System

Abstract

An object of a system according to an embodiment is to fill a gap between training and an actual site by generating a persona using generative AI and performing a simulation.SOLUTION: A system according to an embodiment includes a persona generation unit, a simulation execution unit, and a feedback provision unit. The persona generation unit generates a persona using the generation AI. The simulation execution unit performs a simulation on the basis of the persona generated by the persona generation unit. The feedback providing unit provides feedback based on a result of the simulation performed by the simulation execution unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a problem that the gap between training and actual on-site experience made it difficult to provide appropriate guidance and respond to complaints.

[0005] The system of the embodiment aims to bridge the gap between training and actual work by generating personas using generative AI and conducting simulations. [Means for solving the problem]

[0006] A system according to an embodiment includes a persona generation unit, a simulation implementation unit, and a feedback provision unit. The persona generation unit generates a persona using a generation AI. The simulation implementation unit performs a simulation based on the persona generated by the persona generation unit. The feedback provision unit provides feedback based on the results of the simulation performed by the simulation implementation unit. [Effects of the Invention]

[0007] The system according to the embodiment uses generative AI to generate personas and conduct simulations, thereby bridging the gap between training and actual work. [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 order to bridge the gap between training and the real world, the system according to the present invention allows trainees to gain experience in the real world in advance by conducting simulations using personas created using generative AI. This allows trainees to acquire skills to deal with various situations.

[0029] A system according to an embodiment includes a persona generation unit, a simulation implementation unit, and a feedback provision unit. The persona generation unit generates personas using a generation AI. For example, the generation AI uses technologies such as GPT-3 and BERT to generate personas with attributes such as age, gender, occupation, hobbies, and personality. The simulation implementation unit performs a simulation based on the persona generated by the persona generation unit. For example, if the persona asks, "I don't know how to use a product," the simulation unit performs a simulation in which a trainee provides appropriate guidance. The feedback provision unit provides feedback based on the results of the simulation performed by the simulation implementation unit. For example, if the trainee's response was appropriate, the feedback provision unit evaluates the trainee as "good response," and if improvement is needed, the feedback provision unit provides advice such as "this part should be improved." This allows the system to enable trainees to acquire skills for dealing with various situations.

[0030] The persona generation unit can generate personas with attributes such as age, gender, occupation, hobbies, and personality. The persona generation unit has the ability to change the persona's emotional state in real time, for example, by using the generation AI. For example, if a persona is dissatisfied with a question, it will express that emotion through facial expressions and words. The generation AI also uses an emotion estimation algorithm to change the persona's emotional state in real time. For example, if a persona makes a complaint, it will express anger or irritation. By changing the persona's emotional state in real time, the generation AI also allows trainees to learn how to respond based on emotions. For example, if a persona expresses gratitude, the trainee will respond appropriately. This allows trainees to acquire the skills to respond to various situations by generating diverse personas.

[0031] The simulation implementation unit can perform simulations in which trainees provide appropriate guidance when a persona asks about how to use a product. In the simulation implementation unit, for example, the generation AI analyzes past customer data and learns customer behavior patterns. For example, it analyzes the types of questions and complaints that customers of specific age groups or occupations make. The generation AI also generates personas that reflect actual customer behavior patterns based on past customer data. For example, it creates personas for customers who frequently complain or who request detailed explanations. The generation AI also analyzes past customer data and generates personas that reflect customer behavior patterns, allowing trainees to perform simulations that are closer to actual customer interactions. For example, it learns how customers react to specific questions. This allows trainees to acquire the skills to provide appropriate guidance in response to specific questions.

[0032] The feedback providing unit can evaluate the trainee's response and provide feedback. In this feedback providing unit, for example, the generation AI adds health conditions to the persona's attributes. For example, it generates personas with chronic illnesses or personas that are highly health-conscious. The generation AI also adds lifestyle habits to the persona's attributes. For example, it generates personas that lead a nocturnal lifestyle or personas that have an exercise habit. The generation AI also adds detailed information to the persona's attributes, allowing the trainee to respond to a wider variety of scenarios. For example, it can respond to questions or complaints based on specific health conditions or lifestyle habits. This allows the trainee to evaluate their own response and learn areas for improvement.

[0033] The persona generation unit can analyze past customer data and generate personas that reflect actual customer behavior patterns. In the persona generation unit, for example, the generation AI analyzes past customer data and learns customer behavior patterns. For example, it analyzes the types of questions and complaints that customers of a particular age group or occupation make. The generation AI also generates personas that reflect actual customer behavior patterns based on past customer data. For example, it creates personas for customers who frequently complain or customers who request detailed explanations. The generation AI also analyzes past customer data and generates personas that reflect customer behavior patterns, allowing trainees to perform simulations that are closer to actual customer interactions. For example, it learns how customers react to specific questions. This allows trainees to perform simulations that are closer to actual customer interactions.

[0034] The persona generation unit can add detailed information about health conditions and lifestyle habits to the persona's attributes. In the persona generation unit, for example, the generation AI adds health conditions to the persona's attributes. For example, it generates personas with chronic illnesses or personas who are highly health-conscious. The generation AI also adds lifestyle habits to the persona's attributes. For example, it generates personas who lead a nocturnal lifestyle or personas who have an exercise habit. Furthermore, by adding detailed information to the persona's attributes, the generation AI enables trainees to respond to a wider variety of scenarios. For example, it can respond to questions or complaints based on specific health conditions or lifestyle habits. This enables trainees to respond to a wider variety of scenarios.

[0035] The persona generation unit can generate personas with different cultural backgrounds or languages. In the persona generation unit, for example, the generation AI generates personas with different cultural backgrounds. For example, it creates personas with cultural backgrounds such as Asia, Europe, and Africa. The generation AI also generates personas that speak different languages. For example, it creates personas that speak English, French, Chinese, and other languages. The generation AI also generates personas with different cultural backgrounds and languages, allowing trainees to acquire international response skills. For example, responding to questions and complaints based on different cultures and languages. This allows trainees to acquire international response skills.

[0036] The persona generation unit incorporates voice and facial expression data into the generation of the persona, enabling interactive simulations through sight and hearing. For example, the generation AI in the persona generation unit generates voice data for the persona, and asks questions or makes complaints by voice during the simulation. For example, the persona requests an explanation of a product by voice. The generation AI also generates facial expression data for the persona, and expresses emotions through facial expressions during the simulation. For example, the persona frowns when dissatisfied. Furthermore, by incorporating voice and facial expression data, the generation AI enables trainees to participate in interactive simulations through sight and hearing. For example, it responds appropriately based on the persona's facial expressions and voice. This enables trainees to participate in interactive simulations through sight and hearing.

[0037] The simulation implementation unit can evaluate the trainee's responses in real time during the simulation and provide instant feedback. For example, the generation AI in the simulation unit evaluates the trainee's responses in real time during the simulation and provides instant feedback. For example, if the trainee responds appropriately, it will display "Good response." The generation AI also evaluates the trainee's responses during the simulation and points out areas for improvement in real time. For example, it provides advice such as "It would be good to improve this part." The generation AI also evaluates the trainee's responses in real time during the simulation and provides instant feedback, allowing the trainee to learn areas for improvement on the spot. For example, it may instruct the trainee to "explain more carefully" in the middle of a response. This allows the trainee to learn areas for improvement on the spot.

[0038] The simulation implementation unit can set up situations in which multiple personas appear simultaneously in a simulation scenario. For example, the simulation implementation unit has the generation AI make multiple personas appear simultaneously in a simulation scenario. For example, it sets up a scenario in which multiple customers ask questions or make complaints at the same time. The generation AI also has multiple personas appear in a simulation scenario to help trainees develop simultaneous response skills. For example, they can respond to different questions or complaints at the same time. The generation AI also has multiple personas appear simultaneously in a simulation scenario, allowing trainees to develop complex response skills. For example, they can respond to a situation in which multiple customers have different problems. This allows trainees to develop complex response skills.

[0039] The simulation implementation unit can add emergency situations or unexpected problems to the simulation scenario. In the simulation implementation unit, for example, the generation AI adds emergency situations to the simulation scenario. For example, it simulates how to respond if a customer suddenly falls ill. The generation AI also adds unexpected problems to the simulation scenario. For example, it simulates how to respond if a system failure occurs. The generation AI also adds emergency situations and unexpected problems to the simulation scenario, allowing trainees to develop the skills to respond quickly and appropriately. For example, responding to a sudden complaint from a customer. This allows trainees to develop the skills to respond quickly and appropriately.

[0040] The simulation implementation unit can share the results of the simulation with other trainees and develop it into a group work format where they can jointly come up with solutions to problems. For example, the simulation implementation unit provides a function where the generation AI can share the results of the simulation with other trainees. For example, they can share the simulation log and jointly come up with solutions to problems. The generation AI can also provide a scenario where they can use the results of the simulation to come up with solutions to problems in a group work format. For example, multiple trainees can work together to come up with ways to deal with complaints. The generation AI can also share the results of the simulation with other trainees and develop it into a group work format where they can jointly come up with solutions to problems, allowing trainees to develop the skills to solve problems collaboratively. For example, they can hold discussions based on the results of the simulation. This allows trainees to develop the skills to solve problems collaboratively.

[0041] The feedback providing unit can compare the feedback content with past trainee data to clarify individual growth points. In the feedback providing unit, for example, the generation AI compares the feedback content with past trainee data to clarify individual growth points. For example, it evaluates the trainee's progress based on past data. The generation AI also analyzes past trainee data and reflects this in the feedback content. For example, it compares it with how other trainees responded to the same scenario and points out areas for improvement. The generation AI also compares the feedback content with past trainee data to clarify individual growth points, allowing the trainee to feel their own growth. For example, it compares it with past data and specifically shows which areas have improved. This allows the trainee to feel their own growth.

[0042] The feedback providing unit can include detailed specific improvement measures and points to try in the next simulation in the feedback. For example, the generation AI may include specific improvement measures in the feedback. For example, it may provide specific advice such as, "Try to explain things more carefully next time." The generation AI may also include detailed points to try in the next simulation in the feedback. For example, it may set a specific goal such as, "Aim to respond more quickly to customer questions next time." The generation AI may also include detailed specific improvement measures and points to try in the next simulation in the feedback, allowing the trainee to create a specific action plan. For example, it may provide specific instructions such as, "Next time, pay more attention to the customer's emotions and respond appropriately." This allows the trainee to create a specific action plan.

[0043] The feedback providing unit can provide feedback in audio or video format, making it easier for trainees to understand through their eyes and ears. In the feedback providing unit, for example, the generation AI provides feedback in audio format. For example, it may give advice in audio format such as "It would be good to improve this part." The generation AI may also provide feedback in video format. For example, it may give advice while showing specific improvement measures in a video. The generation AI may also provide feedback in audio or video format, making it easier for trainees to understand through their eyes and ears. For example, it may give feedback while showing specific areas for improvement in an audio or video format. This makes it easier for trainees to understand through their eyes and ears.

[0044] The feedback providing unit can share the feedback content with other trainees and develop it into a discussion format where they can jointly think up improvement measures. The feedback providing unit, for example, provides a function where the generation AI can share the feedback content with other trainees. For example, they can share the feedback content and hold a discussion where they can jointly think up improvement measures. The generation AI can also provide a scenario where improvement measures can be thought up in a discussion format based on the feedback content. For example, multiple trainees can work together to think up improvement measures. The generation AI can also share the feedback content with other trainees and develop it into a discussion format where they can jointly think up improvement measures, allowing trainees to develop the skills to solve problems collaboratively. For example, they can hold a discussion based on the feedback content. This allows trainees to develop the skills to solve problems collaboratively.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The system can be equipped with a cultural understanding estimation function to help trainees improve their skills in dealing with customers from different cultural backgrounds. For example, the system can provide advice on how trainees can respond appropriately to personas from different cultural backgrounds. The cultural understanding estimation function can also be used to evaluate trainees' intercultural understanding and provide additional learning resources as needed. Furthermore, trainees can improve their international dealing skills by conducting intercultural interaction simulations.

[0047] The system can be equipped with a needs estimation function to help trainees improve their skills in accurately grasping customer needs. For example, trainees can estimate customer needs during a simulation and make proposals that meet those needs at the appropriate time. The needs estimation function can also be used to provide specific advice to trainees to help them accurately grasp customer needs. Furthermore, by conducting a simulation to help trainees accurately grasp customer needs, trainees can improve their needs analysis skills.

[0048] The system can be equipped with a lifestyle estimation function to improve the trainee's skills in making proposals tailored to the customer's lifestyle. For example, the trainee can estimate the customer's lifestyle during a simulation and make proposals tailored to the lifestyle at the appropriate time. The lifestyle estimation function can also be used to provide specific advice to the trainee to help them make proposals tailored to the customer's lifestyle. Furthermore, by conducting a simulation in which the trainee makes proposals tailored to the customer's lifestyle, the trainee's proposal skills can be improved.

[0049] The system can be equipped with a purchase history estimation function to improve trainees' skills in making proposals based on customer purchase history. For example, trainees can estimate a customer's purchase history during a simulation and make proposals based on the purchase history at the appropriate time. The purchase history estimation function can also be used to provide trainees with specific advice to help them make proposals based on the customer's purchase history. Furthermore, by conducting a simulation in which trainees make proposals based on the customer's purchase history, trainees can improve their proposal skills.

[0050] The system can be equipped with a feedback estimation function to improve trainees' skills in making improvement proposals based on customer feedback. For example, trainees can estimate customer feedback during a simulation and make improvement proposals based on the feedback at the appropriate time. The feedback estimation function can also be used to provide specific advice to trainees to help them make improvement proposals based on customer feedback. Furthermore, by conducting a simulation in which trainees make improvement proposals based on customer feedback, trainees can improve their improvement proposal skills.

[0051] The system can be equipped with a behavioral pattern estimation function to improve trainees' skills in making predictions based on customer behavioral patterns. For example, trainees can estimate customer behavioral patterns during a simulation and make predictions based on the behavioral patterns at appropriate times. The behavioral pattern estimation function can also be used to provide trainees with specific advice to help them make predictions based on customer behavioral patterns. Furthermore, trainees can improve their prediction skills by running a simulation to make predictions based on customer behavioral patterns.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The persona generation unit generates a persona using a generation AI. For example, the generation AI uses technologies such as GPT-3 or BERT to generate a persona with attributes such as age, gender, occupation, hobbies, and personality. Step 2: The simulation implementation department conducts a simulation based on the persona generated by the persona generation department. For example, if a persona asks, "I don't know how to use the product," a simulation is conducted in which a trainee provides appropriate guidance. Step 3: The feedback department provides feedback based on the results of the simulation conducted by the simulation implementation department. For example, if the trainee's response was appropriate, they will be evaluated as "good response," and if improvement is needed, they will be given advice such as "this part should be improved."

[0054] (Example 2) In order to bridge the gap between training and the real world, the system according to the present invention allows trainees to gain experience in the real world in advance by conducting simulations using personas created using generative AI. This allows trainees to acquire skills to deal with various situations.

[0055] A system according to an embodiment includes a persona generation unit, a simulation implementation unit, and a feedback provision unit. The persona generation unit generates personas using a generation AI. For example, the generation AI uses technologies such as GPT-3 and BERT to generate personas with attributes such as age, gender, occupation, hobbies, and personality. The simulation implementation unit performs a simulation based on the persona generated by the persona generation unit. For example, if the persona asks, "I don't know how to use a product," the simulation unit performs a simulation in which a trainee provides appropriate guidance. The feedback provision unit provides feedback based on the results of the simulation performed by the simulation implementation unit. For example, if the trainee's response was appropriate, the feedback provision unit evaluates the trainee as "good response," and if improvement is needed, the feedback provision unit provides advice such as "this part should be improved." This allows the system to enable trainees to acquire skills for dealing with various situations.

[0056] The persona generation unit can generate personas with attributes such as age, gender, occupation, hobbies, and personality. The persona generation unit has the ability to change the persona's emotional state in real time, for example, by using the generation AI. For example, if a persona is dissatisfied with a question, it will express that emotion through facial expressions and words. The generation AI also uses an emotion estimation algorithm to change the persona's emotional state in real time. For example, if a persona makes a complaint, it will express anger or irritation. By changing the persona's emotional state in real time, the generation AI also allows trainees to learn how to respond based on emotions. For example, if a persona expresses gratitude, the trainee will respond appropriately. This allows trainees to acquire the skills to respond to various situations by generating diverse personas.

[0057] The simulation implementation unit can perform simulations in which trainees provide appropriate guidance when a persona asks about how to use a product. In the simulation implementation unit, for example, the generation AI analyzes past customer data and learns customer behavior patterns. For example, it analyzes the types of questions and complaints that customers of specific age groups or occupations make. The generation AI also generates personas that reflect actual customer behavior patterns based on past customer data. For example, it creates personas for customers who frequently complain or who request detailed explanations. The generation AI also analyzes past customer data and generates personas that reflect customer behavior patterns, allowing trainees to perform simulations that are closer to actual customer interactions. For example, it learns how customers react to specific questions. This allows trainees to acquire the skills to provide appropriate guidance in response to specific questions.

[0058] The feedback providing unit can evaluate the trainee's response and provide feedback. In this feedback providing unit, for example, the generation AI adds health conditions to the persona's attributes. For example, it generates personas with chronic illnesses or personas that are highly health-conscious. The generation AI also adds lifestyle habits to the persona's attributes. For example, it generates personas that lead a nocturnal lifestyle or personas that have an exercise habit. The generation AI also adds detailed information to the persona's attributes, allowing the trainee to respond to a wider variety of scenarios. For example, it can respond to questions or complaints based on specific health conditions or lifestyle habits. This allows the trainee to evaluate their own response and learn areas for improvement.

[0059] The persona generation unit incorporates an emotion estimation function, allowing the persona's emotional state to be changed in real time. For example, the persona generation unit has the function of allowing the generation AI to change the persona's emotional state in real time. For example, if the persona is dissatisfied with a question, it will express that emotion through facial expressions and words. The generation AI also uses an emotion estimation algorithm to change the persona's emotional state in real time. For example, when the persona makes a complaint, it will express anger or irritation. The generation AI also changes the persona's emotional state in real time, allowing trainees to learn how to respond based on emotions. For example, if the persona expresses gratitude, the trainee will respond appropriately. This allows trainees to learn how to respond based on emotions.

[0060] The persona generation unit can analyze past customer data and generate personas that reflect actual customer behavior patterns. In the persona generation unit, for example, the generation AI analyzes past customer data and learns customer behavior patterns. For example, it analyzes the types of questions and complaints that customers of a particular age group or occupation make. The generation AI also generates personas that reflect actual customer behavior patterns based on past customer data. For example, it creates personas for customers who frequently complain or customers who request detailed explanations. The generation AI also analyzes past customer data and generates personas that reflect customer behavior patterns, allowing trainees to perform simulations that are closer to actual customer interactions. For example, it learns how customers react to specific questions. This allows trainees to perform simulations that are closer to actual customer interactions.

[0061] The persona generation unit can add detailed information about health conditions and lifestyle habits to the persona's attributes. In the persona generation unit, for example, the generation AI adds health conditions to the persona's attributes. For example, it generates personas with chronic illnesses or personas who are highly health-conscious. The generation AI also adds lifestyle habits to the persona's attributes. For example, it generates personas who lead a nocturnal lifestyle or personas who have an exercise habit. Furthermore, by adding detailed information to the persona's attributes, the generation AI enables trainees to respond to a wider variety of scenarios. For example, it can respond to questions or complaints based on specific health conditions or lifestyle habits. This enables trainees to respond to a wider variety of scenarios.

[0062] The persona generation unit can generate personas with different cultural backgrounds or languages. In the persona generation unit, for example, the generation AI generates personas with different cultural backgrounds. For example, it creates personas with cultural backgrounds such as Asia, Europe, and Africa. The generation AI also generates personas that speak different languages. For example, it creates personas that speak English, French, Chinese, and other languages. The generation AI also generates personas with different cultural backgrounds and languages, allowing trainees to acquire international response skills. For example, responding to questions and complaints based on different cultures and languages. This allows trainees to acquire international response skills.

[0063] The persona generation unit incorporates voice and facial expression data into the generation of the persona, enabling interactive simulations through sight and hearing. For example, the generation AI in the persona generation unit generates voice data for the persona, and asks questions or makes complaints by voice during the simulation. For example, the persona requests an explanation of a product by voice. The generation AI also generates facial expression data for the persona, and expresses emotions through facial expressions during the simulation. For example, the persona frowns when dissatisfied. Furthermore, by incorporating voice and facial expression data, the generation AI enables trainees to participate in interactive simulations through sight and hearing. For example, it responds appropriately based on the persona's facial expressions and voice. This enables trainees to participate in interactive simulations through sight and hearing.

[0064] The persona generation unit can use the emotion estimation function to automatically generate scenarios according to the emotional changes of the persona. For example, the generation AI in the persona generation unit uses the emotion estimation function to automatically generate scenarios according to the emotional changes of the persona. For example, when the persona feels anger, the content of the complaint is changed. The generation AI also uses the emotion estimation function to generate scenarios based on the emotional changes of the persona. For example, when the persona expresses gratitude, the scenario moves in a positive direction. The generation AI also uses the emotion estimation function to automatically generate scenarios according to the emotional changes of the persona, allowing trainees to learn how to respond based on emotions. For example, it considers appropriate responses based on the persona's emotions. This allows trainees to learn how to respond based on emotions.

[0065] The simulation implementation unit can evaluate the trainee's responses in real time during the simulation and provide instant feedback. For example, the generation AI in the simulation unit evaluates the trainee's responses in real time during the simulation and provides instant feedback. For example, if the trainee responds appropriately, it will display "Good response." The generation AI also evaluates the trainee's responses during the simulation and points out areas for improvement in real time. For example, it provides advice such as "It would be good to improve this part." The generation AI also evaluates the trainee's responses in real time during the simulation and provides instant feedback, allowing the trainee to learn areas for improvement on the spot. For example, it may instruct the trainee to "explain more carefully" in the middle of a response. This allows the trainee to learn areas for improvement on the spot.

[0066] The simulation implementation unit can set up situations in which multiple personas appear simultaneously in a simulation scenario. For example, the simulation implementation unit has the generation AI make multiple personas appear simultaneously in a simulation scenario. For example, it sets up a scenario in which multiple customers ask questions or make complaints at the same time. The generation AI also has multiple personas appear in a simulation scenario to help trainees develop simultaneous response skills. For example, they can respond to different questions or complaints at the same time. The generation AI also has multiple personas appear simultaneously in a simulation scenario, allowing trainees to develop complex response skills. For example, they can respond to a situation in which multiple customers have different problems. This allows trainees to develop complex response skills.

[0067] The simulation implementation unit can change the emotional state of the persona during the simulation. In the simulation implementation unit, for example, the generation AI changes the emotional state of the persona during the simulation. For example, if the persona feels dissatisfied with a question, it expresses that emotion. Furthermore, by the generation AI changing the emotional state of the persona during the simulation, the trainee can learn how to respond according to emotions. For example, if the persona feels anger, it considers an appropriate response. Furthermore, by the generation AI changing the emotional state of the persona during the simulation, the trainee can learn how to respond based on emotions. For example, if the persona expresses gratitude, the trainee will give an appropriate response. This allows the trainee to learn how to respond based on emotions.

[0068] The simulation implementation unit can add emergency situations or unexpected problems to the simulation scenario. In the simulation implementation unit, for example, the generation AI adds emergency situations to the simulation scenario. For example, it simulates how to respond if a customer suddenly falls ill. The generation AI also adds unexpected problems to the simulation scenario. For example, it simulates how to respond if a system failure occurs. The generation AI also adds emergency situations and unexpected problems to the simulation scenario, allowing trainees to develop the skills to respond quickly and appropriately. For example, responding to a sudden complaint from a customer. This allows trainees to develop the skills to respond quickly and appropriately.

[0069] The simulation implementation unit can share the results of the simulation with other trainees and develop it into a group work format where they can jointly come up with solutions to problems. For example, the simulation implementation unit provides a function where the generation AI can share the results of the simulation with other trainees. For example, they can share the simulation log and jointly come up with solutions to problems. The generation AI can also provide a scenario where they can use the results of the simulation to come up with solutions to problems in a group work format. For example, multiple trainees can work together to come up with ways to deal with complaints. The generation AI can also share the results of the simulation with other trainees and develop it into a group work format where they can jointly come up with solutions to problems, allowing trainees to develop the skills to solve problems collaboratively. For example, they can hold discussions based on the results of the simulation. This allows trainees to develop the skills to solve problems collaboratively.

[0070] The simulation implementation unit can use the emotion estimation function to display the emotional changes of the persona during the simulation in real time. In the simulation implementation unit, for example, the generation AI uses the emotion estimation function to display the emotional changes of the persona during the simulation in real time. For example, the emotional state is displayed based on the persona's facial expressions and voice. The generation AI also uses the emotion estimation function to display the emotional changes of the persona during the simulation in real time, allowing trainees to learn how to respond based on emotions. For example, it considers appropriate countermeasures according to the persona's emotional state. The generation AI also uses the emotion estimation function to display the emotional changes of the persona during the simulation in real time, allowing trainees to learn how to respond based on emotions. For example, when the persona feels anger, the emotion is displayed and the trainee responds appropriately. This allows trainees to learn how to respond based on emotions.

[0071] The feedback providing unit can use an emotion estimation function to analyze the emotional state of the trainee and provide advice according to the emotion. For example, the generation AI analyzes the emotional state of the trainee at the time of feedback and provides advice according to the emotion. For example, if the trainee is feeling anxious, the feedback providing unit provides reassuring advice. The generation AI also uses the emotion estimation function to analyze the emotional state of the trainee in real time at the time of feedback and provides appropriate advice. For example, it provides words of encouragement that will help the trainee gain confidence. The generation AI also analyzes the emotional state of the trainee at the time of feedback and provides advice according to the emotion, allowing the trainee to learn more effectively. For example, if the trainee is feeling depressed, it provides positive feedback. This allows the trainee to learn more effectively.

[0072] The feedback providing unit can compare the feedback content with past trainee data to clarify individual growth points. In the feedback providing unit, for example, the generation AI compares the feedback content with past trainee data to clarify individual growth points. For example, it evaluates the trainee's progress based on past data. The generation AI also analyzes past trainee data and reflects this in the feedback content. For example, it compares it with how other trainees responded to the same scenario and points out areas for improvement. The generation AI also compares the feedback content with past trainee data to clarify individual growth points, allowing the trainee to feel their own growth. For example, it compares it with past data and specifically shows which areas have improved. This allows the trainee to feel their own growth.

[0073] The feedback providing unit can include detailed specific improvement measures and points to try in the next simulation in the feedback. For example, the generation AI may include specific improvement measures in the feedback. For example, it may provide specific advice such as, "Try to explain things more carefully next time." The generation AI may also include detailed points to try in the next simulation in the feedback. For example, it may set a specific goal such as, "Aim to respond more quickly to customer questions next time." The generation AI may also include detailed specific improvement measures and points to try in the next simulation in the feedback, allowing the trainee to create a specific action plan. For example, it may provide specific instructions such as, "Next time, pay more attention to the customer's emotions and respond appropriately." This allows the trainee to create a specific action plan.

[0074] The feedback providing unit can provide feedback in audio or video format, making it easier for trainees to understand through their eyes and ears. In the feedback providing unit, for example, the generation AI provides feedback in audio format. For example, it may give advice in audio format such as "It would be good to improve this part." The generation AI may also provide feedback in video format. For example, it may give advice while showing specific improvement measures in a video. The generation AI may also provide feedback in audio or video format, making it easier for trainees to understand through their eyes and ears. For example, it may give feedback while showing specific areas for improvement in an audio or video format. This makes it easier for trainees to understand through their eyes and ears.

[0075] The feedback providing unit can share the feedback content with other trainees and develop it into a discussion format where they can jointly think up improvement measures. The feedback providing unit, for example, provides a function where the generation AI can share the feedback content with other trainees. For example, they can share the feedback content and hold a discussion where they can jointly think up improvement measures. The generation AI can also provide a scenario where improvement measures can be thought up in a discussion format based on the feedback content. For example, multiple trainees can work together to think up improvement measures. The generation AI can also share the feedback content with other trainees and develop it into a discussion format where they can jointly think up improvement measures, allowing trainees to develop the skills to solve problems collaboratively. For example, they can hold a discussion based on the feedback content. This allows trainees to develop the skills to solve problems collaboratively.

[0076] The feedback providing unit can use an emotion estimation function to analyze the emotional state of the trainee and suggest an approach that will elicit positive emotions. For example, the generation AI in the feedback providing unit analyzes the emotional state of the trainee at the time of feedback and suggests an approach that will elicit positive emotions. For example, it provides encouraging words that will help the trainee gain confidence. The generation AI also uses the emotion estimation function to analyze the emotional state of the trainee in real time at the time of feedback and suggests an appropriate approach. For example, if the trainee is feeling depressed, it provides positive feedback. The generation AI also analyzes the emotional state of the trainee at the time of feedback and suggests an approach that will elicit positive emotions, allowing the trainee to learn more effectively. For example, if the trainee is feeling anxious, it provides reassuring advice. This allows the trainee to learn more effectively.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The system can be equipped with a stress estimation function to help trainees improve their stress management skills in the real world. For example, it can monitor the stress level felt by trainees during a simulation in real time and suggest relaxation methods at appropriate times. The stress estimation function can also be used to adjust the difficulty of the simulation if the trainee is in a high-stress state. Furthermore, when trainees feel stressed, advice on stress reduction can be provided, thereby helping trainees improve their stress management skills.

[0079] The system can be equipped with a cultural understanding estimation function to help trainees improve their skills in dealing with customers from different cultural backgrounds. For example, the system can provide advice on how trainees can respond appropriately to personas from different cultural backgrounds. The cultural understanding estimation function can also be used to evaluate trainees' intercultural understanding and provide additional learning resources as needed. Furthermore, trainees can improve their international dealing skills by conducting intercultural interaction simulations.

[0080] The system can be equipped with a purchasing intent estimation function to help trainees improve their skills in increasing customers' purchasing intent. For example, trainees can estimate a customer's purchasing intent during a simulation and propose a sales approach at the appropriate time. The purchasing intent estimation function can also be used to provide trainees with specific advice to increase customers' purchasing intent. Furthermore, by having trainees perform simulations to increase customers' purchasing intent, they can improve their sales skills.

[0081] The system can be equipped with a satisfaction estimation function to help trainees improve their skills in improving customer satisfaction. For example, the system can estimate customer satisfaction during a simulation and suggest follow-up actions at the appropriate time. The satisfaction estimation function can also be used to provide specific advice to trainees to improve customer satisfaction. Furthermore, trainees can improve their customer service skills by performing a simulation to improve customer satisfaction.

[0082] The system can be equipped with a trust estimation function to help trainees improve their skills in building customer trust. For example, the system can estimate a customer's trust during a simulation and suggest approaches to build trust at the appropriate time. The trust estimation function can also be used to provide trainees with specific advice on how to build customer trust. Furthermore, by having trainees conduct simulations to build customer trust, they can improve their trust-building skills.

[0083] The system can be equipped with a needs estimation function to help trainees improve their skills in accurately grasping customer needs. For example, trainees can estimate customer needs during a simulation and make proposals that meet those needs at the appropriate time. The needs estimation function can also be used to provide specific advice to trainees to help them accurately grasp customer needs. Furthermore, by conducting a simulation to help trainees accurately grasp customer needs, trainees can improve their needs analysis skills.

[0084] The system can be equipped with a lifestyle estimation function to improve the trainee's skills in making proposals tailored to the customer's lifestyle. For example, the trainee can estimate the customer's lifestyle during a simulation and make proposals tailored to the lifestyle at the appropriate time. The lifestyle estimation function can also be used to provide specific advice to the trainee to help them make proposals tailored to the customer's lifestyle. Furthermore, by conducting a simulation in which the trainee makes proposals tailored to the customer's lifestyle, the trainee's proposal skills can be improved.

[0085] The system can be equipped with a purchase history estimation function to improve trainees' skills in making proposals based on customer purchase history. For example, trainees can estimate a customer's purchase history during a simulation and make proposals based on the purchase history at the appropriate time. The purchase history estimation function can also be used to provide trainees with specific advice to help them make proposals based on the customer's purchase history. Furthermore, by conducting a simulation in which trainees make proposals based on the customer's purchase history, trainees can improve their proposal skills.

[0086] The system can be equipped with a feedback estimation function to improve trainees' skills in making improvement proposals based on customer feedback. For example, trainees can estimate customer feedback during a simulation and make improvement proposals based on the feedback at the appropriate time. The feedback estimation function can also be used to provide specific advice to trainees to help them make improvement proposals based on customer feedback. Furthermore, by conducting a simulation in which trainees make improvement proposals based on customer feedback, trainees can improve their improvement proposal skills.

[0087] The system can be equipped with a behavioral pattern estimation function to improve trainees' skills in making predictions based on customer behavioral patterns. For example, trainees can estimate customer behavioral patterns during a simulation and make predictions based on the behavioral patterns at appropriate times. The behavioral pattern estimation function can also be used to provide trainees with specific advice to help them make predictions based on customer behavioral patterns. Furthermore, trainees can improve their prediction skills by running a simulation to make predictions based on customer behavioral patterns.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The persona generation unit generates a persona using a generation AI. For example, the generation AI uses technologies such as GPT-3 or BERT to generate a persona with attributes such as age, gender, occupation, hobbies, and personality. Step 2: The simulation implementation department conducts a simulation based on the persona generated by the persona generation department. For example, if a persona asks, "I don't know how to use the product," a simulation is conducted in which a trainee provides appropriate guidance. Step 3: The feedback department provides feedback based on the results of the simulation conducted by the simulation implementation department. For example, if the trainee's response was appropriate, they will be evaluated as "good response," and if improvement is needed, they will be given advice such as "this part should be improved."

[0090] 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.

[0091] 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.

[0092] 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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."

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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]

[0157] 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 persona generation unit that generates a persona using a generation AI; a simulation execution unit that performs a simulation based on the persona generated by the persona generation unit; a feedback providing unit that provides feedback based on the results of the simulation performed by the simulation performing unit. A system characterized by:

2. The persona generation unit Generate personas with attributes such as age, gender, occupation, hobbies, and personality 2. The system of claim 1.

3. The simulation execution unit A simulation is conducted in which the trainee provides appropriate guidance when the persona asks how to use the product.

2. The system of claim 1.

4. The feedback providing unit: Evaluate trainees' responses and provide feedback 2. The system of claim 1.

5. The persona generation unit Incorporating emotion estimation functionality to change the persona's emotional state in real time 2. The system of claim 1.

6. The persona generation unit Analyze past customer data and generate personas that reflect actual customer behavior patterns 2. The system of claim 1.

7. The persona generation unit Add detailed health and lifestyle information to the persona attributes 2. The system of claim 1.

8. The persona generation unit Generate personas with different cultural backgrounds or languages 2. The system of claim 1.

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