System

Abstract

An object of a system according to an embodiment is to efficiently improve crew serving quality.SOLUTION: A system according to an embodiment includes a low profile pattern learning unit, a reception support unit, and an evaluation feedback unit. The low-pre pattern learning unit learns a low-pre pattern. The reception support unit supports the reception of the crew on the basis of the low maintenance pattern learned by the low maintenance pattern learning unit. The evaluation feedback unit evaluates and feeds back the reception quality of the crew supported by the reception support unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately provide a support system for efficiently improving the quality of crew service, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently improve the quality of service provided by crew members. [Means for solving the problem]

[0006] The system according to the embodiment includes a role-playing pattern learning unit, a response support unit, and an evaluation feedback unit. The role-playing pattern learning unit learns role-playing patterns. The response support unit supports the crew's response based on the role-playing patterns learned by the role-playing pattern learning unit. The evaluation feedback unit evaluates the response quality of the crew supported by the response support unit and provides feedback. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently improve the quality of service provided by crew members. [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) In the role-playing system according to an embodiment of the present invention, AI learns role-playing patterns, plays the role of the customer in the role-play, learns the flow of customer service and correct and incorrect responses, provides support when the crew member gets stuck, sets evaluation points for the quality of customer service, and provides feedback. This allows the role-playing system to improve the crew member's customer service skills.

[0029] The role-playing system according to the embodiment includes a role-playing pattern learning unit, a customer support unit, and an evaluation feedback unit. The role-playing pattern learning unit learns role-playing patterns. For example, the generation AI learns various role-playing patterns and plays the role of a customer based on those patterns. Furthermore, when a crew member practices customer service, the generation AI provides various scenarios in which the "customer" plays a role. The generation AI receives inputs from prompts containing the role-playing scenario and the customer's reaction, and generates appropriate responses based on those prompts. For example, the generation AI can handle various scenarios, such as "a customer asking a question about a product" or "a customer making a complaint." The customer support unit supports the crew member's customer service based on the role-playing patterns learned by the role-playing pattern learning unit. For example, the generation AI learns the flow of customer service and correct and incorrect responses, and provides support when the crew member is stuck. The generation AI receives inputs from prompts containing the crew member's response content and the situation, and provides appropriate support based on those prompts. For example, if the crew member cannot think of an appropriate response, the generation AI can provide support by suggesting, "In this case, it would be good to respond like this." The evaluation feedback unit evaluates the response quality of the crew supported by the response support unit and provides feedback. For example, the generation AI sets evaluation points for response quality and provides feedback. The input to the generation AI is a prompt including the crew's response content and evaluation criteria, and the generation AI performs evaluation and feedback based on the prompt. For example, it evaluates whether the crew's response was appropriate and provides feedback such as "This was good" or "This needs improvement." In this way, the role-playing system according to the embodiment can improve the crew's response skills.

[0030] The role-play pattern learning unit can analyze the user's past response history and generate individually customized role-play scenarios. For example, in the role-play pattern learning unit, the generation AI collects the user's past response history and identifies frequently occurring problems and questions. Based on this, it generates role-play scenarios that are optimal for the user and provides individual training. The role-play pattern learning unit also analyzes the user's past response history and identifies specific patterns and trends. This generates customized role-play scenarios that allow the user to focus on practicing scenarios that they are weak at. In the role-play pattern learning unit, the generation AI also generates individual role-play scenarios based on the user's past response history. For example, it creates scenarios related to specific complaint handling or product explanations, allowing the user to practice in a format that is close to actual work. This makes it possible to provide the user with the optimal role-play scenario.

[0031] The role-playing pattern learning unit learns role-playing patterns from different cultures and languages, enabling it to handle international customer service scenarios. For example, the generation AI of the role-playing pattern learning unit learns role-playing patterns from different cultures and languages ​​to generate international customer service scenarios. For example, it provides scenarios that support multiple languages, such as English and Chinese. The role-playing pattern learning unit also learns the behavior and reactions of customers from different cultural backgrounds and generates role-playing scenarios based on that. For example, it creates scenarios that include culture-specific complaint handling and product explanations. The role-playing pattern learning unit also uses the generation AI to generate multilingual role-playing scenarios, providing training for users to improve their international customer service skills. For example, users can practice responding to customers in different languages. This makes it possible to handle international customer service scenarios.

[0032] The role-playing pattern learning unit can provide a multimodal role-playing experience using visuals and audio in addition to the role-playing scenario. For example, the generation AI in the role-playing pattern learning unit adds visual elements to the role-playing scenario, allowing the user to visually understand the scenario. For example, it displays product images and customer facial expressions. The role-playing pattern learning unit also uses speech recognition technology to allow the generation AI to analyze the user's responses via audio and provide feedback in real time. For example, it points out areas for improvement in pronunciation and intonation. The role-playing pattern learning unit also provides a multimodal role-playing experience that combines visuals and audio, allowing the user to practice in a more realistic environment. For example, it reproduces the sounds and background noise of an actual store. This makes it possible to provide a multimodal role-playing experience using visuals and audio.

[0033] The role-playing pattern learning unit can learn role-playing patterns from different industries, enabling application in a wide range of fields. For example, the role-playing pattern learning unit's generation AI learns role-playing patterns from the medical industry and provides training scenarios for medical professionals. For example, it generates scenarios for patient care and medical consultations. The role-playing pattern learning unit also learns role-playing patterns from the financial industry and provides training scenarios for bank employees and insurance agents. For example, it generates scenarios for loan consultations and insurance contracts. The role-playing pattern learning unit also learns role-playing patterns from the education industry and provides training scenarios for teachers and educational staff. For example, it generates scenarios for parent-teacher conferences and class progress. This enables application in a wide range of fields.

[0034] The response support unit can analyze users' response patterns in real time and provide instant feedback. For example, the response support unit uses a generation AI to analyze users' response patterns in real time and provide instant feedback on appropriate responses and areas for improvement. For example, if a user responds incorrectly, the unit will present the correct response. The response support unit also monitors users' response patterns in real time and builds a system in which the generation AI provides instant feedback. For example, if the flow of the response is not smooth, the unit will point out areas for improvement. The response support unit also uses a generation AI to analyze users' response patterns in real time and provide instant feedback, allowing users to improve their response skills on the spot. For example, the unit will point out areas for improvement in the tone of the response or the use of language. This allows the unit to analyze users' response patterns in real time and provide instant feedback.

[0035] The customer support department can use past customer service data to identify a user's weaknesses and provide focused training. In the customer service support department, for example, the generation AI analyzes past customer service data to identify a user's weaknesses. For example, if a response in a specific scenario is inappropriate, the department will focus on training that scenario. In addition, the customer service support department can identify a user's weaknesses based on the user's past customer service data and provide an individually customized training plan. For example, for a user who is not good at handling specific complaints, the department can have the user focus on practicing complaint handling scenarios. In addition, the customer support department can build a system in which the generation AI uses past customer service data to identify a user's weaknesses and provide focused training. For example, the department can identify response patterns that the user frequently makes mistakes in and provide training aimed at improving those patterns. This allows the customer's weaknesses to be identified and focused training to be provided.

[0036] The customer service support unit can visualize the customer service flow, allowing users to intuitively understand it. For example, the customer service support unit builds a system in which the generation AI visualizes the customer service flow, allowing users to intuitively understand it. For example, the customer service support unit displays the customer service flow using a flowchart or mind map. The customer service support unit also allows users to check their own customer service patterns and identify areas for improvement based on the visualized customer service flow. For example, each step of the customer service process is displayed in a different color. The customer service support unit also supports the improvement of customer service skills by having the generation AI visualize the customer service flow, allowing users to intuitively understand it. For example, the customer service flow is displayed as an animation. This visualizes the customer service flow, allowing users to intuitively understand it.

[0037] The customer service support unit can automatically generate different customer service scenarios, allowing users to respond to a variety of situations. For example, the customer service support unit constructs a system in which a generation AI automatically generates different customer service scenarios, allowing users to respond to a variety of situations. For example, it generates various scenarios such as product explanations and complaint handling. The customer service support unit also trains users to respond to a variety of situations based on the automatically generated customer service scenarios. For example, it provides scenarios that suit different customer types and situations. The customer service support unit also automatically generates different customer service scenarios using the generation AI, allowing users to respond to a variety of situations, allowing them to practice in a manner that is closer to actual customer service situations. For example, it dynamically changes the difficulty and content of the scenarios. This allows users to respond to a variety of situations.

[0038] The evaluation feedback unit can analyze the user's response quality in detail and suggest specific points for improvement. For example, the evaluation feedback unit uses a generation AI to analyze the user's response quality in detail and suggest specific points for improvement. For example, it points out areas for improvement in the tone of the response or the language used. The evaluation feedback unit also builds a system in which the generation AI analyzes the user's response quality in detail and suggests specific points for improvement. For example, it specifically suggests areas for improvement in the flow and content of the response. The evaluation feedback unit also uses a generation AI to analyze the user's response quality in detail and suggest specific points for improvement, allowing the user to improve their response skills on the spot. For example, it suggests areas for improvement for each step of the response. This allows the user's response quality to be analyzed in detail and specific points for improvement to be suggested.

[0039] The evaluation feedback unit can use past feedback data to track the user's growth and support long-term skill development. For example, the evaluation feedback unit constructs a system in which the generation AI analyzes past feedback data and tracks the user's growth. For example, the evaluation feedback unit visualizes the user's progress in skill development based on past feedback. The evaluation feedback unit also provides a training plan based on the user's past feedback data using the generation AI to support long-term skill development. For example, it generates a new training scenario based on past improvements. The evaluation feedback unit also constructs a system in which the generation AI uses past feedback data to track the user's growth and support long-term skill development. For example, it displays the user's growth in graphs and charts. This makes it possible to track the user's growth and support long-term skill development.

[0040] The evaluation feedback unit can visualize the evaluation results of response quality, allowing the user to intuitively understand. The evaluation feedback unit, for example, builds a system in which the generation AI visualizes the evaluation results of response quality, allowing the user to intuitively understand. For example, it displays the evaluation results in graphs or charts. The evaluation feedback unit also enables the user to check their own response quality and identify areas for improvement based on the visualized evaluation results. For example, it displays the evaluation results in color. The evaluation feedback unit also supports the improvement of response skills by having the generation AI visualize the evaluation results of response quality, allowing the user to intuitively understand. For example, it displays the evaluation results in animation. This visualizes the evaluation results of response quality, allowing the user to intuitively understand.

[0041] The evaluation feedback unit can set different evaluation criteria so that users are evaluated from multiple perspectives. For example, the evaluation feedback unit constructs a system in which the generation AI sets different evaluation criteria so that users are evaluated from multiple perspectives. For example, evaluation is based on multiple criteria such as the tone, content, and flow of the response. The evaluation feedback unit also enables users to check their own response quality from multiple perspectives based on the different evaluation criteria and identify areas for improvement. For example, it displays a score for each evaluation criterion. The evaluation feedback unit also supports the improvement of response skills by having the generation AI set different evaluation criteria so that users are evaluated from multiple perspectives. For example, it provides specific feedback for each evaluation criterion. This allows users to be evaluated from multiple perspectives.

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

[0043] The role-play pattern learning unit can analyze a user's past response history and generate individually customized role-play scenarios. For example, the generation AI collects a user's past response history and identifies frequently occurring problems and questions. Based on this, it generates role-play scenarios that are optimal for the user and provides individual training. The role-play pattern learning unit also analyzes a user's past response history and identifies specific patterns and trends. This generates customized role-play scenarios that allow the user to focus on practicing scenarios that they are weak at. The role-play pattern learning unit also generates individual role-play scenarios based on the user's past response history. For example, it creates scenarios related to specific complaint handling or product explanations, allowing the user to practice in a format that is close to actual work. This makes it possible to provide the user with the optimal role-play scenario.

[0044] The role-playing pattern learning unit learns role-playing patterns from different cultures and languages, enabling it to handle international customer service scenarios. For example, the generation AI learns role-playing patterns from different cultures and languages ​​and generates international customer service scenarios. For example, it provides scenarios that support multiple languages, such as English and Chinese. The role-playing pattern learning unit also learns the behavior and reactions of customers from different cultural backgrounds and generates role-playing scenarios based on that. For example, it creates scenarios that include culture-specific complaint handling and product explanations. The role-playing pattern learning unit also uses the generation AI to generate multilingual role-playing scenarios, providing training for users to improve their international customer service skills. For example, users can practice responding to customers in different languages. This makes it possible to handle international customer service scenarios.

[0045] The role-playing pattern learning unit can provide a multimodal role-playing experience using visuals and audio in addition to the role-playing scenario. For example, the generation AI adds visual elements to the role-playing scenario, allowing the user to visually understand the scenario. For example, it displays product images and customer facial expressions. The role-playing pattern learning unit also uses speech recognition technology to allow the generation AI to analyze the user's responses and provide real-time feedback. For example, it can point out areas for improvement in pronunciation and intonation. The role-playing pattern learning unit also provides a multimodal role-playing experience that combines visuals and audio, allowing the user to practice in a more realistic environment. For example, it can reproduce the sounds and background noise of an actual store. This makes it possible to provide a multimodal role-playing experience using visuals and audio.

[0046] The role-playing pattern learning unit can learn role-playing patterns from different industries, enabling application in a wide range of fields. For example, the generation AI can learn role-playing patterns from the medical industry and provide training scenarios for medical professionals. For example, it can generate scenarios for patient care and medical consultations. The role-playing pattern learning unit can also learn role-playing patterns from the financial industry and provide training scenarios for bank employees and insurance agents. For example, it can generate scenarios for loan consultations and insurance contracts. The role-playing pattern learning unit can also learn role-playing patterns from the education industry and provide training scenarios for teachers and educational staff. For example, it can generate scenarios for parent-teacher conferences and class progress. This enables application in a wide range of fields.

[0047] The customer support department can analyze users' response patterns in real time and provide instant feedback. For example, the generation AI can analyze users' response patterns in real time and provide instant feedback on appropriate responses and areas for improvement. For example, if a user responds incorrectly, the correct response can be presented. The customer support department can also monitor users' response patterns in real time and build a system in which the generation AI can provide instant feedback. For example, if the flow of service is not smooth, the generation AI can point out areas for improvement. The customer support department can also analyze users' response patterns in real time and provide instant feedback, allowing users to improve their response skills on the spot. For example, the generation AI can point out areas for improvement in the tone of service or the use of language. This allows the customer support department to analyze users' response patterns in real time and provide instant feedback.

[0048] The customer support department can use past customer service data to identify a user's weaknesses and provide focused training. For example, the generation AI analyzes past customer service data to identify a user's weaknesses. For example, if a response in a particular scenario is inappropriate, training on that scenario will be focused on that scenario. The customer support department can also use the generation AI to identify a user's weaknesses based on the user's past customer service data and provide an individually customized training plan. For example, for a user who is not good at handling particular complaints, the AI ​​can have them practice complaint handling scenarios intensively. The customer support department can also build a system in which the generation AI uses past customer service data to identify a user's weaknesses and provide focused training. For example, the AI ​​can identify response patterns that the user frequently makes mistakes in and provide training to improve those patterns. This allows the user's weaknesses to be identified and training to be focused on.

[0049] The customer service support unit can visualize the customer service flow, allowing users to intuitively understand it. For example, a system can be built in which the generation AI visualizes the customer service flow and allows users to intuitively understand it. For example, the customer service flow can be displayed using a flowchart or mind map. The customer service support unit can also use the visualized customer service flow to check users' own customer service patterns and identify areas for improvement. For example, each step of the customer service flow can be displayed in a different color. The customer service support unit can also support the improvement of customer service skills by having the generation AI visualize the customer service flow and allow users to intuitively understand it. For example, the customer service flow can be displayed as an animation. This visualizes the customer service flow and allows users to intuitively understand it.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The role-playing pattern learning unit learns role-playing patterns. For example, the generation AI learns various role-playing patterns and takes on the role of a customer based on those. In addition, when crew members practice customer service, the generation AI provides various scenarios in which the "customer" plays a role. The input to the generation AI is prompts that include the role-playing scenario and the customer's reaction, and the generation AI generates appropriate responses based on those prompts. For example, it can handle various scenarios, such as "a customer asking a question about a product" or "a customer making a complaint." Step 2: The response support unit supports the crew's response based on the role-playing patterns learned by the role-playing pattern learning unit. For example, the generation AI learns the flow of responses and correct and incorrect answers, and provides support when the crew gets stuck. The input to the generation AI is a prompt that includes the crew's response content and situation, and the generation AI provides appropriate support based on that prompt. For example, if the crew cannot think of an appropriate response, the generation AI will provide support by saying, "In this case, it would be good to answer like this." Step 3: The evaluation and feedback section evaluates the quality of the crew's service supported by the customer service support section and provides feedback. For example, the generation AI sets evaluation points for the quality of the service and provides feedback. The input to the generation AI is a prompt that includes the content of the crew's service and evaluation criteria, and the generation AI evaluates and provides feedback based on the prompt. For example, it evaluates whether the crew's service was appropriate and provides feedback such as "This was good" or "This needs improvement."

[0052] (Example 2) In the role-playing system according to an embodiment of the present invention, AI learns role-playing patterns, plays the role of the customer in the role-play, learns the flow of customer service and correct and incorrect responses, provides support when the crew member gets stuck, sets evaluation points for the quality of customer service, and provides feedback. This allows the role-playing system to improve the crew member's customer service skills.

[0053] The role-playing system according to the embodiment includes a role-playing pattern learning unit, a customer support unit, and an evaluation feedback unit. The role-playing pattern learning unit learns role-playing patterns. For example, the generation AI learns various role-playing patterns and plays the role of a customer based on those patterns. Furthermore, when a crew member practices customer service, the generation AI provides various scenarios in which the "customer" plays a role. The generation AI receives inputs from prompts containing the role-playing scenario and the customer's reaction, and generates appropriate responses based on those prompts. For example, the generation AI can handle various scenarios, such as "a customer asking a question about a product" or "a customer making a complaint." The customer support unit supports the crew member's customer service based on the role-playing patterns learned by the role-playing pattern learning unit. For example, the generation AI learns the flow of customer service and correct and incorrect responses, and provides support when the crew member is stuck. The generation AI receives inputs from prompts containing the crew member's response content and the situation, and provides appropriate support based on those prompts. For example, if the crew member cannot think of an appropriate response, the generation AI can provide support by suggesting, "In this case, it would be good to respond like this." The evaluation feedback unit evaluates the response quality of the crew supported by the response support unit and provides feedback. For example, the generation AI sets evaluation points for response quality and provides feedback. The input to the generation AI is a prompt including the crew's response content and evaluation criteria, and the generation AI performs evaluation and feedback based on the prompt. For example, it evaluates whether the crew's response was appropriate and provides feedback such as "This was good" or "This needs improvement." In this way, the role-playing system according to the embodiment can improve the crew's response skills.

[0054] The role-play pattern learning unit can analyze the user's past response history and generate individually customized role-play scenarios. For example, in the role-play pattern learning unit, the generation AI collects the user's past response history and identifies frequently occurring problems and questions. Based on this, it generates role-play scenarios that are optimal for the user and provides individual training. The role-play pattern learning unit also analyzes the user's past response history and identifies specific patterns and trends. This generates customized role-play scenarios that allow the user to focus on practicing scenarios that they are weak at. In the role-play pattern learning unit, the generation AI also generates individual role-play scenarios based on the user's past response history. For example, it creates scenarios related to specific complaint handling or product explanations, allowing the user to practice in a format that is close to actual work. This makes it possible to provide the user with the optimal role-play scenario.

[0055] The role-playing pattern learning unit learns role-playing patterns from different cultures and languages, enabling it to handle international customer service scenarios. For example, the generation AI of the role-playing pattern learning unit learns role-playing patterns from different cultures and languages ​​to generate international customer service scenarios. For example, it provides scenarios that support multiple languages, such as English and Chinese. The role-playing pattern learning unit also learns the behavior and reactions of customers from different cultural backgrounds and generates role-playing scenarios based on that. For example, it creates scenarios that include culture-specific complaint handling and product explanations. The role-playing pattern learning unit also uses the generation AI to generate multilingual role-playing scenarios, providing training for users to improve their international customer service skills. For example, users can practice responding to customers in different languages. This makes it possible to handle international customer service scenarios.

[0056] The role-playing pattern learning unit can use the emotion estimation function to play the role of a customer according to the user's emotional state. For example, the role-playing pattern learning unit uses the emotion estimation function to have the generation AI analyze the user's emotional state in real time and play the role of a customer accordingly. For example, if the user is nervous, the generation AI will play the role of a kind customer. The role-playing pattern learning unit also dynamically changes the scenario based on the user's emotional state to provide a more realistic role-playing experience. For example, if the user is feeling stressed, the generation AI will start with an easy scenario. The role-playing pattern learning unit also uses the emotion estimation function to have the generation AI play the role of a customer according to the user's emotions, allowing the user to practice in a manner closer to actual customer service situations. For example, if the user is confident, the generation AI will provide a more difficult scenario. This allows the generation AI to play the role of a customer according to the user's emotional state.

[0057] The role-playing pattern learning unit can provide a multimodal role-playing experience using visuals and audio in addition to the role-playing scenario. For example, the generation AI in the role-playing pattern learning unit adds visual elements to the role-playing scenario, allowing the user to visually understand the scenario. For example, it displays product images and customer facial expressions. The role-playing pattern learning unit also uses speech recognition technology to allow the generation AI to analyze the user's responses via audio and provide feedback in real time. For example, it points out areas for improvement in pronunciation and intonation. The role-playing pattern learning unit also provides a multimodal role-playing experience that combines visuals and audio, allowing the user to practice in a more realistic environment. For example, it reproduces the sounds and background noise of an actual store. This makes it possible to provide a multimodal role-playing experience using visuals and audio.

[0058] The role-playing pattern learning unit can learn role-playing patterns from different industries, enabling application in a wide range of fields. For example, the role-playing pattern learning unit's generation AI learns role-playing patterns from the medical industry and provides training scenarios for medical professionals. For example, it generates scenarios for patient care and medical consultations. The role-playing pattern learning unit also learns role-playing patterns from the financial industry and provides training scenarios for bank employees and insurance agents. For example, it generates scenarios for loan consultations and insurance contracts. The role-playing pattern learning unit also learns role-playing patterns from the education industry and provides training scenarios for teachers and educational staff. For example, it generates scenarios for parent-teacher conferences and class progress. This enables application in a wide range of fields.

[0059] The role-playing pattern learning unit can dynamically change the role-playing scenario based on the user's emotions using the emotion estimation function. For example, the role-playing pattern learning unit uses the emotion estimation function to allow the generation AI to analyze the user's emotional state in real time and dynamically change the role-playing scenario accordingly. For example, if the user is nervous, the generation AI starts with an easy scenario. Furthermore, the role-playing pattern learning unit adjusts the difficulty and content of the scenario based on the user's emotional state to provide more effective training. For example, if the user is confident, the generation AI provides a more difficult scenario. Furthermore, the role-playing pattern learning unit uses the emotion estimation function to allow the generation AI to provide a role-playing scenario that corresponds to the user's emotions, allowing the user to practice in a manner that is closer to actual customer service situations. For example, if the user is feeling stressed, the generation AI provides a relaxing scenario. This allows the role-playing scenario to be dynamically changed based on the user's emotions.

[0060] The response support unit can analyze users' response patterns in real time and provide instant feedback. For example, the response support unit uses a generation AI to analyze users' response patterns in real time and provide instant feedback on appropriate responses and areas for improvement. For example, if a user responds incorrectly, the unit will present the correct response. The response support unit also monitors users' response patterns in real time and builds a system in which the generation AI provides instant feedback. For example, if the flow of the response is not smooth, the unit will point out areas for improvement. The response support unit also uses a generation AI to analyze users' response patterns in real time and provide instant feedback, allowing users to improve their response skills on the spot. For example, the unit will point out areas for improvement in the tone of the response or the use of language. This allows the unit to analyze users' response patterns in real time and provide instant feedback.

[0061] The customer support department can use past customer service data to identify a user's weaknesses and provide focused training. In the customer service support department, for example, the generation AI analyzes past customer service data to identify a user's weaknesses. For example, if a response in a specific scenario is inappropriate, the department will focus on training that scenario. In addition, the customer service support department can identify a user's weaknesses based on the user's past customer service data and provide an individually customized training plan. For example, for a user who is not good at handling specific complaints, the department can have the user focus on practicing complaint handling scenarios. In addition, the customer support department can build a system in which the generation AI uses past customer service data to identify a user's weaknesses and provide focused training. For example, the department can identify response patterns that the user frequently makes mistakes in and provide training aimed at improving those patterns. This allows the customer's weaknesses to be identified and focused training to be provided.

[0062] The customer service support unit can use the emotion estimation function to provide customer service advice based on the user's emotional state. For example, the customer service support unit uses the emotion estimation function to have the generation AI analyze the user's emotional state in real time and provide customer service advice based on that. For example, if the user is nervous, the generation AI provides advice to relax. The customer service support unit also builds a system that provides customer service advice based on the user's emotional state and reduces stress. For example, if the user is feeling stressed, the generation AI starts with an easy scenario. The customer service support unit also uses the emotion estimation function to have the generation AI provide customer service advice based on the user's emotions, allowing the user to practice in a manner that is closer to actual customer service situations. For example, if the user is confident, the generation AI provides a more difficult scenario. This makes it possible to provide customer service advice based on the user's emotional state.

[0063] The customer service support unit can visualize the customer service flow, allowing users to intuitively understand it. For example, the customer service support unit builds a system in which the generation AI visualizes the customer service flow, allowing users to intuitively understand it. For example, the customer service support unit displays the customer service flow using a flowchart or mind map. The customer service support unit also allows users to check their own customer service patterns and identify areas for improvement based on the visualized customer service flow. For example, each step of the customer service process is displayed in a different color. The customer service support unit also supports the improvement of customer service skills by having the generation AI visualize the customer service flow, allowing users to intuitively understand it. For example, the customer service flow is displayed as an animation. This visualizes the customer service flow, allowing users to intuitively understand it.

[0064] The customer service support unit can automatically generate different customer service scenarios, allowing users to respond to a variety of situations. For example, the customer service support unit constructs a system in which a generation AI automatically generates different customer service scenarios, allowing users to respond to a variety of situations. For example, it generates various scenarios such as product explanations and complaint handling. The customer service support unit also trains users to respond to a variety of situations based on the automatically generated customer service scenarios. For example, it provides scenarios that suit different customer types and situations. The customer service support unit also automatically generates different customer service scenarios using the generation AI, allowing users to respond to a variety of situations, allowing them to practice in a manner that is closer to actual customer service situations. For example, it dynamically changes the difficulty and content of the scenarios. This allows users to respond to a variety of situations.

[0065] The customer service support unit can use the emotion estimation function to adjust the flow of service based on the user's emotions. For example, the customer service support unit uses the emotion estimation function to have the generation AI analyze the user's emotional state in real time and adjust the flow of service accordingly. For example, if the user is nervous, the generation AI will proceed with the flow of service slowly. The customer service support unit also dynamically changes the flow of service based on the user's emotional state to provide more effective support. For example, if the user is feeling stressed, the generation AI will start with an easy scenario. The customer service support unit also uses the emotion estimation function to have the generation AI provide a flow of service that corresponds to the user's emotions, allowing the user to practice in a manner that is closer to actual customer service situations. For example, if the user is confident, the generation AI will provide a more difficult scenario. This makes it possible to adjust the flow of service based on the user's emotions.

[0066] The evaluation feedback unit can analyze the user's response quality in detail and suggest specific points for improvement. For example, the evaluation feedback unit uses a generation AI to analyze the user's response quality in detail and suggest specific points for improvement. For example, it points out areas for improvement in the tone of the response or the language used. The evaluation feedback unit also builds a system in which the generation AI analyzes the user's response quality in detail and suggests specific points for improvement. For example, it specifically suggests areas for improvement in the flow and content of the response. The evaluation feedback unit also uses a generation AI to analyze the user's response quality in detail and suggest specific points for improvement, allowing the user to improve their response skills on the spot. For example, it suggests areas for improvement for each step of the response. This allows the user's response quality to be analyzed in detail and specific points for improvement to be suggested.

[0067] The evaluation feedback unit can use past feedback data to track the user's growth and support long-term skill development. For example, the evaluation feedback unit constructs a system in which the generation AI analyzes past feedback data and tracks the user's growth. For example, the evaluation feedback unit visualizes the user's progress in skill development based on past feedback. The evaluation feedback unit also provides a training plan based on the user's past feedback data using the generation AI to support long-term skill development. For example, it generates a new training scenario based on past improvements. The evaluation feedback unit also constructs a system in which the generation AI uses past feedback data to track the user's growth and support long-term skill development. For example, it displays the user's growth in graphs and charts. This makes it possible to track the user's growth and support long-term skill development.

[0068] The evaluation feedback unit uses the emotion estimation function to provide feedback based on the user's emotional state, thereby improving motivation. For example, the evaluation feedback unit uses the emotion estimation function to have the generation AI analyze the user's emotional state in real time and provide feedback based on that. For example, if the user is feeling down, the evaluation feedback unit provides encouraging feedback. The evaluation feedback unit also builds a system in which the generation AI provides feedback based on the user's emotional state to improve motivation. For example, if the user is confident, the evaluation feedback unit provides feedback encouraging the user to try harder. The evaluation feedback unit also uses the emotion estimation function to have the generation AI provide feedback according to the user's emotions, allowing the user to practice in a manner similar to an actual customer service situation. For example, if the user is feeling stressed, the evaluation feedback unit provides feedback that helps the user relax. This allows the generation AI to provide feedback based on the user's emotional state and improve motivation.

[0069] The evaluation feedback unit can visualize the evaluation results of response quality, allowing the user to intuitively understand. The evaluation feedback unit, for example, builds a system in which the generation AI visualizes the evaluation results of response quality, allowing the user to intuitively understand. For example, it displays the evaluation results in graphs or charts. The evaluation feedback unit also enables the user to check their own response quality and identify areas for improvement based on the visualized evaluation results. For example, it displays the evaluation results in color. The evaluation feedback unit also supports the improvement of response skills by having the generation AI visualize the evaluation results of response quality, allowing the user to intuitively understand. For example, it displays the evaluation results in animation. This visualizes the evaluation results of response quality, allowing the user to intuitively understand.

[0070] The evaluation feedback unit can set different evaluation criteria so that users are evaluated from multiple perspectives. For example, the evaluation feedback unit constructs a system in which the generation AI sets different evaluation criteria so that users are evaluated from multiple perspectives. For example, evaluation is based on multiple criteria such as the tone, content, and flow of the response. The evaluation feedback unit also enables users to check their own response quality from multiple perspectives based on the different evaluation criteria and identify areas for improvement. For example, it displays a score for each evaluation criterion. The evaluation feedback unit also supports the improvement of response skills by having the generation AI set different evaluation criteria so that users are evaluated from multiple perspectives. For example, it provides specific feedback for each evaluation criterion. This allows users to be evaluated from multiple perspectives.

[0071] The evaluation feedback unit can use the emotion estimation function to adjust the content of the feedback based on the user's emotions. For example, the evaluation feedback unit uses the emotion estimation function to have the generation AI analyze the user's emotional state in real time and adjust the content of the feedback based on that. For example, if the user is feeling down, the generation AI provides encouraging feedback. The evaluation feedback unit also dynamically changes the content of the feedback based on the user's emotional state, building a system that encourages more effective improvement. For example, if the user is confident, the generation AI provides feedback encouraging further challenges. The evaluation feedback unit also uses the emotion estimation function to have the generation AI provide feedback according to the user's emotions, allowing the user to practice in a manner that is closer to actual customer service situations. For example, if the user is feeling stressed, the generation AI provides relaxing feedback. This makes it possible to adjust the content of the feedback based on the user's emotions.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The role-play pattern learning unit can analyze a user's past response history and generate individually customized role-play scenarios. For example, the generation AI collects a user's past response history and identifies frequently occurring problems and questions. Based on this, it generates role-play scenarios that are optimal for the user and provides individual training. The role-play pattern learning unit also analyzes a user's past response history and identifies specific patterns and trends. This generates customized role-play scenarios that allow the user to focus on practicing scenarios that they are weak at. The role-play pattern learning unit also generates individual role-play scenarios based on the user's past response history. For example, it creates scenarios related to specific complaint handling or product explanations, allowing the user to practice in a format that is close to actual work. This makes it possible to provide the user with the optimal role-play scenario.

[0074] The role-playing pattern learning unit learns role-playing patterns from different cultures and languages, enabling it to handle international customer service scenarios. For example, the generation AI learns role-playing patterns from different cultures and languages ​​and generates international customer service scenarios. For example, it provides scenarios that support multiple languages, such as English and Chinese. The role-playing pattern learning unit also learns the behavior and reactions of customers from different cultural backgrounds and generates role-playing scenarios based on that. For example, it creates scenarios that include culture-specific complaint handling and product explanations. The role-playing pattern learning unit also uses the generation AI to generate multilingual role-playing scenarios, providing training for users to improve their international customer service skills. For example, users can practice responding to customers in different languages. This makes it possible to handle international customer service scenarios.

[0075] The role-playing pattern learning unit uses the emotion estimation function to play the role of a customer according to the user's emotional state. For example, using the emotion estimation function, the generation AI analyzes the user's emotional state in real time and plays the role of a customer accordingly. For example, if the user is nervous, it plays the role of a kind customer. The role-playing pattern learning unit also dynamically changes the scenario based on the user's emotional state to provide a more realistic role-playing experience. For example, if the user is feeling stressed, it starts with an easy scenario. The role-playing pattern learning unit also uses the emotion estimation function to have the generation AI play the role of a customer according to the user's emotions, allowing the user to practice in a manner closer to actual customer service situations. For example, if the user is confident, it provides a more difficult scenario. This allows the generation AI to play the role of a customer according to the user's emotional state.

[0076] The role-playing pattern learning unit can provide a multimodal role-playing experience using visuals and audio in addition to the role-playing scenario. For example, the generation AI adds visual elements to the role-playing scenario, allowing the user to visually understand the scenario. For example, it displays product images and customer facial expressions. The role-playing pattern learning unit also uses speech recognition technology to allow the generation AI to analyze the user's responses and provide real-time feedback. For example, it can point out areas for improvement in pronunciation and intonation. The role-playing pattern learning unit also provides a multimodal role-playing experience that combines visuals and audio, allowing the user to practice in a more realistic environment. For example, it can reproduce the sounds and background noise of an actual store. This makes it possible to provide a multimodal role-playing experience using visuals and audio.

[0077] The role-playing pattern learning unit can learn role-playing patterns from different industries, enabling application in a wide range of fields. For example, the generation AI can learn role-playing patterns from the medical industry and provide training scenarios for medical professionals. For example, it can generate scenarios for patient care and medical consultations. The role-playing pattern learning unit can also learn role-playing patterns from the financial industry and provide training scenarios for bank employees and insurance agents. For example, it can generate scenarios for loan consultations and insurance contracts. The role-playing pattern learning unit can also learn role-playing patterns from the education industry and provide training scenarios for teachers and educational staff. For example, it can generate scenarios for parent-teacher conferences and class progress. This enables application in a wide range of fields.

[0078] The role-playing pattern learning unit can use the emotion estimation function to dynamically change the role-playing scenario based on the user's emotions. For example, using the emotion estimation function, the generation AI analyzes the user's emotional state in real time and dynamically changes the role-playing scenario accordingly. For example, if the user is nervous, it starts with an easy scenario. The role-playing pattern learning unit also adjusts the difficulty and content of the scenario based on the user's emotional state to provide more effective training. For example, if the user is confident, it provides a more difficult scenario. The role-playing pattern learning unit also uses the emotion estimation function to provide role-playing scenarios that correspond to the user's emotions, allowing the user to practice in a manner that is closer to actual customer service situations. For example, if the user is feeling stressed, it provides a scenario that helps them relax. This allows the role-playing scenario to be dynamically changed based on the user's emotions.

[0079] The customer support department can analyze users' response patterns in real time and provide instant feedback. For example, the generation AI can analyze users' response patterns in real time and provide instant feedback on appropriate responses and areas for improvement. For example, if a user responds incorrectly, the correct response can be presented. The customer support department can also monitor users' response patterns in real time and build a system in which the generation AI can provide instant feedback. For example, if the flow of service is not smooth, the generation AI can point out areas for improvement. The customer support department can also analyze users' response patterns in real time and provide instant feedback, allowing users to improve their response skills on the spot. For example, the generation AI can point out areas for improvement in the tone of service or the use of language. This allows the customer support department to analyze users' response patterns in real time and provide instant feedback.

[0080] The customer support department can use past customer service data to identify a user's weaknesses and provide focused training. For example, the generation AI analyzes past customer service data to identify a user's weaknesses. For example, if a response in a particular scenario is inappropriate, training on that scenario will be focused on that scenario. The customer support department can also use the generation AI to identify a user's weaknesses based on the user's past customer service data and provide an individually customized training plan. For example, for a user who is not good at handling particular complaints, the AI ​​can have them practice complaint handling scenarios intensively. The customer support department can also build a system in which the generation AI uses past customer service data to identify a user's weaknesses and provide focused training. For example, the AI ​​can identify response patterns that the user frequently makes mistakes in and provide training to improve those patterns. This allows the user's weaknesses to be identified and training to be focused on.

[0081] The customer service support unit can use the emotion estimation function to provide customer service advice based on the user's emotional state. For example, using the emotion estimation function, the generation AI analyzes the user's emotional state in real time and provides customer service advice based on that. For example, if the user is nervous, it provides advice to relax. The customer service support unit also builds a system in which the generation AI provides customer service advice based on the user's emotional state to reduce stress. For example, if the user is feeling stressed, it starts with an easy scenario. The customer service support unit also uses the emotion estimation function to allow the generation AI to provide customer service advice based on the user's emotions, allowing the user to practice in a manner that is closer to actual customer service situations. For example, if the user is confident, it provides a more difficult scenario. This makes it possible to provide customer service advice based on the user's emotional state.

[0082] The customer service support unit can visualize the customer service flow, allowing users to intuitively understand it. For example, a system can be built in which the generation AI visualizes the customer service flow and allows users to intuitively understand it. For example, the customer service flow can be displayed using a flowchart or mind map. The customer service support unit can also use the visualized customer service flow to check users' own customer service patterns and identify areas for improvement. For example, each step of the customer service flow can be displayed in a different color. The customer service support unit can also support the improvement of customer service skills by having the generation AI visualize the customer service flow and allow users to intuitively understand it. For example, the customer service flow can be displayed as an animation. This visualizes the customer service flow and allows users to intuitively understand it.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The role-playing pattern learning unit learns role-playing patterns. For example, the generation AI learns various role-playing patterns and takes on the role of a customer based on those. In addition, when crew members practice customer service, the generation AI provides various scenarios in which the "customer" plays a role. The input to the generation AI is prompts that include the role-playing scenario and the customer's reaction, and the generation AI generates appropriate responses based on those prompts. For example, it can handle various scenarios, such as "a customer asking a question about a product" or "a customer making a complaint." Step 2: The response support unit supports the crew's response based on the role-playing patterns learned by the role-playing pattern learning unit. For example, the generation AI learns the flow of responses and correct and incorrect answers, and provides support when the crew gets stuck. The input to the generation AI is a prompt that includes the crew's response content and situation, and the generation AI provides appropriate support based on that prompt. For example, if the crew cannot think of an appropriate response, the generation AI will provide support by saying, "In this case, it would be good to answer like this." Step 3: The evaluation and feedback section evaluates the quality of the crew's service supported by the customer service support section and provides feedback. For example, the generation AI sets evaluation points for the quality of the service and provides feedback. The input to the generation AI is a prompt that includes the content of the crew's service and evaluation criteria, and the generation AI evaluates and provides feedback based on the prompt. For example, it evaluates whether the crew's service was appropriate and provides feedback such as "This was good" or "This needs improvement."

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 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 role-playing pattern learning unit that learns role-playing patterns; a response support unit that supports crew responses based on the role-play patterns learned by the role-play pattern learning unit; an evaluation feedback unit that evaluates the response quality of the crew supported by the response support unit and provides feedback; A system characterized by:

2. The role-play pattern learning unit Analyzes the user's past response history and generates individually customized role-play scenarios 2. The system of claim 1.

3. The role-play pattern learning unit Learn role-playing patterns from different cultures and languages, and be able to handle international customer service scenarios.

2. The system of claim 1.

4. The role-play pattern learning unit Acting as the customer according to the user's emotional state 2. The system of claim 1.

5. The role-play pattern learning unit In addition to the role-playing scenario, it provides a multimodal role-playing experience using visuals and audio.

2. The system of claim 1.

6. The role-play pattern learning unit Learn role-playing patterns from different industries and apply them in a wide range of fields 2. The system of claim 1.

7. The role-play pattern learning unit Dynamically changing role-play scenarios based on user emotions 2. The system of claim 1.

8. The response support unit Analyze users' response patterns in real time and provide immediate feedback 2. The system of claim 1.

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