system

Abstract

The system according to the embodiment aims to enable customer service role-playing to be performed anytime and anywhere. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a dialogue unit, and an evaluation unit. The reception unit receives instructions for a customer service role-play from a user. The generation unit analyzes the instructions received by the reception unit and generates a customer service scenario. The dialogue unit engages in a dialogue with the user based on the scenario generated by the generation unit. The evaluation unit evaluates the results of the dialogue conducted by the dialogue unit and provides feedback.

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, role-playing to improve customer service skills was limited in time and location.

[0005] The system according to the embodiment aims to enable customer service role-playing to be performed anytime and anywhere. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a dialogue unit, and an evaluation unit. The reception unit receives instructions for a customer service role-play from a user. The generation unit analyzes the instructions received by the reception unit and generates a customer service scenario. The dialogue unit engages in a dialogue with the user based on the scenario generated by the generation unit. The evaluation unit evaluates the results of the dialogue conducted by the dialogue unit and provides feedback. [Effects of the Invention]

[0007] The system according to the embodiment allows customer service role-playing to be performed anytime and anywhere. [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 utilizes a generation AI and copilot to enable customer service role-playing anytime, anywhere. In this system, a user inputs instructions to the generation AI to initiate a customer service role-playing session. The generation AI then analyzes the instructions and generates an appropriate customer service scenario. Based on the generated scenario, the user can interact with the generation AI and improve their customer service skills. For example, in this system, a user inputs an instruction to the generation AI, such as, "I would like to practice how to respond when a customer asks me a question about a product." The generation AI analyzes the input instructions and generates a scenario based on past customer service data and scenario data. Based on the generated scenario, the generation AI presents questions and requests to the user as a customer, and the user responds appropriately. This allows users to perform customer service role-playing anytime, anywhere and improve their customer service skills. This allows users to perform customer service role-playing using the generation AI, even outside of store hours or at home. The generation AI can also evaluate the user's responses and provide feedback. This allows users to objectively evaluate their customer service skills and identify areas for improvement.

[0029] A customer service role-playing system according to an embodiment includes a reception unit, a generation unit, a dialogue unit, and an evaluation unit. The reception unit receives instructions for the customer service role-playing from a user. For example, the user may input an instruction such as, "I would like to practice how to respond when a customer asks me about a product." The generation unit uses a generation AI to analyze the instructions received by the reception unit and generate a customer service scenario. The generation AI generates a scenario based on past customer service data and scenario data in response to the user's instructions. For example, the generation AI generates a "response scenario when a customer asks me about a product." The dialogue unit engages in a dialogue with the user based on the scenario generated by the generation unit. The generation AI, acting as a customer, presents questions and requests to the user, and the user responds appropriately. For example, the generation AI, acting as a customer, asks, "What are the features of this product?" and the user responds. The evaluation unit evaluates the results of the dialogue conducted by the dialogue unit and provides feedback. The evaluation unit evaluates the user's response and provides feedback on areas for improvement. For example, the evaluation unit specifically points out good points and areas for improvement in the user's response. As a result, the customer service role-playing system according to the embodiment allows users to perform customer service role-playing anytime and anywhere, thereby improving their customer service skills.

[0030] The generation unit can generate a scenario based on past customer service data or scenario data. The generation unit generates a scenario based on, for example, past customer service data. For example, the generation unit analyzes past customer service records and customer feedback to generate an appropriate scenario. The generation unit can also generate a scenario based on scenario data used in the past. For example, the generation unit reuses scenarios that have received high ratings in the past and provides them to the user. This makes it possible to generate more appropriate scenarios by utilizing past data. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past customer service data into the generation AI and have the generation AI generate a scenario.

[0031] The dialogue unit can present a question or request to the user as a customer. The dialogue unit, for example, uses a generation AI to present a question to the user as a customer. For example, the dialogue unit generates a question such as "Please tell me the features of this product" using the generation AI and presents it to the user. The dialogue unit can also present a request to the user as a customer using the generation AI. For example, the dialogue unit generates a request such as "Please check the stock status of this product" using the generation AI and presents it to the user. This allows the user to experience an experience close to an actual customer service scenario. Some or all of the above-mentioned processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can have the generation AI generate a question or request as a customer and present it to the user.

[0032] The evaluation unit can evaluate the user's response and provide feedback. The evaluation unit, for example, uses a generation AI to evaluate the user's response. For example, the evaluation unit can analyze the good points and areas for improvement of the user's response using the generation AI and provide feedback. The evaluation unit can also score the user's response using the generation AI and provide feedback based on the score. For example, the evaluation unit can evaluate the user's response on a scale of 100 points and provide feedback based on the evaluation result. This allows the user to objectively evaluate their customer service skills and find areas for improvement. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can have the generation AI evaluate the user's response and provide the evaluation result as feedback.

[0033] The reception unit can analyze the user's past customer service role-playing history and select the optimal reception method. The reception unit can, for example, use a generation AI to analyze the user's past customer service role-playing history. For example, the reception unit can analyze the frequency and content of role-playing the user has done in the past and select the optimal reception method. The reception unit can also prioritize scenarios that the user has had difficulty with in the past. For example, the reception unit can select the optimal reception method for a specific time period from the user's past role-playing history. This makes it possible to provide the optimal reception method based on the past history. Some or all of the above-mentioned processing in the reception unit can be performed using or without the generation AI. For example, the reception unit can have the generation AI analyze the user's past customer service role-playing history and select the optimal reception method based on the results.

[0034] The reception unit can filter scenarios based on the user's current skill level and areas of interest when accepting a customer service role-play. The reception unit, for example, uses a generation AI to analyze the user's current skill level and areas of interest. For example, the reception unit can identify the user's skill level and areas of interest based on the user's past role-play history and feedback. The reception unit can also filter optimal scenarios based on the user's skill level and areas of interest. For example, the reception unit can suggest scenarios of different difficulty levels depending on the user's skill level. The reception unit can also preferentially suggest related scenarios based on the user's areas of interest. This makes it possible to provide optimal scenarios based on the user's skill level and areas of interest. Some or all of the above-described processing in the reception unit can be performed using or without the generation AI. For example, the reception unit can have the generation AI analyze the user's skill level and areas of interest and filter optimal scenarios based on the results.

[0035] The reception unit can select the optimal reception means depending on the user's input method when accepting a customer service role-play. The reception unit, for example, uses a generation AI to analyze the user's input method. For example, if the user selects voice input, the reception unit can use voice recognition to accept the call. Furthermore, if the user selects text input, the reception unit can also provide a text-based interface. Furthermore, if the user selects image input, the reception unit can use image recognition to accept the call. For example, if the user inputs an image using a smartphone camera, the reception unit analyzes the image and proposes an appropriate scenario. This makes it possible to provide the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can have a generation AI analyze the user's input method and select the optimal reception means based on the results.

[0036] When accepting a customer service role-play request, the reception unit can prioritize accepting highly relevant scenarios taking into account the user's geographical location information. The reception unit, for example, uses a generation AI to analyze the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize suggesting scenarios related to that area. Furthermore, if the user is traveling, the reception unit can prioritize suggesting scenarios related to tourist spots. Furthermore, if the user is at home, the reception unit can prioritize suggesting customer service scenarios at home. This makes it possible to provide an optimal scenario based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can have the generation AI analyze the user's geographical location information and prioritize accepting highly relevant scenarios based on the results.

[0037] The reception unit can analyze the user's social media activity and receive related scenarios when receiving a customer service role-play. The reception unit can, for example, use a generation AI to analyze the user's social media activity. For example, the reception unit can suggest scenarios related to products in which the user has shown interest on social media. The reception unit can also analyze the content of the user's social media posts and suggest related scenarios. Furthermore, the reception unit can also suggest related scenarios by referring to the activities of the user's friends on social media. This makes it possible to provide optimal scenarios based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit can be performed using or without the generation AI. For example, the reception unit can have the generation AI analyze the user's social media activity and receive related scenarios based on the results.

[0038] The reception unit can customize the reception method by reflecting the user's past feedback when accepting a customer in a customer service role-play. The reception unit, for example, uses a generation AI to analyze the user's past feedback. For example, the reception unit can propose an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially propose specific scenarios based on the user's past feedback. Furthermore, the reception unit can customize the reception interface by reflecting the user's feedback. This makes it possible to provide an optimal reception method based on the user's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can have the generation AI analyze the user's past feedback and customize the reception method based on the results.

[0039] When generating a scenario, the generation unit can adjust the level of detail of the scenario based on the importance of customer service. The generation unit, for example, uses a generation AI to analyze the importance of customer service. For example, the generation unit adjusts the level of detail of the scenario based on the importance of the customer or the importance of the transaction. The generation unit can also adjust the length of the scenario and the level of detail of the information depending on the importance of customer service. For example, the generation unit generates a scenario that includes detailed explanations and procedures for an important customer service scenario. The generation unit can also generate a scenario that includes basic explanations and procedures for a general customer service scenario. This makes it possible to provide an optimal level of detail for the scenario depending on the importance of customer service. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can have a generation AI analyze the importance of customer service and adjust the level of detail of the scenario based on the results.

[0040] When generating a scenario, the generation unit can apply different generation algorithms depending on the customer service category. The generation unit, for example, uses a generation AI to analyze the customer service category. For example, the generation unit applies different generation algorithms depending on the customer service category. The generation unit can also adjust the content and expression method of the scenario based on the customer service category. For example, for a product explanation scenario, the generation unit applies an algorithm that includes detailed product information. For a complaint handling scenario, the generation unit can also apply an algorithm that includes quick and courteous response. This makes it possible to provide an optimal generation algorithm depending on the customer service category. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can have a generation AI analyze the customer service category and apply different generation algorithms based on the results.

[0041] When generating a scenario, the generation unit can improve the accuracy of generation by referring to the user's past scenario results. The generation unit, for example, uses a generation AI to analyze the user's past scenario results. For example, the generation unit generates a similar scenario based on a scenario in which the user was successful in the past. The generation unit can also generate a scenario that reflects improvements based on a scenario in which the user was unsuccessful in the past. Furthermore, the generation unit can analyze the user's past scenario results and generate an optimal scenario. This makes it possible to provide an optimal scenario based on the user's past scenario results. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can have the generation AI analyze the user's past scenario results and improve the accuracy of generation based on the results.

[0042] When generating scenarios, the generation unit can determine the priority of the scenarios based on the time of submission of the customer service requests. The generation unit, for example, uses a generation AI to analyze the time of submission of the customer service requests. For example, the generation unit determines the priority of the scenarios based on the submission deadline and priority. The generation unit can also adjust the generation order of the scenarios depending on the time of submission. For example, the generation unit generates an urgent customer service scenario with the highest priority. The generation unit can also generate a regular customer service scenario with the normal priority. This makes it possible to provide an optimal priority of scenarios depending on the time of submission of the customer service requests. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can have a generation AI analyze the time of submission of the customer service requests and determine the priority of the scenarios based on the results.

[0043] When generating scenarios, the generation unit can adjust the order of the scenarios based on the relevance of customer service. The generation unit, for example, uses a generation AI to analyze the relevance of customer service. For example, the generation unit adjusts the order of the scenarios based on the degree of similarity between the themes and contents of the scenarios. The generation unit can also adjust the order of generated scenarios based on the relevance. For example, the generation unit generates important customer service scenarios first. The generation unit can also generate general customer service scenarios in the middle. This makes it possible to provide an optimal scenario order according to the relevance of customer service. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can have a generation AI analyze the relevance of customer service and adjust the order of the scenarios based on the results.

[0044] When generating a scenario, the generation unit can adjust the use of technical terms in the scenario according to the user's level of expertise. The generation unit, for example, uses a generation AI to analyze the user's level of expertise. For example, the generation unit can identify the user's level of expertise based on the user's past role-playing history and feedback. The generation unit can also adjust the use of technical terms in the scenario according to the user's level of expertise. For example, if the user is a beginner, the generation unit can generate a simple scenario that avoids technical terms. Also, if the user is an intermediate player, the generation unit can generate a scenario that includes basic technical terms. This makes it possible to provide an optimal scenario according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can have the generation AI analyze the user's level of expertise and adjust the use of technical terms in the scenario based on the results.

[0045] During a dialogue, the dialogue unit can select an optimal dialogue method by referring to the user's past dialogue history. The dialogue unit, for example, uses a generation AI to analyze the user's past dialogue history. For example, the dialogue unit selects an optimal dialogue method based on dialogue styles that the user has preferred in the past. The dialogue unit can also avoid dialogue styles that the user has struggled with in the past. Furthermore, the dialogue unit can select a dialogue method for a specific topic from the user's past dialogue history. This makes it possible to provide an optimal dialogue method based on the user's past dialogue history. Some or all of the above-mentioned processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can have the generation AI analyze the user's past dialogue history and select an optimal dialogue method based on the results.

[0046] The dialogue unit can customize dialogue content based on the user's current skill level during dialogue. The dialogue unit, for example, uses a generation AI to analyze the user's current skill level. For example, the dialogue unit identifies the user's skill level based on the user's past role-playing history and feedback. The dialogue unit can also customize dialogue content based on the user's skill level. For example, the dialogue unit can provide basic dialogue content if the user is a beginner. Also, the dialogue unit can provide applied dialogue content if the user is an intermediate player. This makes it possible to provide optimal dialogue content according to the user's skill level. Some or all of the above-mentioned processing in the dialogue unit may be performed using or without the generation AI. For example, the dialogue unit can have the generation AI analyze the user's skill level and customize the dialogue content based on the results.

[0047] The dialogue unit can improve the dialogue method by reflecting user feedback during dialogue. The dialogue unit, for example, uses a generation AI to analyze the user's feedback. For example, the dialogue unit can adjust the dialogue method based on feedback provided by the user in past dialogues. Furthermore, if the user prefers a particular dialogue style, the dialogue unit can preferentially adopt that style. Furthermore, the dialogue unit can adjust the dialogue progress speed and tone by reflecting the user's feedback. This makes it possible to provide an optimal dialogue method based on the user's feedback. Some or all of the above-mentioned processing in the dialogue unit may be performed using or without the generation AI. For example, the dialogue unit can have the generation AI analyze the user's feedback and improve the dialogue method based on the results.

[0048] During a dialogue, the dialogue unit can select an optimal dialogue method taking into account the user's geographical location information. The dialogue unit, for example, uses a generation AI to analyze the user's geographical location information. For example, if the user is in a specific area, the dialogue unit can proceed with the dialogue on a topic related to that area. Furthermore, if the user is traveling, the dialogue unit can proceed with the dialogue on a topic related to tourist spots. Furthermore, if the user is at home, the dialogue unit can proceed with the dialogue on a topic related to home life. This makes it possible to provide an optimal dialogue method based on the user's geographical location information. Some or all of the above-mentioned processing in the dialogue unit may be performed using or without the generation AI. For example, the dialogue unit can have the generation AI analyze the user's geographical location information and select an optimal dialogue method based on the results.

[0049] During a dialogue, the dialogue unit can analyze the user's social media activity and suggest dialogue content. The dialogue unit, for example, uses a generation AI to analyze the user's social media activity. For example, the dialogue unit can suggest dialogue related to topics in which the user has shown interest on social media. The dialogue unit can also analyze the content of the user's social media posts and suggest related dialogue. Furthermore, the dialogue unit can suggest related dialogue based on the activity of the user's friends on social media. This makes it possible to provide optimal dialogue content based on the user's social media activity. Some or all of the above-mentioned processing in the dialogue unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the dialogue unit can have a generation AI analyze the user's social media activity and suggest dialogue content based on the results.

[0050] The dialogue unit can customize the dialogue method by reflecting the user's past feedback during dialogue. The dialogue unit, for example, uses a generation AI to analyze the user's past feedback. For example, the dialogue unit adjusts the dialogue method based on feedback provided by the user in the past. Furthermore, if the user prefers a particular dialogue style, the dialogue unit can preferentially adopt that style. Furthermore, the dialogue unit can adjust the dialogue speed and tone by reflecting the user's feedback. This makes it possible to provide an optimal dialogue method based on the user's past feedback. Some or all of the above-mentioned processing in the dialogue unit may be performed using or without the generation AI. For example, the dialogue unit can have the generation AI analyze the user's past feedback and customize the dialogue method based on the results.

[0051] During evaluation, the evaluation unit can select the optimal evaluation method by referring to the user's past evaluation history. The evaluation unit, for example, uses a generation AI to analyze the user's past evaluation history. For example, the evaluation unit selects the optimal evaluation method based on evaluation styles that the user has previously preferred. The evaluation unit can also avoid evaluation styles that the user has previously struggled with. Furthermore, the evaluation unit can select an evaluation method for a specific topic from the user's past evaluation history. This makes it possible to provide the optimal evaluation method based on the user's past evaluation history. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can have the generation AI analyze the user's past evaluation history and select the optimal evaluation method based on the results.

[0052] The evaluation unit can customize the evaluation content based on the user's current skill level during evaluation. The evaluation unit, for example, uses a generation AI to analyze the user's current skill level. For example, the evaluation unit identifies the user's skill level based on the user's past role-playing history and feedback. The evaluation unit can also customize the evaluation content based on the user's skill level. For example, the evaluation unit can provide basic evaluation content if the user is a beginner. Also, the evaluation unit can provide applied evaluation content if the user is an intermediate player. This makes it possible to provide optimal evaluation content according to the user's skill level. Some or all of the above-mentioned processing in the evaluation unit may be performed using or without the generation AI. For example, the evaluation unit can have the generation AI analyze the user's skill level and customize the evaluation content based on the results.

[0053] The evaluation unit can improve the evaluation method by reflecting user feedback during evaluation. The evaluation unit, for example, uses a generation AI to analyze the user's feedback. For example, the evaluation unit can adjust the evaluation method based on feedback provided by the user in past evaluations. Furthermore, if the user prefers a particular evaluation style, the evaluation unit can preferentially adopt that style. Furthermore, the evaluation unit can adjust the progress speed and tone of the evaluation by reflecting the user's feedback. This makes it possible to provide an optimal evaluation method based on the user's feedback. Some or all of the above-mentioned processing in the evaluation unit may be performed using or without the generation AI. For example, the evaluation unit can have the generation AI analyze the user's feedback and improve the evaluation method based on the results.

[0054] The evaluation unit can select the optimal evaluation method by taking into account the user's geographical location information when evaluating. The evaluation unit, for example, uses a generation AI to analyze the user's geographical location information. For example, if the user is in a specific area, the evaluation unit can proceed with the evaluation on topics related to that area. Also, if the user is traveling, the evaluation unit can proceed with the evaluation on topics related to tourist spots. Furthermore, if the user is at home, the evaluation unit can proceed with the evaluation on topics related to home life. This makes it possible to provide an optimal evaluation method based on the user's geographical location information. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can have the generation AI analyze the user's geographical location information and select the optimal evaluation method based on the results.

[0055] At the time of evaluation, the evaluation unit can analyze the user's social media activity and suggest evaluation content. The evaluation unit can, for example, use a generation AI to analyze the user's social media activity. For example, the evaluation unit can suggest evaluations related to topics in which the user has shown interest on social media. The evaluation unit can also analyze the content of the user's social media posts and suggest related evaluations. Furthermore, the evaluation unit can also suggest related evaluations based on the activity of the user's friends on social media. This makes it possible to provide optimal evaluation content based on the user's social media activity. Some or all of the above-mentioned processing in the evaluation unit may be performed using or without the generation AI. For example, the evaluation unit can have the generation AI analyze the user's social media activity and suggest evaluation content based on the results.

[0056] The evaluation unit can customize the evaluation method by reflecting the user's past feedback during evaluation. The evaluation unit, for example, uses a generation AI to analyze the user's past feedback. For example, the evaluation unit can adjust the evaluation method based on feedback provided by the user in the past. If the user prefers a particular evaluation style, the evaluation unit can also preferentially adopt that style. Furthermore, the evaluation unit can adjust the speed and tone of the evaluation by reflecting the user's feedback. This makes it possible to provide an optimal evaluation method based on the user's past feedback. Some or all of the above-mentioned processing in the evaluation unit may be performed using or without the generation AI. For example, the evaluation unit can have the generation AI analyze the user's past feedback and customize the evaluation method based on the results.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The reception unit can analyze the user's past customer service role-play history and prioritize suggesting scenarios that the user finds particularly difficult. For example, if the user has had difficulty explaining a product in the past, the reception unit can suggest that scenario again, allowing the user to improve. The reception unit can also resuggest scenarios that the user has previously rated highly, strengthening successful experiences. Furthermore, the reception unit can prioritize suggesting scenarios that the user is particularly interested in, based on the user's past feedback. This makes it possible to provide optimal scenarios based on the user's past experience.

[0059] The generation unit can adjust the difficulty of the scenario based on the results of the user's past customer service role-playing. For example, if the user previously received a high evaluation for an easy scenario, the generation unit can increase the difficulty of the next scenario. Also, if the user previously received a low evaluation for a difficult scenario, the generation unit can lower the difficulty of the next scenario. Furthermore, the generation unit can customize the content of the scenario based on the user's past feedback. This makes it possible to provide the optimal scenario according to the user's skill level.

[0060] The reception unit can propose region-specific customer service scenarios taking into account the user's geographical location information. For example, if the user is in a tourist destination, a customer service scenario for tourists in that area can be proposed. Also, if the user is in a specific city, a customer service scenario based on the culture and customs of that city can be proposed. Furthermore, if the user is at home, a customer service scenario for the home can be proposed. This makes it possible to provide the optimal scenario based on the user's geographical location information.

[0061] The dialogue unit can refer to the user's past dialogue history and preferentially suggest topics that the user is particularly interested in. For example, if the user has previously shown interest in product descriptions, the dialogue unit can suggest that topic again. Also, if the user has previously shown interest in handling complaints, the dialogue unit can preferentially suggest that topic. Furthermore, if the user has previously given a high rating to a particular scenario, the dialogue unit can suggest that scenario again. This makes it possible to provide optimal dialogue content based on the user's past dialogue history.

[0062] The evaluation unit can customize the evaluation criteria based on the user's past feedback. For example, if the user has preferred a particular evaluation criterion in the past, the evaluation unit can preferentially adopt that criterion. Also, if the user has avoided a particular evaluation criterion in the past, the evaluation unit can avoid that criterion. Furthermore, the level of detail of the evaluation can be adjusted based on the user's past feedback. This makes it possible to provide optimal evaluation criteria based on the user's past feedback.

[0063] The reception unit can analyze the user's social media activity and suggest scenarios related to topics that the user is interested in. For example, if the user frequently posts about a particular product on social media, the reception unit can suggest scenarios related to that product. Also, if the user expresses interest in a particular event on social media, the reception unit can suggest scenarios related to that event. Furthermore, the reception unit can suggest related scenarios based on the activities of the user's friends on social media. This makes it possible to provide optimal scenarios based on the user's social media activity.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit receives instructions for the customer service role-play from the user. For example, the user can input an instruction such as, "I would like to practice how to respond when a customer asks me a question about a product." Step 2: The generation unit uses the generation AI to analyze the instructions received by the reception unit and generate a customer service scenario. The generation AI generates a scenario based on the user's instructions, based on past customer service data and scenario data. For example, the generation AI generates a "response scenario when a customer asks a question about a product." Step 3: The dialogue unit interacts with the user based on the scenario generated by the generation unit. The generation AI plays the role of a customer and presents questions and requests to the user, who then responds appropriately. For example, the generation AI plays the role of a customer and asks, "Please tell me the features of this product," and the user responds. Step 4: The evaluation unit evaluates the results of the dialogue performed by the dialogue unit and provides feedback. The evaluation unit evaluates the user's response and provides feedback on areas for improvement. For example, the evaluation unit specifically points out good points in the user's response and areas for improvement.

[0066] (Example 2) A customer service role-playing system according to an embodiment of the present invention utilizes a generation AI and copilot to enable customer service role-playing anytime, anywhere. In this system, a user inputs instructions to the generation AI to initiate a customer service role-playing session. The generation AI then analyzes the instructions and generates an appropriate customer service scenario. Based on the generated scenario, the user can interact with the generation AI and improve their customer service skills. For example, in this system, a user inputs an instruction to the generation AI, such as, "I would like to practice how to respond when a customer asks me a question about a product." The generation AI analyzes the input instructions and generates a scenario based on past customer service data and scenario data. Based on the generated scenario, the generation AI presents questions and requests to the user as a customer, and the user responds appropriately. This allows users to perform customer service role-playing anytime, anywhere and improve their customer service skills. This allows users to perform customer service role-playing using the generation AI, even outside of store hours or at home. The generation AI can also evaluate the user's responses and provide feedback. This allows users to objectively evaluate their customer service skills and identify areas for improvement.

[0067] A customer service role-playing system according to an embodiment includes a reception unit, a generation unit, a dialogue unit, and an evaluation unit. The reception unit receives instructions for the customer service role-playing from a user. For example, the user may input an instruction such as, "I would like to practice how to respond when a customer asks me about a product." The generation unit uses a generation AI to analyze the instructions received by the reception unit and generate a customer service scenario. The generation AI generates a scenario based on past customer service data and scenario data in response to the user's instructions. For example, the generation AI generates a "response scenario when a customer asks me about a product." The dialogue unit engages in a dialogue with the user based on the scenario generated by the generation unit. The generation AI, acting as a customer, presents questions and requests to the user, and the user responds appropriately. For example, the generation AI, acting as a customer, asks, "What are the features of this product?" and the user responds. The evaluation unit evaluates the results of the dialogue conducted by the dialogue unit and provides feedback. The evaluation unit evaluates the user's response and provides feedback on areas for improvement. For example, the evaluation unit specifically points out good points and areas for improvement in the user's response. As a result, the customer service role-playing system according to the embodiment allows users to perform customer service role-playing anytime and anywhere, thereby improving their customer service skills.

[0068] The generation unit can generate a scenario based on past customer service data or scenario data. The generation unit generates a scenario based on, for example, past customer service data. For example, the generation unit analyzes past customer service records and customer feedback to generate an appropriate scenario. The generation unit can also generate a scenario based on scenario data used in the past. For example, the generation unit reuses scenarios that have received high ratings in the past and provides them to the user. This makes it possible to generate more appropriate scenarios by utilizing past data. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past customer service data into the generation AI and have the generation AI generate a scenario.

[0069] The dialogue unit can present a question or request to the user as a customer. The dialogue unit, for example, uses a generation AI to present a question to the user as a customer. For example, the dialogue unit generates a question such as "Please tell me the features of this product" using the generation AI and presents it to the user. The dialogue unit can also present a request to the user as a customer using the generation AI. For example, the dialogue unit generates a request such as "Please check the stock status of this product" using the generation AI and presents it to the user. This allows the user to experience an experience close to an actual customer service scenario. Some or all of the above-mentioned processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can have the generation AI generate a question or request as a customer and present it to the user.

[0070] The evaluation unit can evaluate the user's response and provide feedback. The evaluation unit, for example, uses a generation AI to evaluate the user's response. For example, the evaluation unit can analyze the good points and areas for improvement of the user's response using the generation AI and provide feedback. The evaluation unit can also score the user's response using the generation AI and provide feedback based on the score. For example, the evaluation unit can evaluate the user's response on a scale of 100 points and provide feedback based on the evaluation result. This allows the user to objectively evaluate their customer service skills and find areas for improvement. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can have the generation AI evaluate the user's response and provide the evaluation result as feedback.

[0071] The reception unit can estimate the user's emotions and adjust the start timing of the customer service role-play based on the estimated emotions. The reception unit estimates the user's emotions using, for example, a generation AI. For example, the reception unit analyzes the user's facial expressions and voice to estimate the user's emotions. The reception unit can also adjust the start timing of the customer service role-play based on the estimated emotions. For example, if the user is nervous, the reception unit can provide the user with preparation time to relax before starting the customer service role-play. Also, if the user is relaxed, the reception unit can immediately start the customer service role-play. This allows the customer service role-play to start at the optimal timing depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit can have the generation AI estimate the user's emotions and adjust the start timing of the customer service role-play based on the result.

[0072] The reception unit can analyze the user's past customer service role-playing history and select the optimal reception method. The reception unit can, for example, use a generation AI to analyze the user's past customer service role-playing history. For example, the reception unit can analyze the frequency and content of role-playing the user has done in the past and select the optimal reception method. The reception unit can also prioritize scenarios that the user has had difficulty with in the past. For example, the reception unit can select the optimal reception method for a specific time period from the user's past role-playing history. This makes it possible to provide the optimal reception method based on the past history. Some or all of the above-mentioned processing in the reception unit can be performed using or without the generation AI. For example, the reception unit can have the generation AI analyze the user's past customer service role-playing history and select the optimal reception method based on the results.

[0073] The reception unit can filter scenarios based on the user's current skill level and areas of interest when accepting a customer service role-play. The reception unit, for example, uses a generation AI to analyze the user's current skill level and areas of interest. For example, the reception unit can identify the user's skill level and areas of interest based on the user's past role-play history and feedback. The reception unit can also filter optimal scenarios based on the user's skill level and areas of interest. For example, the reception unit can suggest scenarios of different difficulty levels depending on the user's skill level. The reception unit can also preferentially suggest related scenarios based on the user's areas of interest. This makes it possible to provide optimal scenarios based on the user's skill level and areas of interest. Some or all of the above-described processing in the reception unit can be performed using or without the generation AI. For example, the reception unit can have the generation AI analyze the user's skill level and areas of interest and filter optimal scenarios based on the results.

[0074] The reception unit can select the optimal reception means depending on the user's input method when accepting a customer service role-play. The reception unit, for example, uses a generation AI to analyze the user's input method. For example, if the user selects voice input, the reception unit can use voice recognition to accept the call. Furthermore, if the user selects text input, the reception unit can also provide a text-based interface. Furthermore, if the user selects image input, the reception unit can use image recognition to accept the call. For example, if the user inputs an image using a smartphone camera, the reception unit analyzes the image and proposes an appropriate scenario. This makes it possible to provide the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit can have a generation AI analyze the user's input method and select the optimal reception means based on the results.

[0075] The reception unit can estimate the user's emotions and determine the priority of role-plays to be accepted based on the estimated user emotions. The reception unit, for example, uses a generation AI to estimate the user's emotions. For example, the reception unit analyzes the user's facial expressions and voice to estimate the user's emotions. The reception unit can also determine the priority of role-plays to be accepted based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can prioritize relaxing scenarios. Also, if the user is excited, the reception unit can prioritize challenging scenarios. This makes it possible to provide an optimal priority of role-plays according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit can have the generation AI estimate the user's emotions and determine the priority of role-plays based on the result.

[0076] When accepting a customer service role-play request, the reception unit can prioritize accepting highly relevant scenarios taking into account the user's geographical location information. The reception unit, for example, uses a generation AI to analyze the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize suggesting scenarios related to that area. Furthermore, if the user is traveling, the reception unit can prioritize suggesting scenarios related to tourist spots. Furthermore, if the user is at home, the reception unit can prioritize suggesting customer service scenarios at home. This makes it possible to provide an optimal scenario based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can have the generation AI analyze the user's geographical location information and prioritize accepting highly relevant scenarios based on the results.

[0077] The reception unit can analyze the user's social media activity and receive related scenarios when receiving a customer service role-play. The reception unit can, for example, use a generation AI to analyze the user's social media activity. For example, the reception unit can suggest scenarios related to products in which the user has shown interest on social media. The reception unit can also analyze the content of the user's social media posts and suggest related scenarios. Furthermore, the reception unit can also suggest related scenarios by referring to the activities of the user's friends on social media. This makes it possible to provide optimal scenarios based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit can be performed using or without the generation AI. For example, the reception unit can have the generation AI analyze the user's social media activity and receive related scenarios based on the results.

[0078] The reception unit can customize the reception method by reflecting the user's past feedback when accepting a customer in a customer service role-play. The reception unit, for example, uses a generation AI to analyze the user's past feedback. For example, the reception unit can propose an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially propose specific scenarios based on the user's past feedback. Furthermore, the reception unit can customize the reception interface by reflecting the user's feedback. This makes it possible to provide an optimal reception method based on the user's past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using or without the generation AI. For example, the reception unit can have the generation AI analyze the user's past feedback and customize the reception method based on the results.

[0079] The generation unit can estimate the user's emotions and adjust the way the scenario is expressed based on the estimated user's emotions. The generation unit, for example, estimates the user's emotions using a generation AI. For example, the generation unit analyzes the user's facial expressions and voice to estimate the user's emotions. The generation unit can also adjust the way the scenario is expressed based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a scenario using a calm expression method. Also, if the user is nervous, the generation unit can generate a scenario using a simple and easy-to-understand expression method. This makes it possible to provide an optimal way to express the scenario according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can have the generation AI estimate the user's emotions and adjust the way the scenario is expressed based on the result.

[0080] When generating a scenario, the generation unit can adjust the level of detail of the scenario based on the importance of customer service. The generation unit, for example, uses a generation AI to analyze the importance of customer service. For example, the generation unit adjusts the level of detail of the scenario based on the importance of the customer or the importance of the transaction. The generation unit can also adjust the length of the scenario and the level of detail of the information depending on the importance of customer service. For example, the generation unit generates a scenario that includes detailed explanations and procedures for an important customer service scenario. The generation unit can also generate a scenario that includes basic explanations and procedures for a general customer service scenario. This makes it possible to provide an optimal level of detail for the scenario depending on the importance of customer service. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can have a generation AI analyze the importance of customer service and adjust the level of detail of the scenario based on the results.

[0081] When generating a scenario, the generation unit can apply different generation algorithms depending on the customer service category. The generation unit, for example, uses a generation AI to analyze the customer service category. For example, the generation unit applies different generation algorithms depending on the customer service category. The generation unit can also adjust the content and expression method of the scenario based on the customer service category. For example, for a product explanation scenario, the generation unit applies an algorithm that includes detailed product information. For a complaint handling scenario, the generation unit can also apply an algorithm that includes quick and courteous response. This makes it possible to provide an optimal generation algorithm depending on the customer service category. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can have a generation AI analyze the customer service category and apply different generation algorithms based on the results.

[0082] When generating a scenario, the generation unit can improve the accuracy of generation by referring to the user's past scenario results. The generation unit, for example, uses a generation AI to analyze the user's past scenario results. For example, the generation unit generates a similar scenario based on a scenario in which the user was successful in the past. The generation unit can also generate a scenario that reflects improvements based on a scenario in which the user was unsuccessful in the past. Furthermore, the generation unit can analyze the user's past scenario results and generate an optimal scenario. This makes it possible to provide an optimal scenario based on the user's past scenario results. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can have the generation AI analyze the user's past scenario results and improve the accuracy of generation based on the results.

[0083] The generation unit can estimate the user's emotions and adjust the length of the scenario based on the estimated user emotions. The generation unit, for example, uses a generation AI to estimate the user's emotions. For example, the generation unit analyzes the user's facial expressions and voice to estimate the user's emotions. The generation unit can also adjust the length of the scenario based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise scenario. On the other hand, if the user is relaxed, the generation unit can generate a longer scenario with detailed explanations. This allows the optimal scenario length to be provided according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit can have the generation AI estimate the user's emotions and adjust the length of the scenario based on the result.

[0084] When generating scenarios, the generation unit can determine the priority of the scenarios based on the time of submission of the customer service requests. The generation unit, for example, uses a generation AI to analyze the time of submission of the customer service requests. For example, the generation unit determines the priority of the scenarios based on the submission deadline and priority. The generation unit can also adjust the generation order of the scenarios depending on the time of submission. For example, the generation unit generates an urgent customer service scenario with the highest priority. The generation unit can also generate a regular customer service scenario with the normal priority. This makes it possible to provide an optimal priority of scenarios depending on the time of submission of the customer service requests. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can have a generation AI analyze the time of submission of the customer service requests and determine the priority of the scenarios based on the results.

[0085] When generating scenarios, the generation unit can adjust the order of the scenarios based on the relevance of customer service. The generation unit, for example, uses a generation AI to analyze the relevance of customer service. For example, the generation unit adjusts the order of the scenarios based on the degree of similarity between the themes and contents of the scenarios. The generation unit can also adjust the order of generated scenarios based on the relevance. For example, the generation unit generates important customer service scenarios first. The generation unit can also generate general customer service scenarios in the middle. This makes it possible to provide an optimal scenario order according to the relevance of customer service. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can have a generation AI analyze the relevance of customer service and adjust the order of the scenarios based on the results.

[0086] When generating a scenario, the generation unit can adjust the use of technical terms in the scenario according to the user's level of expertise. The generation unit, for example, uses a generation AI to analyze the user's level of expertise. For example, the generation unit can identify the user's level of expertise based on the user's past role-playing history and feedback. The generation unit can also adjust the use of technical terms in the scenario according to the user's level of expertise. For example, if the user is a beginner, the generation unit can generate a simple scenario that avoids technical terms. Also, if the user is an intermediate player, the generation unit can generate a scenario that includes basic technical terms. This makes it possible to provide an optimal scenario according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can have the generation AI analyze the user's level of expertise and adjust the use of technical terms in the scenario based on the results.

[0087] The dialogue unit can estimate the user's emotions and adjust the dialogue progression method based on the estimated user emotions. The dialogue unit estimates the user's emotions using, for example, a generation AI. For example, the dialogue unit analyzes the user's facial expressions and voice to estimate the user's emotions. The dialogue unit can also adjust the dialogue progression method based on the estimated emotions. For example, if the user is nervous, the dialogue unit can proceed with the dialogue in a calm tone. Also, if the user is relaxed, the dialogue unit can proceed with the dialogue in a friendly tone. This makes it possible to provide an optimal dialogue progression method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can have the generation AI estimate the user's emotions and adjust the dialogue progression method based on the result.

[0088] During a dialogue, the dialogue unit can select an optimal dialogue method by referring to the user's past dialogue history. The dialogue unit, for example, uses a generation AI to analyze the user's past dialogue history. For example, the dialogue unit selects an optimal dialogue method based on dialogue styles that the user has preferred in the past. The dialogue unit can also avoid dialogue styles that the user has struggled with in the past. Furthermore, the dialogue unit can select a dialogue method for a specific topic from the user's past dialogue history. This makes it possible to provide an optimal dialogue method based on the user's past dialogue history. Some or all of the above-mentioned processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can have the generation AI analyze the user's past dialogue history and select an optimal dialogue method based on the results.

[0089] The dialogue unit can customize dialogue content based on the user's current skill level during dialogue. The dialogue unit, for example, uses a generation AI to analyze the user's current skill level. For example, the dialogue unit identifies the user's skill level based on the user's past role-playing history and feedback. The dialogue unit can also customize dialogue content based on the user's skill level. For example, the dialogue unit can provide basic dialogue content if the user is a beginner. Also, the dialogue unit can provide applied dialogue content if the user is an intermediate player. This makes it possible to provide optimal dialogue content according to the user's skill level. Some or all of the above-mentioned processing in the dialogue unit may be performed using or without the generation AI. For example, the dialogue unit can have the generation AI analyze the user's skill level and customize the dialogue content based on the results.

[0090] The dialogue unit can improve the dialogue method by reflecting user feedback during dialogue. The dialogue unit, for example, uses a generation AI to analyze the user's feedback. For example, the dialogue unit can adjust the dialogue method based on feedback provided by the user in past dialogues. Furthermore, if the user prefers a particular dialogue style, the dialogue unit can preferentially adopt that style. Furthermore, the dialogue unit can adjust the dialogue progress speed and tone by reflecting the user's feedback. This makes it possible to provide an optimal dialogue method based on the user's feedback. Some or all of the above-mentioned processing in the dialogue unit may be performed using or without the generation AI. For example, the dialogue unit can have the generation AI analyze the user's feedback and improve the dialogue method based on the results.

[0091] The dialogue unit can estimate the user's emotions and determine the priority of dialogues based on the estimated user emotions. The dialogue unit, for example, estimates the user's emotions using a generation AI. For example, the dialogue unit analyzes the user's facial expressions and voice to estimate the user's emotions. The dialogue unit can also determine the priority of dialogues based on the estimated emotions. For example, if the user is feeling stressed, the dialogue unit can prioritize providing relaxing dialogues. Also, if the user is excited, the dialogue unit can prioritize challenging dialogues. This makes it possible to provide optimal dialogue priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the dialogue unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the dialogue unit can have the generation AI estimate the user's emotions and determine the priority of dialogues based on the results.

[0092] During a dialogue, the dialogue unit can select an optimal dialogue method taking into account the user's geographical location information. The dialogue unit, for example, uses a generation AI to analyze the user's geographical location information. For example, if the user is in a specific area, the dialogue unit can proceed with the dialogue on a topic related to that area. Furthermore, if the user is traveling, the dialogue unit can proceed with the dialogue on a topic related to tourist spots. Furthermore, if the user is at home, the dialogue unit can proceed with the dialogue on a topic related to home life. This makes it possible to provide an optimal dialogue method based on the user's geographical location information. Some or all of the above-mentioned processing in the dialogue unit may be performed using or without the generation AI. For example, the dialogue unit can have the generation AI analyze the user's geographical location information and select an optimal dialogue method based on the results.

[0093] During a dialogue, the dialogue unit can analyze the user's social media activity and suggest dialogue content. The dialogue unit, for example, uses a generation AI to analyze the user's social media activity. For example, the dialogue unit can suggest dialogue related to topics in which the user has shown interest on social media. The dialogue unit can also analyze the content of the user's social media posts and suggest related dialogue. Furthermore, the dialogue unit can suggest related dialogue based on the activity of the user's friends on social media. This makes it possible to provide optimal dialogue content based on the user's social media activity. Some or all of the above-mentioned processing in the dialogue unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the dialogue unit can have a generation AI analyze the user's social media activity and suggest dialogue content based on the results.

[0094] The dialogue unit can customize the dialogue method by reflecting the user's past feedback during dialogue. The dialogue unit, for example, uses a generation AI to analyze the user's past feedback. For example, the dialogue unit adjusts the dialogue method based on feedback provided by the user in the past. Furthermore, if the user prefers a particular dialogue style, the dialogue unit can preferentially adopt that style. Furthermore, the dialogue unit can adjust the dialogue speed and tone by reflecting the user's feedback. This makes it possible to provide an optimal dialogue method based on the user's past feedback. Some or all of the above-mentioned processing in the dialogue unit may be performed using or without the generation AI. For example, the dialogue unit can have the generation AI analyze the user's past feedback and customize the dialogue method based on the results.

[0095] The evaluation unit can estimate the user's emotions and adjust the evaluation method based on the estimated user's emotions. The evaluation unit, for example, estimates the user's emotions using a generation AI. For example, the evaluation unit analyzes the user's facial expressions and voice to estimate the user's emotions. The evaluation unit can also adjust the evaluation method based on the estimated emotions. For example, the evaluation unit can provide feedback in a gentle tone if the user is nervous. The evaluation unit can also provide detailed feedback if the user is relaxed. This makes it possible to provide an optimal evaluation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can have the generation AI estimate the user's emotions and adjust the evaluation method based on the results.

[0096] During evaluation, the evaluation unit can select the optimal evaluation method by referring to the user's past evaluation history. The evaluation unit, for example, uses a generation AI to analyze the user's past evaluation history. For example, the evaluation unit selects the optimal evaluation method based on evaluation styles that the user has previously preferred. The evaluation unit can also avoid evaluation styles that the user has previously struggled with. Furthermore, the evaluation unit can select an evaluation method for a specific topic from the user's past evaluation history. This makes it possible to provide the optimal evaluation method based on the user's past evaluation history. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can have the generation AI analyze the user's past evaluation history and select the optimal evaluation method based on the results.

[0097] The evaluation unit can customize the evaluation content based on the user's current skill level during evaluation. The evaluation unit, for example, uses a generation AI to analyze the user's current skill level. For example, the evaluation unit identifies the user's skill level based on the user's past role-playing history and feedback. The evaluation unit can also customize the evaluation content based on the user's skill level. For example, the evaluation unit can provide basic evaluation content if the user is a beginner. Also, the evaluation unit can provide applied evaluation content if the user is an intermediate player. This makes it possible to provide optimal evaluation content according to the user's skill level. Some or all of the above-mentioned processing in the evaluation unit may be performed using or without the generation AI. For example, the evaluation unit can have the generation AI analyze the user's skill level and customize the evaluation content based on the results.

[0098] The evaluation unit can improve the evaluation method by reflecting user feedback during evaluation. The evaluation unit, for example, uses a generation AI to analyze the user's feedback. For example, the evaluation unit can adjust the evaluation method based on feedback provided by the user in past evaluations. Furthermore, if the user prefers a particular evaluation style, the evaluation unit can preferentially adopt that style. Furthermore, the evaluation unit can adjust the progress speed and tone of the evaluation by reflecting the user's feedback. This makes it possible to provide an optimal evaluation method based on the user's feedback. Some or all of the above-mentioned processing in the evaluation unit may be performed using or without the generation AI. For example, the evaluation unit can have the generation AI analyze the user's feedback and improve the evaluation method based on the results.

[0099] The evaluation unit can estimate the user's emotions and determine the priority of evaluations based on the estimated user emotions. The evaluation unit estimates the user's emotions using, for example, a generation AI. For example, the evaluation unit analyzes the user's facial expressions and voice to estimate the user's emotions. The evaluation unit can also determine the priority of evaluations based on the estimated emotions. For example, if the user is feeling stressed, the evaluation unit can prioritize relaxing evaluations. Also, if the user is excited, the evaluation unit can prioritize challenging evaluations. This makes it possible to provide an optimal priority of evaluations according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can have the generation AI estimate the user's emotions and determine the priority of evaluations based on the results.

[0100] The evaluation unit can select the optimal evaluation method by taking into account the user's geographical location information when evaluating. The evaluation unit, for example, uses a generation AI to analyze the user's geographical location information. For example, if the user is in a specific area, the evaluation unit can proceed with the evaluation on topics related to that area. Also, if the user is traveling, the evaluation unit can proceed with the evaluation on topics related to tourist spots. Furthermore, if the user is at home, the evaluation unit can proceed with the evaluation on topics related to home life. This makes it possible to provide an optimal evaluation method based on the user's geographical location information. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can have the generation AI analyze the user's geographical location information and select the optimal evaluation method based on the results.

[0101] At the time of evaluation, the evaluation unit can analyze the user's social media activity and suggest evaluation content. The evaluation unit can, for example, use a generation AI to analyze the user's social media activity. For example, the evaluation unit can suggest evaluations related to topics in which the user has shown interest on social media. The evaluation unit can also analyze the content of the user's social media posts and suggest related evaluations. Furthermore, the evaluation unit can also suggest related evaluations based on the activity of the user's friends on social media. This makes it possible to provide optimal evaluation content based on the user's social media activity. Some or all of the above-mentioned processing in the evaluation unit may be performed using or without the generation AI. For example, the evaluation unit can have the generation AI analyze the user's social media activity and suggest evaluation content based on the results.

[0102] The evaluation unit can customize the evaluation method by reflecting the user's past feedback during evaluation. The evaluation unit, for example, uses a generation AI to analyze the user's past feedback. For example, the evaluation unit can adjust the evaluation method based on feedback provided by the user in the past. If the user prefers a particular evaluation style, the evaluation unit can also preferentially adopt that style. Furthermore, the evaluation unit can adjust the speed and tone of the evaluation by reflecting the user's feedback. This makes it possible to provide an optimal evaluation method based on the user's past feedback. Some or all of the above-mentioned processing in the evaluation unit may be performed using or without the generation AI. For example, the evaluation unit can have the generation AI analyze the user's past feedback and customize the evaluation method based on the results. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, dialogue unit, and evaluation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives instructions for customer service role-playing from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a customer service scenario using a generation AI. The dialogue unit is realized, for example, by the control unit 46A of the smart device 14 and engages in a dialogue with the user based on the generated scenario. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the results of the dialogue and provides feedback. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, dialogue unit, and evaluation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives customer service role-play instructions from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a customer service scenario using a generation AI. The dialogue unit is realized, for example, by the control unit 46A of the smart glasses 214 and engages in a dialogue with the user based on the generated scenario. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the results of the dialogue and provides feedback. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, dialogue unit, and evaluation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives customer service role-play instructions from the user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a customer service scenario using a generation AI. The dialogue unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and engages in a dialogue with the user based on the generated scenario. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the results of the dialogue and provides feedback. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, dialogue unit, and evaluation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives customer service role-play instructions from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a customer service scenario using a generation AI. The dialogue unit is realized, for example, by the control unit 46A of the robot 414 and engages in a dialogue with the user based on the generated scenario. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the results of the dialogue and provides feedback.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] The reception unit can analyze the user's past customer service role-play history and prioritize suggesting scenarios that the user finds particularly difficult. For example, if the user has had difficulty explaining a product in the past, the reception unit can suggest that scenario again, allowing the user to improve. The reception unit can also resuggest scenarios that the user has previously rated highly, strengthening successful experiences. Furthermore, the reception unit can prioritize suggesting scenarios that the user is particularly interested in, based on the user's past feedback. This makes it possible to provide optimal scenarios based on the user's past experience.

[0105] The generation unit can adjust the difficulty of the scenario based on the results of the user's past customer service role-playing. For example, if the user previously received a high evaluation for an easy scenario, the generation unit can increase the difficulty of the next scenario. Also, if the user previously received a low evaluation for a difficult scenario, the generation unit can lower the difficulty of the next scenario. Furthermore, the generation unit can customize the content of the scenario based on the user's past feedback. This makes it possible to provide the optimal scenario according to the user's skill level.

[0106] The dialogue unit can estimate the user's emotions and adjust the tone of the dialogue based on the estimated emotions. For example, if the user is nervous, the dialogue unit can proceed with the dialogue in a calm tone. If the user is relaxed, the dialogue unit can proceed with the dialogue in a friendly tone. Furthermore, if the user is excited, the dialogue unit can proceed with the dialogue in an energetic tone. This makes it possible to provide an optimal dialogue experience according to the user's emotions.

[0107] The evaluation unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is nervous, the evaluation unit can provide feedback in a gentle tone. If the user is relaxed, the evaluation unit can provide detailed feedback. Furthermore, if the user is excited, the evaluation unit can provide challenging feedback. In this way, optimal feedback can be provided according to the user's emotions.

[0108] The reception unit can propose region-specific customer service scenarios taking into account the user's geographical location information. For example, if the user is in a tourist destination, a customer service scenario for tourists in that area can be proposed. Also, if the user is in a specific city, a customer service scenario based on the culture and customs of that city can be proposed. Furthermore, if the user is at home, a customer service scenario for the home can be proposed. This makes it possible to provide the optimal scenario based on the user's geographical location information.

[0109] The generation unit can estimate the user's emotions and adjust the speed at which the scenario progresses based on the estimated emotions. For example, if the user is nervous, the generation unit can slow down the speed at which the scenario progresses. Alternatively, if the user is relaxed, the generation unit can keep the speed at which the scenario progresses normal. Furthermore, if the user is excited, the generation unit can speed up the speed at which the scenario progresses. This makes it possible to provide an optimal speed at which the scenario progresses according to the user's emotions.

[0110] The dialogue unit can refer to the user's past dialogue history and preferentially suggest topics that the user is particularly interested in. For example, if the user has previously shown interest in product descriptions, the dialogue unit can suggest that topic again. Also, if the user has previously shown interest in handling complaints, the dialogue unit can preferentially suggest that topic. Furthermore, if the user has previously given a high rating to a particular scenario, the dialogue unit can suggest that scenario again. This makes it possible to provide optimal dialogue content based on the user's past dialogue history.

[0111] The evaluation unit can customize the evaluation criteria based on the user's past feedback. For example, if the user has preferred a particular evaluation criterion in the past, the evaluation unit can preferentially adopt that criterion. Also, if the user has avoided a particular evaluation criterion in the past, the evaluation unit can avoid that criterion. Furthermore, the level of detail of the evaluation can be adjusted based on the user's past feedback. This makes it possible to provide optimal evaluation criteria based on the user's past feedback.

[0112] The reception unit can analyze the user's social media activity and suggest scenarios related to topics that the user is interested in. For example, if the user frequently posts about a particular product on social media, the reception unit can suggest scenarios related to that product. Also, if the user expresses interest in a particular event on social media, the reception unit can suggest scenarios related to that event. Furthermore, the reception unit can suggest related scenarios based on the activities of the user's friends on social media. This makes it possible to provide optimal scenarios based on the user's social media activity.

[0113] The generation unit can estimate the user's emotions and adjust the content of the scenario based on the estimated emotions. For example, if the user is nervous, the generation unit can generate a scenario with relaxing content. If the user is relaxed, the generation unit can also generate a scenario with challenging content. Furthermore, if the user is excited, the generation unit can also generate a scenario with energetic content. This makes it possible to provide optimal scenario content according to the user's emotions.

[0114] The processing flow of the second embodiment will be briefly explained below.

[0115] Step 1: The reception unit receives instructions for the customer service role-play from the user. For example, the user can input an instruction such as, "I would like to practice how to respond when a customer asks me a question about a product." Step 2: The generation unit uses the generation AI to analyze the instructions received by the reception unit and generate a customer service scenario. The generation AI generates a scenario based on the user's instructions, based on past customer service data and scenario data. For example, the generation AI generates a "response scenario when a customer asks a question about a product." Step 3: The dialogue unit interacts with the user based on the scenario generated by the generation unit. The generation AI plays the role of a customer and presents questions and requests to the user, who then responds appropriately. For example, the generation AI plays the role of a customer and asks, "Please tell me the features of this product," and the user responds. Step 4: The evaluation unit evaluates the results of the dialogue performed by the dialogue unit and provides feedback. The evaluation unit evaluates the user's response and provides feedback on areas for improvement. For example, the evaluation unit specifically points out good points in the user's response and areas for improvement.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 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 identification processing unit 290 using these models.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] [Explanation of symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives instructions for customer service role-playing from a user; a generation unit that analyzes the instruction received by the reception unit and generates a customer service scenario; a dialogue unit that dialogues with a user based on the scenario generated by the generation unit; an evaluation unit that evaluates the results of the dialogue performed by the dialogue unit and provides feedback; A system characterized by:

2. The generation unit Generate scenarios based on past customer service data or scenario data 2. The system of claim 1.

3. The dialogue unit Present a question or request to the user as a customer 2. The system of claim 1.

4. The evaluation unit Rate the user's response and provide feedback 2. The system of claim 1.

5. The reception unit Estimate the user's emotions and adjust the timing of the customer service role-play based on the estimated emotions.

2. The system of claim 1.

6. The reception unit Analyze the user's past customer service role-play history and select the optimal reception method 2. The system of claim 1.

7. The reception unit Filtering based on the user's current skill level and interests during a customer service role-play 2. The system of claim 1.

8. The reception unit Select the most appropriate reception method depending on the user's input method when accepting a customer service role play 2. The system of claim 1.

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