I'm looking for new shoes today." The generation AI can also analyze the user's tone and speed of voice and generate a response based on that data. Furthermore, the generation AI can learn the user's past practice history and generate individually optimized responses. For example, it can generate a more appropriate response for a response that the user has had difficulty with in the past. Furthermore, the generation AI can use its emotion estimation function to generate a response that corresponds to the user's emotional state. For example, if the user is nervous, the generation AI can generate a response that will relax them. Step 3: The administrator checks each user's practice status in real time through the app and provides feedback. For example, the administrator can check the user's practice content, number of times, and interaction history with the generating AI, and provide appropriate feedback.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 7, a 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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]

[0139] 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. Multiple users practice customer service and customer service using generative AI. A generation AI that generates an appropriate response to the user's input; and an administrator who checks the user's practice status in real time and provides feedback. A system characterized by:

2. The generated AI is Analyzing the tone or rate of the user's voice and generating a response based on that data 2. The system of claim 1.

3. The generated AI is Supporting customer service practice in different languages 2. The system of claim 1.

4. The administrator: Analyze the user's practice data and automatically generate an individual training plan.

2. The system of claim 1.

5. The generated AI is Generate a response according to the emotional state of the user 2. The system of claim 1.

6. The generated AI is Analyzing the user's gestures or facial expressions and generating a response accordingly.

2. The system of claim 1.

7. The administrator: Recording emotional changes during the user's practice and evaluating emotional growth 2. The system of claim 1.

8. The administrator: Add a counseling feature to provide emotional support based on the user's practice data.

2. The system of claim 1.

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