system

Abstract

The system according to this embodiment aims to provide psychological preparation for dealing with complainers and troubles in the service and hospitality industries. [Solution] The system according to the embodiment comprises a posting unit, an analysis unit, a generation unit, and a provision unit. The posting unit posts cases of complainers and troubles. The analysis unit analyzes the cases posted by the posting unit. The generation unit generates simulations based on the cases analyzed by the analysis unit. The provision unit provides the simulations generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the psychological preparation for customers and troubles in the service industry and customer service industry is not sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to provide psychological preparation for customers and troubles in the service industry and customer service industry.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a posting unit, an analysis unit, a generation unit, and a provision unit. The posting unit posts cases of complaints and troubles. The analysis unit analyzes the cases posted by the posting unit. The generation unit generates simulations based on the cases analyzed by the analysis unit. The provision unit provides the simulations generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide psychological preparation for dealing with complainers and troubles in the service and hospitality industries. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) 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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The simulation system according to an embodiment of the present invention is a system for mitigating psychological damage in the service and hospitality industries. This system allows service-using establishments to post examples of complainers and troubles they have encountered, and a generating AI generates a simulation. The simulation uses VR or video games to recreate the trouble case with a fictional, aggressive avatar in a setting that matches the user's store information. This is expected to not only allow people working in the service and hospitality industries to prepare for psychological damage, but also to act as a deterrent against customers. For example, service-using establishments post examples of complainers and troubles they have encountered. In this case, the establishment describes the specific content and circumstances of the trouble in detail. For example, they might post the specific words and actions of a complainer in a restaurant, as well as the time and location where the trouble occurred. This information is stored in a database that serves as the basis for the simulation. Next, the generating AI generates a simulation based on the posted examples. The generating AI analyzes the posted text data and generates a fictional, aggressive avatar. This avatar recreates a specific trouble case based on the posted examples. For example, an avatar is generated that recreates the words and actions of a complainer in a restaurant. This avatar uses VR and video games to recreate trouble scenarios tailored to the user's store information. Simulation users experience videos through VR or video games in which a fictional, aggressive avatar recreates trouble scenarios. This allows people in the service and hospitality industries to prepare for actual troubles. For example, restaurant employees can learn how to appropriately respond to the behavior of difficult customers. Furthermore, it is believed that the popularity of simulations will also act as a deterrent to customers. It is expected that the occurrence of troubles will decrease as more customers fear being used as a difficult customer scenario, even anonymously. This system not only allows people in the service and hospitality industries to prepare for psychological damage, but also enables stores to cooperate in providing services without psychological burden. For example, if stores of various types, such as restaurants, high-end stores, and brand stores, use the simulation, the trouble-handling capabilities of the entire industry will improve.Furthermore, the simulation customization function allows for training tailored to the specific characteristics of each store. For example, simulations are provided to handle various situations, such as troubleshooting at customer service centers or training for foreign workers. As a result, the simulation system can reduce psychological damage in the service and customer-facing industries and provide a deterrent against undesirable behavior from customers.

[0029] The simulation system according to this embodiment comprises a posting unit, an analysis unit, a generation unit, and a provision unit. The posting unit posts examples of complainers and troubles. For example, the posting unit can post examples of complainers and troubles encountered by service-using stores. The posting unit can describe the specific content and circumstances of the trouble in detail. For example, it can post the specific words and actions of a complainer at a restaurant, the time and place when the trouble occurred, etc. The analysis unit analyzes the examples posted by the posting unit. For example, the analysis unit can analyze the posted text data. The analysis unit can analyze the posted examples using methods such as text analysis and sentiment analysis. The generation unit generates a simulation based on the examples analyzed by the analysis unit. The generation unit can generate a fictional, domineering avatar using a generation AI. For example, the generation AI can analyze the posted text data and generate a fictional, domineering avatar. The generation AI can generate a fictional, domineering avatar using technologies such as deep learning and generative opposite-agent networks (GANs). The provision unit provides the simulation generated by the generation unit. The service provider can, for example, provide simulations using VR or video games. The service provider can provide simulations in a setting that matches the user's store information. For example, based on the user's store information, the service provider can provide a simulation in which a fictional, overbearing avatar recreates a trouble scenario. As a result, the simulation system according to this embodiment can reduce psychological damage in the service and hospitality industries and provide a deterrent to customers.

[0030] The posting section allows users to submit cases of customer complaints and troubles. For example, businesses using the service can submit cases of customer complaints and troubles they have encountered. Specifically, restaurants, retail stores, and service businesses can provide detailed descriptions of the specific words and actions of customers encountered during their daily operations, as well as the time, location, and circumstances of the trouble. For instance, a restaurant could submit a report detailing the language used by a customer to an employee in an aggressive manner, the specific time and location of the incident, the store's congestion level at the time, and the employee's response. The posting section provides an interface for entering this information in text format, making it easy for users to submit detailed information. The posting section can also accept multimedia data such as images and videos, enabling the submission of more specific and realistic cases. Furthermore, the posting section centrally manages submitted data and can link with other departments and systems as needed. For example, submitted data can be stored on a cloud server and made accessible to the analysis and generation departments. The posting section can also provide appropriate guidelines and templates to contributors to improve the accuracy and reliability of submitted content. This allows the posting department to efficiently and effectively collect cases of complaints and problems, thereby improving the overall performance of the system.

[0031] The analysis unit analyzes the cases posted by the posting unit. For example, the analysis unit can analyze the posted text data. Specifically, it uses methods such as text analysis and sentiment analysis to analyze the posted cases in detail. In text analysis, natural language processing (NLP) technology is used to extract important keywords and phrases from the posted text data to understand the content of the trouble and the characteristics of the complainer. In sentiment analysis, the tone and intensity of emotions are analyzed from the posted text data to evaluate the complainer's emotional state and the severity of the trouble. For example, the analysis unit can analyze the posted text data to determine how aggressive the complainer's behavior is, and identify the cause and background of the trouble. Furthermore, the analysis unit can also use past posting data and statistical information to analyze the patterns and trends of trouble occurrences. For example, it can analyze whether troubles tend to occur frequently at specific times or days of the week, or whether there is a tendency for complainers to occur frequently at specific stores or in specific areas, and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect patterns that are different from the norm or abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The generation unit generates simulations based on cases analyzed by the analysis unit. The generation unit can generate fictional, aggressive avatars using a generative AI. Specifically, the generative AI analyzes posted text data and generates fictional, aggressive avatars. The generative AI can generate realistic avatars using technologies such as deep learning and generative opposite networks (GANs). For example, the generative AI analyzes the behavior and emotional state of posted complainers and generates the behavior and facial expressions of an aggressive avatar based on that analysis. The generated avatars behave like actual complainers, allowing users to experience realistic simulations. Furthermore, the generation unit can simulate various scenarios using the generated avatars. For example, it can generate simulations tailored to different times of day, locations, and situations, enabling users to handle diverse trouble scenarios. The generation unit can also continuously improve the accuracy and realism of the simulations based on user feedback. This allows the generation unit to provide realistic and effective simulations, improving users' ability to handle problems.

[0033] The service provider provides simulations generated by the generation unit. The service provider can, for example, provide simulations using VR or video games. Specifically, it can use VR headsets and dedicated simulation software to allow users to experience realistic trouble scenarios. The service provider can provide simulations tailored to the user's store information. For example, it can provide a simulation in which a fictional, assertive avatar recreates trouble cases, taking into account the user's store layout, equipment, and employee placement. This allows users to experience trouble scenarios that could actually occur in their own stores and learn specific response methods. Furthermore, the service provider can collect simulation results and user feedback to continuously improve the overall system performance. For example, it can record user behavior and reactions during simulations and collaborate with the analysis and generation units to provide more effective simulations. The service provider can also enable team training and cooperative play by allowing multiple users to experience simulations simultaneously. This allows the service provider to provide users with realistic and effective simulations, improving their trouble-shooting capabilities.

[0034] The generation unit can generate fictional, domineering avatars using a generative AI. For example, the generation unit can analyze submitted text data and generate fictional, domineering avatars. The generative AI can generate fictional, domineering avatars using technologies such as deep learning and generative opposite networks (GANs). For example, the generative AI can use deep learning to extract features of a domineering avatar from submitted text data and generate an avatar based on those features. Furthermore, the generative AI can use a generative opposite network (GAN) to generate a domineering avatar from submitted text data. This allows for the generation of fictional, domineering avatars, thereby providing a realistic simulation.

[0035] The service provider can offer simulations using VR or video games. For example, the service provider can offer simulations using VR technology. The service provider can allow users to wear a VR headset and experience a simulation in which a fictional, domineering avatar recreates a trouble scenario. The service provider can also offer simulations using video games. For example, the service provider can offer simulations in the form of video games, allowing users to operate a game controller and experience a simulation in which a fictional, domineering avatar recreates a trouble scenario. In this way, by using VR or video games, the service provider can provide users with an immersive simulation experience.

[0036] The submission section allows for detailed descriptions of specific troubles and their circumstances. For example, it can provide detailed descriptions of customer complaints and troubles encountered by service-using businesses. By providing detailed descriptions of specific troubles and their circumstances, the submission section can improve the accuracy of simulations. For example, the submission section can provide detailed descriptions of the specific words and actions of a customer complaining at a restaurant, as well as the time and location where the trouble occurred. This improves the accuracy of simulations by providing detailed descriptions of specific troubles and their circumstances.

[0037] The analysis unit can analyze the submitted text data. For example, the analysis unit can analyze the submitted text data and extract the information necessary for generating a simulation. The analysis unit can analyze the submitted text data using methods such as text analysis and sentiment analysis. For example, the analysis unit can use text analysis to extract the words and actions of complainers and the circumstances of the trouble from the submitted text data. The analysis unit can also use sentiment analysis to analyze the emotions and attitudes of complainers from the submitted text data. In this way, by analyzing the submitted text data, it is possible to extract the information necessary for generating a simulation.

[0038] The service provider can provide simulations tailored to the user's store information. For example, based on the user's store information, the service provider can provide a simulation in which a fictional, overbearing avatar recreates a trouble scenario. The service provider can customize the content of the simulation based on the user's store information. For example, the service provider can adjust the simulation scenario based on the layout and services offered by the user's store. The service provider can also adjust the characteristics of the simulation avatar based on the customer base of the user's store. This allows the service provider to prepare for actual troubles by providing simulations tailored to the user's store information.

[0039] The posting function can automatically suggest similar trouble cases by referring to the user's past posting history when a post is made. For example, the posting function can automatically display similar cases based on trouble cases that the user has previously posted. The posting function can also prioritize suggesting cases that contain keywords that match the content the user has previously posted. Furthermore, the posting function can suggest trouble cases related to specific times or locations from the user's past posting history. This allows users to efficiently post similar trouble cases by referring to their past posting history.

[0040] The submission system can prioritize processing submitted content based on the frequency and impact of the problem. For example, it can prioritize cases with a high frequency of occurrence and quickly generate simulations. It can also prioritize cases with a high impact and generate detailed simulations. Furthermore, it can comprehensively evaluate the frequency and impact of each problem and prioritize processing the most important cases. This allows for a rapid response to critical cases by prioritizing the processing of submitted content based on the frequency and impact of each problem.

[0041] The posting function can prioritize posting highly relevant cases by considering the user's geographical location when posting. For example, it can prioritize displaying trouble cases that occurred near the user's current location. Furthermore, the posting function can suggest region-specific trouble cases based on the user's geographical location. In addition, the posting function can prioritize posting the most relevant cases by considering the user's location. This allows for the efficient posting of region-specific trouble cases by considering the user's geographical location.

[0042] The posting function can analyze a user's social media activity and automatically post relevant trouble cases when a post is made. For example, it can analyze a user's social media posts and suggest relevant trouble cases. It can also automatically post trouble cases that users have shared on social media. Furthermore, based on the user's social media activity, the posting function can prioritize posting the most relevant trouble cases. This allows for the efficient posting of relevant trouble cases by analyzing the user's social media activity.

[0043] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. Furthermore, the analysis unit can adjust the analysis algorithm by referring to past analysis results. In addition, the analysis unit can improve the accuracy of the analysis algorithm by analyzing past analysis data. Thus, the accuracy of the analysis algorithm can be improved by referring to past analysis data.

[0044] The analysis unit can apply different analysis methods depending on the category of the trouble during the analysis. For example, the analysis unit can apply natural language processing to the analysis of the behavior of complainants. Furthermore, the analysis unit can apply geographic information systems to the analysis of the location where the trouble occurred. In addition, the analysis unit can apply statistical analysis to the analysis of the impact of the trouble. By applying analysis methods appropriate to the category of the trouble, the accuracy of the analysis can be improved.

[0045] The analysis unit can determine the priority of analysis based on the submission date of the submitted cases. For example, the analysis unit can prioritize the analysis of recently submitted cases. It can also prioritize the analysis of cases submitted within a specific time period. Furthermore, the analysis unit can prioritize the analysis of the most important cases, taking into account the submission date of the submitted cases. This allows for the rapid analysis of the latest cases by determining the priority of analysis based on the submission date of the submitted cases.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and databases during the analysis process. For example, it can improve the accuracy of its analysis by referring to relevant academic papers. It can also improve the accuracy of its analysis by referring to relevant databases. Furthermore, it can improve the accuracy of its analysis by referring to relevant industry reports. In this way, the accuracy of the analysis can be improved by referring to relevant literature and databases.

[0047] The generation unit can optimize its generation algorithm by referring to past generation data during generation. For example, the generation unit can select the optimal generation algorithm based on past generation data. Furthermore, the generation unit can adjust the generation algorithm by referring to past generation results. In addition, the generation unit can analyze past generation data to improve the accuracy of the generation algorithm. Thus, by referring to past generation data, the accuracy of the generation algorithm can be improved.

[0048] The generation unit can apply different generation methods depending on the category of the trouble during generation. For example, the generation unit can apply natural language processing to simulations concerning the behavior of complainers. It can also apply geographic information systems to simulations concerning the location where the trouble occurred. Furthermore, it can apply statistical analysis to simulations concerning the impact of the trouble. By applying generation methods appropriate to the category of the trouble, the accuracy of the simulation can be improved.

[0049] The generation unit can prioritize generating highly relevant simulations by considering the user's geographical location information during the generation process. For example, the generation unit can generate simulations based on trouble cases that occurred near the user's current location. Furthermore, the generation unit can incorporate region-specific trouble cases into the simulations based on the user's geographical location information. In addition, the generation unit can prioritize generating the most relevant simulations by considering the user's location information. This allows for efficient simulation of region-specific trouble cases by considering the user's geographical location information.

[0050] The generation unit can improve the accuracy of its generation by referring to relevant literature and databases during the generation process. For example, it can improve the accuracy of its generation by referring to relevant academic papers. It can also improve the accuracy of its generation by referring to relevant databases. Furthermore, it can improve the accuracy of its generation by referring to relevant industry reports. In this way, the accuracy of the generation can be improved by referring to relevant literature and databases.

[0051] The service provider can optimize its service provision algorithm by referring to past service provision data during the service provision process. For example, the service provider can select the optimal service provision algorithm based on past service provision data. Furthermore, the service provider can adjust its service provision algorithm by referring to past service provision results. In addition, the service provider can analyze past service provision data to improve the accuracy of its service provision algorithm. Thus, by referring to past service provision data, the accuracy of the service provision algorithm can be improved.

[0052] The service provider can customize the simulation based on the user's store information at the time of delivery. For example, the service provider can customize the simulation based on the layout of the user's store. Furthermore, the service provider can customize the simulation based on the services offered at the user's store. In addition, the service provider can customize the simulation based on the customer base of the user's store. This allows for more practical training by customizing the simulation based on the user's store information.

[0053] The service provider can select the optimal delivery method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can provide a delivery method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a delivery method optimized for a larger screen. In addition, if the user is using a VR device, the service provider can provide a delivery method optimized for VR. This allows the service provider to select the optimal delivery method by considering the user's device information.

[0054] The service provider can improve the accuracy of its service by referring to relevant literature and databases during the service provision process. For example, it can improve the accuracy of its service by referring to relevant academic papers. It can also improve the accuracy of its service by referring to relevant databases. Furthermore, it can improve the accuracy of its service by referring to relevant industry reports. In this way, the accuracy of the service can be improved by referring to relevant literature and databases.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The simulation system may also include a data analysis unit. This unit can analyze data collected during the simulation and extract trends and patterns. For example, it can compare multiple simulation results to identify common problems and areas for improvement. Furthermore, it can optimize the simulation based on the analysis results. This allows the data analysis unit to provide valuable insights for improving the effectiveness of the simulation.

[0057] The simulation system can also include a customization section. This customization section can customize the simulation content according to the user's needs and requests. For example, the customization section can provide simulation scenarios tailored to specific industries or occupations. Furthermore, the customization section can suggest optimal simulation content based on the user's past simulation history. This allows the customization section to provide the most effective simulation for the user.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The posting section allows users to submit examples of customer complaints and troubles. For example, businesses using the service can submit examples of customer complaints and troubles they have encountered. Users can describe the specific details of the trouble and its circumstances, including the specific words and actions of the customer at the restaurant, as well as the time and location where the trouble occurred. Step 2: The analysis unit analyzes the cases submitted by the posting unit. For example, it can analyze the submitted text data and use methods such as text analysis and sentiment analysis to analyze the submitted cases. Step 3: The generation unit generates a simulation based on the cases analyzed by the analysis unit. Using the generation AI, a fictional, domineering avatar can be generated. The generation AI can analyze the posted text data using technologies such as deep learning and generative opposite networks (GANs) to generate a fictional, domineering avatar. Step 4: The provider unit provides the simulation generated by the generator unit. For example, the simulation can be provided using VR or video games. The provider unit can provide a simulation in which a fictional, overbearing avatar recreates a trouble scenario based on the user's store information.

[0060] (Example of form 2) The simulation system according to an embodiment of the present invention is a system for mitigating psychological damage in the service and hospitality industries. This system allows service-using establishments to post examples of complainers and troubles they have encountered, and a generating AI generates a simulation. The simulation uses VR or video games to recreate the trouble case with a fictional, aggressive avatar in a setting that matches the user's store information. This is expected to not only allow people working in the service and hospitality industries to prepare for psychological damage, but also to act as a deterrent against customers. For example, service-using establishments post examples of complainers and troubles they have encountered. In this case, the establishment describes the specific content and circumstances of the trouble in detail. For example, they might post the specific words and actions of a complainer in a restaurant, as well as the time and location where the trouble occurred. This information is stored in a database that serves as the basis for the simulation. Next, the generating AI generates a simulation based on the posted examples. The generating AI analyzes the posted text data and generates a fictional, aggressive avatar. This avatar recreates a specific trouble case based on the posted examples. For example, an avatar is generated that recreates the words and actions of a complainer in a restaurant. This avatar uses VR and video games to recreate trouble scenarios tailored to the user's store information. Simulation users experience videos through VR or video games in which a fictional, aggressive avatar recreates trouble scenarios. This allows people in the service and hospitality industries to prepare for actual troubles. For example, restaurant employees can learn how to appropriately respond to the behavior of difficult customers. Furthermore, it is believed that the popularity of simulations will also act as a deterrent to customers. It is expected that the occurrence of troubles will decrease as more customers fear being used as a difficult customer scenario, even anonymously. This system not only allows people in the service and hospitality industries to prepare for psychological damage, but also enables stores to cooperate in providing services without psychological burden. For example, if stores of various types, such as restaurants, high-end stores, and brand stores, use the simulation, the trouble-handling capabilities of the entire industry will improve.Furthermore, the simulation customization function allows for training tailored to the specific characteristics of each store. For example, simulations are provided to handle various situations, such as troubleshooting at customer service centers or training for foreign workers. As a result, the simulation system can reduce psychological damage in the service and customer-facing industries and provide a deterrent against undesirable behavior from customers.

[0061] The simulation system according to this embodiment comprises a posting unit, an analysis unit, a generation unit, and a provision unit. The posting unit posts examples of complainers and troubles. For example, the posting unit can post examples of complainers and troubles encountered by service-using stores. The posting unit can describe the specific content and circumstances of the trouble in detail. For example, it can post the specific words and actions of a complainer at a restaurant, the time and place when the trouble occurred, etc. The analysis unit analyzes the examples posted by the posting unit. For example, the analysis unit can analyze the posted text data. The analysis unit can analyze the posted examples using methods such as text analysis and sentiment analysis. The generation unit generates a simulation based on the examples analyzed by the analysis unit. The generation unit can generate a fictional, domineering avatar using a generation AI. For example, the generation AI can analyze the posted text data and generate a fictional, domineering avatar. The generation AI can generate a fictional, domineering avatar using technologies such as deep learning and generative opposite-agent networks (GANs). The provision unit provides the simulation generated by the generation unit. The service provider can, for example, provide simulations using VR or video games. The service provider can provide simulations in a setting that matches the user's store information. For example, based on the user's store information, the service provider can provide a simulation in which a fictional, overbearing avatar recreates a trouble scenario. As a result, the simulation system according to this embodiment can reduce psychological damage in the service and hospitality industries and provide a deterrent to customers.

[0062] The posting section allows users to submit cases of customer complaints and troubles. For example, businesses using the service can submit cases of customer complaints and troubles they have encountered. Specifically, restaurants, retail stores, and service businesses can provide detailed descriptions of the specific words and actions of customers encountered during their daily operations, as well as the time, location, and circumstances of the trouble. For instance, a restaurant could submit a report detailing the language used by a customer to an employee in an aggressive manner, the specific time and location of the incident, the store's congestion level at the time, and the employee's response. The posting section provides an interface for entering this information in text format, making it easy for users to submit detailed information. The posting section can also accept multimedia data such as images and videos, enabling the submission of more specific and realistic cases. Furthermore, the posting section centrally manages submitted data and can link with other departments and systems as needed. For example, submitted data can be stored on a cloud server and made accessible to the analysis and generation departments. The posting section can also provide appropriate guidelines and templates to contributors to improve the accuracy and reliability of submitted content. This allows the posting department to efficiently and effectively collect cases of complaints and problems, thereby improving the overall performance of the system.

[0063] The analysis unit analyzes the cases posted by the posting unit. For example, the analysis unit can analyze the posted text data. Specifically, it uses methods such as text analysis and sentiment analysis to analyze the posted cases in detail. In text analysis, natural language processing (NLP) technology is used to extract important keywords and phrases from the posted text data to understand the content of the trouble and the characteristics of the complainer. In sentiment analysis, the tone and intensity of emotions are analyzed from the posted text data to evaluate the complainer's emotional state and the severity of the trouble. For example, the analysis unit can analyze the posted text data to determine how aggressive the complainer's behavior is, and identify the cause and background of the trouble. Furthermore, the analysis unit can also use past posting data and statistical information to analyze the patterns and trends of trouble occurrences. For example, it can analyze whether troubles tend to occur frequently at specific times or days of the week, or whether there is a tendency for complainers to occur frequently at specific stores or in specific areas, and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect patterns that are different from the norm or abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0064] The generation unit generates simulations based on cases analyzed by the analysis unit. The generation unit can generate fictional, aggressive avatars using a generative AI. Specifically, the generative AI analyzes posted text data and generates fictional, aggressive avatars. The generative AI can generate realistic avatars using technologies such as deep learning and generative opposite networks (GANs). For example, the generative AI analyzes the behavior and emotional state of posted complainers and generates the behavior and facial expressions of an aggressive avatar based on that analysis. The generated avatars behave like actual complainers, allowing users to experience realistic simulations. Furthermore, the generation unit can simulate various scenarios using the generated avatars. For example, it can generate simulations tailored to different times of day, locations, and situations, enabling users to handle diverse trouble scenarios. The generation unit can also continuously improve the accuracy and realism of the simulations based on user feedback. This allows the generation unit to provide realistic and effective simulations, improving users' ability to handle problems.

[0065] The service provider provides simulations generated by the generation unit. The service provider can, for example, provide simulations using VR or video games. Specifically, it can use VR headsets and dedicated simulation software to allow users to experience realistic trouble scenarios. The service provider can provide simulations tailored to the user's store information. For example, it can provide a simulation in which a fictional, assertive avatar recreates trouble cases, taking into account the user's store layout, equipment, and employee placement. This allows users to experience trouble scenarios that could actually occur in their own stores and learn specific response methods. Furthermore, the service provider can collect simulation results and user feedback to continuously improve the overall system performance. For example, it can record user behavior and reactions during simulations and collaborate with the analysis and generation units to provide more effective simulations. The service provider can also enable team training and cooperative play by allowing multiple users to experience simulations simultaneously. This allows the service provider to provide users with realistic and effective simulations, improving their trouble-shooting capabilities.

[0066] The generation unit can generate fictional, domineering avatars using a generative AI. For example, the generation unit can analyze submitted text data and generate fictional, domineering avatars. The generative AI can generate fictional, domineering avatars using technologies such as deep learning and generative opposite networks (GANs). For example, the generative AI can use deep learning to extract features of a domineering avatar from submitted text data and generate an avatar based on those features. Furthermore, the generative AI can use a generative opposite network (GAN) to generate a domineering avatar from submitted text data. This allows for the generation of fictional, domineering avatars, thereby providing a realistic simulation.

[0067] The service provider can offer simulations using VR or video games. For example, the service provider can offer simulations using VR technology. The service provider can allow users to wear a VR headset and experience a simulation in which a fictional, domineering avatar recreates a trouble scenario. The service provider can also offer simulations using video games. For example, the service provider can offer simulations in the form of video games, allowing users to operate a game controller and experience a simulation in which a fictional, domineering avatar recreates a trouble scenario. In this way, by using VR or video games, the service provider can provide users with an immersive simulation experience.

[0068] The submission section allows for detailed descriptions of specific troubles and their circumstances. For example, it can provide detailed descriptions of customer complaints and troubles encountered by service-using businesses. By providing detailed descriptions of specific troubles and their circumstances, the submission section can improve the accuracy of simulations. For example, the submission section can provide detailed descriptions of the specific words and actions of a customer complaining at a restaurant, as well as the time and location where the trouble occurred. This improves the accuracy of simulations by providing detailed descriptions of specific troubles and their circumstances.

[0069] The analysis unit can analyze the submitted text data. For example, the analysis unit can analyze the submitted text data and extract the information necessary for generating a simulation. The analysis unit can analyze the submitted text data using methods such as text analysis and sentiment analysis. For example, the analysis unit can use text analysis to extract the words and actions of complainers and the circumstances of the trouble from the submitted text data. The analysis unit can also use sentiment analysis to analyze the emotions and attitudes of complainers from the submitted text data. In this way, by analyzing the submitted text data, it is possible to extract the information necessary for generating a simulation.

[0070] The service provider can provide simulations tailored to the user's store information. For example, based on the user's store information, the service provider can provide a simulation in which a fictional, overbearing avatar recreates a trouble scenario. The service provider can customize the content of the simulation based on the user's store information. For example, the service provider can adjust the simulation scenario based on the layout and services offered by the user's store. The service provider can also adjust the characteristics of the simulation avatar based on the customer base of the user's store. This allows the service provider to prepare for actual troubles by providing simulations tailored to the user's store information.

[0071] The posting function can estimate the user's emotions and adjust the level of detail in the post based on the estimated emotions. For example, if the user is stressed, the posting function can provide a simple input form and minimize the posting process. If the user is relaxed, the posting function can provide detailed input options and suggest a customizable posting method. Furthermore, if the user is in a hurry, the posting function can prioritize voice input to allow for quick posting of problem cases. This reduces the burden on the user by adjusting the level of detail in the post according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0072] The posting function can automatically suggest similar trouble cases by referring to the user's past posting history when a post is made. For example, the posting function can automatically display similar cases based on trouble cases that the user has previously posted. The posting function can also prioritize suggesting cases that contain keywords that match the content the user has previously posted. Furthermore, the posting function can suggest trouble cases related to specific times or locations from the user's past posting history. This allows users to efficiently post similar trouble cases by referring to their past posting history.

[0073] The submission system can prioritize processing submitted content based on the frequency and impact of the problem. For example, it can prioritize cases with a high frequency of occurrence and quickly generate simulations. It can also prioritize cases with a high impact and generate detailed simulations. Furthermore, it can comprehensively evaluate the frequency and impact of each problem and prioritize processing the most important cases. This allows for a rapid response to critical cases by prioritizing the processing of submitted content based on the frequency and impact of each problem.

[0074] The posting system can estimate the user's emotions and prioritize posts based on those emotions. For example, if a user is feeling highly anxious, the posting system can prioritize that post. Similarly, if a user is angry, the posting system can quickly process that post and take appropriate action. Furthermore, if the user is calm, the posting system can prioritize other urgent posts. This allows for the rapid processing of urgent posts by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The posting function can prioritize posting highly relevant cases by considering the user's geographical location when posting. For example, it can prioritize displaying trouble cases that occurred near the user's current location. Furthermore, the posting function can suggest region-specific trouble cases based on the user's geographical location. In addition, the posting function can prioritize posting the most relevant cases by considering the user's location. This allows for the efficient posting of region-specific trouble cases by considering the user's geographical location.

[0076] The posting function can analyze a user's social media activity and automatically post relevant trouble cases when a post is made. For example, it can analyze a user's social media posts and suggest relevant trouble cases. It can also automatically post trouble cases that users have shared on social media. Furthermore, based on the user's social media activity, the posting function can prioritize posting the most relevant trouble cases. This allows for the efficient posting of relevant trouble cases by analyzing the user's social media activity.

[0077] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can increase the accuracy of the analysis and provide detailed results. Conversely, if the user is relaxed, the analysis unit can adjust the accuracy of the analysis and provide concise results. Furthermore, if the user is in a hurry, the analysis unit can perform the analysis quickly and provide results. In this way, by adjusting the accuracy of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. Furthermore, the analysis unit can adjust the analysis algorithm by referring to past analysis results. In addition, the analysis unit can improve the accuracy of the analysis algorithm by analyzing past analysis data. Thus, the accuracy of the analysis algorithm can be improved by referring to past analysis data.

[0079] The analysis unit can apply different analysis methods depending on the category of the trouble during the analysis. For example, the analysis unit can apply natural language processing to the analysis of the behavior of complainants. Furthermore, the analysis unit can apply geographic information systems to the analysis of the location where the trouble occurred. In addition, the analysis unit can apply statistical analysis to the analysis of the impact of the trouble. By applying analysis methods appropriate to the category of the trouble, the accuracy of the analysis can be improved.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. In this way, by adjusting the display method of the analysis results according to the user's emotions, results that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The analysis unit can determine the priority of analysis based on the submission date of the submitted cases. For example, the analysis unit can prioritize the analysis of recently submitted cases. It can also prioritize the analysis of cases submitted within a specific time period. Furthermore, the analysis unit can prioritize the analysis of the most important cases, taking into account the submission date of the submitted cases. This allows for the rapid analysis of the latest cases by determining the priority of analysis based on the submission date of the submitted cases.

[0082] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and databases during the analysis process. For example, it can improve the accuracy of its analysis by referring to relevant academic papers. It can also improve the accuracy of its analysis by referring to relevant databases. Furthermore, it can improve the accuracy of its analysis by referring to relevant industry reports. In this way, the accuracy of the analysis can be improved by referring to relevant literature and databases.

[0083] The generation unit can estimate the user's emotions and adjust the level of detail of the simulation it generates based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a detailed simulation. If the user is in a hurry, it can generate a concise simulation. Furthermore, if the user is excited, it can generate a visually stimulating simulation. By adjusting the level of detail of the simulation according to the user's emotions, a more appropriate simulation can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The generation unit can optimize its generation algorithm by referring to past generation data during generation. For example, the generation unit can select the optimal generation algorithm based on past generation data. Furthermore, the generation unit can adjust the generation algorithm by referring to past generation results. In addition, the generation unit can analyze past generation data to improve the accuracy of the generation algorithm. Thus, by referring to past generation data, the accuracy of the generation algorithm can be improved.

[0085] The generation unit can apply different generation methods depending on the category of the trouble during generation. For example, the generation unit can apply natural language processing to simulations concerning the behavior of complainers. It can also apply geographic information systems to simulations concerning the location where the trouble occurred. Furthermore, it can apply statistical analysis to simulations concerning the impact of the trouble. By applying generation methods appropriate to the category of the trouble, the accuracy of the simulation can be improved.

[0086] The generation unit can estimate the user's emotions and determine the priority of simulations to generate based on the estimated emotions. For example, if the user is feeling strong anxiety, the generation unit can prioritize generating that simulation. It can also quickly generate a simulation if the user is feeling angry. Furthermore, if the user is calm, the generation unit can prioritize other, more urgent simulations. This allows for the rapid delivery of urgent simulations by prioritizing them based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The generation unit can prioritize generating highly relevant simulations by considering the user's geographical location information during the generation process. For example, the generation unit can generate simulations based on trouble cases that occurred near the user's current location. Furthermore, the generation unit can incorporate region-specific trouble cases into the simulations based on the user's geographical location information. In addition, the generation unit can prioritize generating the most relevant simulations by considering the user's location information. This allows for efficient simulation of region-specific trouble cases by considering the user's geographical location information.

[0088] The generation unit can improve the accuracy of its generation by referring to relevant literature and databases during the generation process. For example, it can improve the accuracy of its generation by referring to relevant academic papers. It can also improve the accuracy of its generation by referring to relevant databases. Furthermore, it can improve the accuracy of its generation by referring to relevant industry reports. In this way, the accuracy of the generation can be improved by referring to relevant literature and databases.

[0089] The delivery unit can estimate the user's emotions and adjust the simulation delivery method based on the estimated emotions. For example, if the user is nervous, the delivery unit can provide a simple and highly visual delivery method. If the user is relaxed, the delivery unit can provide a delivery method that includes detailed information. Furthermore, if the user is in a hurry, the delivery unit can provide a delivery method that gets straight to the point. This allows for the selection of a more appropriate delivery method by adjusting the simulation delivery method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The service provider can optimize its service provision algorithm by referring to past service provision data during the service provision process. For example, the service provider can select the optimal service provision algorithm based on past service provision data. Furthermore, the service provider can adjust its service provision algorithm by referring to past service provision results. In addition, the service provider can analyze past service provision data to improve the accuracy of its service provision algorithm. Thus, by referring to past service provision data, the accuracy of the service provision algorithm can be improved.

[0091] The service provider can customize the simulation based on the user's store information at the time of delivery. For example, the service provider can customize the simulation based on the layout of the user's store. Furthermore, the service provider can customize the simulation based on the services offered at the user's store. In addition, the service provider can customize the simulation based on the customer base of the user's store. This allows for more practical training by customizing the simulation based on the user's store information.

[0092] The service provider can estimate the user's emotions and determine the order in which simulations are provided based on the estimated emotions. For example, if the user is feeling strong anxiety, the service provider can prioritize providing that simulation. Similarly, if the user is feeling angry, the service provider can quickly provide that simulation. Furthermore, if the user is calm, the service provider can prioritize other simulations of higher urgency. This allows for the rapid provision of high-urgency simulations by determining the order of simulations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The service provider can select the optimal delivery method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can provide a delivery method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a delivery method optimized for a larger screen. In addition, if the user is using a VR device, the service provider can provide a delivery method optimized for VR. This allows the service provider to select the optimal delivery method by considering the user's device information.

[0094] The service provider can improve the accuracy of its service by referring to relevant literature and databases during the service provision process. For example, it can improve the accuracy of its service by referring to relevant academic papers. It can also improve the accuracy of its service by referring to relevant databases. Furthermore, it can improve the accuracy of its service by referring to relevant industry reports. In this way, the accuracy of the service can be improved by referring to relevant literature and databases.

[0095] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0096] The simulation system may also include a feedback unit. The feedback unit can collect feedback from users who have experienced the simulation and provide it to the analysis unit. For example, the feedback unit can allow users to input feedback in the form of a questionnaire after completing the simulation. The feedback unit can also estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, if the user is feeling stressed, a concise feedback form can be provided; if they are relaxed, a more detailed feedback form can be provided. This allows the feedback unit to collect feedback tailored to the user's emotions, which can then be used to improve the simulation.

[0097] The simulation system may also include an advice unit. This unit can provide advice to the user based on the simulation results. For example, it can suggest areas for improvement and effective response methods for the user's actions during the simulation. Furthermore, the advice unit can estimate the user's emotions and adjust the advice based on those emotions. For instance, if the user is feeling anxious, it can provide advice that includes words of encouragement; if the user is relaxed, it can provide detailed explanations of specific areas for improvement. This allows the advice unit to improve the user's ability to handle real-world problems by providing advice tailored to their emotions.

[0098] The simulation system may also include a recording unit. This unit can record the user's actions and reactions during the simulation, making them available to the analysis unit later. For example, the recording unit can record the user's responses and statements during the simulation. Furthermore, the recording unit can estimate the user's emotions and adjust the recording content based on those estimates. For instance, if the user is tense, a detailed record of their actions can be made; if they are relaxed, a concise record can be made. This allows the recording unit to tailor its recording to the user's emotions, which can then be used for later analysis and improvement.

[0099] The simulation system may also include a notification unit. This notification unit can inform the user of the simulation's progress and results. For example, it can send notifications to the user at the start and end of the simulation. Furthermore, the notification unit can estimate the user's emotions and adjust the content of notifications based on those emotions. For instance, if the user is feeling anxious, it can send a reassuring notification; if they are relaxed, it can provide detailed progress updates. This allows the notification unit to enhance the effectiveness of the simulation by providing notifications tailored to the user's emotions.

[0100] The simulation system may also include an evaluation unit. This unit can assess the simulation results and provide feedback to the user. For example, it can score the user's responses during the simulation and notify the user of the results. Furthermore, the evaluation unit can estimate the user's emotions and adjust the evaluation based on those emotions. For instance, if the user is stressed, it can prioritize positive feedback; if relaxed, it can provide detailed explanations of specific areas for improvement. This allows the evaluation unit to enhance user motivation by providing evaluations tailored to the user's emotions.

[0101] The simulation system may also include a data analysis unit. This unit can analyze data collected during the simulation and extract trends and patterns. For example, it can compare multiple simulation results to identify common problems and areas for improvement. Furthermore, it can optimize the simulation based on the analysis results. This allows the data analysis unit to provide valuable insights for improving the effectiveness of the simulation.

[0102] The simulation system can also include a customization section. This customization section can customize the simulation content according to the user's needs and requests. For example, the customization section can provide simulation scenarios tailored to specific industries or occupations. Furthermore, the customization section can suggest optimal simulation content based on the user's past simulation history. This allows the customization section to provide the most effective simulation for the user.

[0103] The simulation system can also be equipped with a real-time support unit. This unit can provide real-time support to the user during the simulation. For example, it can suggest appropriate responses when the user encounters a difficult situation. Furthermore, the real-time support unit can estimate the user's emotions and adjust the support based on those emotions. For instance, if the user is tense, it can provide concise and easy-to-understand advice; if relaxed, it can provide detailed explanations. This allows the real-time support unit to enhance the effectiveness of the simulation by providing support tailored to the user's emotions.

[0104] The simulation system can also include a scenario generation unit. This unit can automatically generate new simulation scenarios based on user submissions and past simulation results. For example, it can analyze user-submitted trouble cases and combine similar cases to generate new scenarios. Furthermore, the scenario generation unit can estimate the user's emotions and adjust the difficulty and content of the scenarios based on those emotions. For instance, if the user is stressed, it can provide a less difficult scenario; if they are relaxed, it can provide a more difficult scenario. This allows the scenario generation unit to enhance the effectiveness of the simulation by providing scenarios tailored to the user's emotions.

[0105] The simulation system may also include a training unit. The training unit can provide training programs to users based on the simulation results. For example, the training unit can create individualized training programs based on an evaluation of the user's responses during the simulation. Furthermore, the training unit can estimate the user's emotions and adjust the training content based on those emotions. For instance, if the user is feeling anxious, it can provide training to help them relax; if they are relaxed, it can provide training to learn specific response methods. This allows the training unit to improve the user's ability to handle real-world problems by providing training tailored to their emotions.

[0106] The following briefly describes the processing flow for example form 2.

[0107] Step 1: The posting section allows users to submit examples of customer complaints and troubles. For example, businesses using the service can submit examples of customer complaints and troubles they have encountered. Users can describe the specific details of the trouble and its circumstances, including the specific words and actions of the customer at the restaurant, as well as the time and location where the trouble occurred. Step 2: The analysis unit analyzes the cases submitted by the posting unit. For example, it can analyze the submitted text data and use methods such as text analysis and sentiment analysis to analyze the submitted cases. Step 3: The generation unit generates a simulation based on the cases analyzed by the analysis unit. Using the generation AI, a fictional, domineering avatar can be generated. The generation AI can analyze the posted text data using technologies such as deep learning and generative opposite networks (GANs) to generate a fictional, domineering avatar. Step 4: The provider unit provides the simulation generated by the generator unit. For example, the simulation can be provided using VR or video games. The provider unit can provide a simulation in which a fictional, overbearing avatar recreates a trouble scenario based on the user's store information.

[0108] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0109] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0110] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0111] Each of the multiple elements described above, including the posting unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the posting unit can post cases of complainers and troubles using the receiving device 38 of the smart device 14. The analysis unit analyzes the posted cases using the specific processing unit 290 of the data processing unit 12. The generation unit generates a fictional, aggressive avatar using a generation AI, for example, using the specific processing unit 290 of the data processing unit 12. The provision unit can provide a simulation using the output device 40 of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0112] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0113] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0115] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0116] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0118] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0119] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0120] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0121] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0122] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0124] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0126] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] Each of the multiple elements described above, including the posting unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the posting unit can post cases of complainers or troubles using the microphone 238 of the smart glasses 214. The analysis unit analyzes the posted cases using the specific processing unit 290 of the data processing unit 12. The generation unit generates a fictional, aggressive avatar using a generation AI, for example, using the specific processing unit 290 of the data processing unit 12. The provision unit can provide simulations using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0128] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0129] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0132] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the posting unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the posting unit can post cases of complainers or troubles using the microphone 238 of the headset terminal 314. The analysis unit analyzes the posted cases using the specific processing unit 290 of the data processing unit 12. The generation unit generates a fictional, aggressive avatar using a generation AI, for example, using the specific processing unit 290 of the data processing unit 12. The provision unit can provide simulations using the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0144] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0145] As shown in Figure 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.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0148] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0152] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the posting unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the posting unit can post cases of complainers or troubles using the microphone 238 of the robot 414. The analysis unit analyzes the posted cases using the specific processing unit 290 of the data processing unit 12, for example. The generation unit generates a fictional, aggressive avatar using a generation AI, for example, by the specific processing unit 290 of the data processing unit 12. The provision unit can provide simulations using the speaker 240 of the robot 414, for example. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0161] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0162] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0163] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0164] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0165] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0166] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0167] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0168] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0169] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0170] 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.

[0171] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0172] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0173] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0174] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0175] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0176] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0177] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0178] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0179] (Note 1) A posting section for submitting examples of complainers and troubles, An analysis unit that analyzes the cases submitted by the aforementioned submission unit, A generation unit that generates a simulation based on the case analyzed by the aforementioned analysis unit, The system comprises a providing unit that provides the simulation generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Generating AI creates a fictional, domineering avatar. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Providing simulations using VR and video games The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned submission section, Describe the specific nature and circumstances of the problem in detail. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Analyze the submitted text data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We provide simulations tailored to the user's store information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned submission section, It estimates the user's sentiment and adjusts the level of detail in the post based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned submission section, When you post, the system will automatically suggest similar trouble cases by referring to your past posting history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned submission section, When a post is submitted, the content is prioritized based on the frequency and impact of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned submission section, It estimates user sentiment and determines the priority of posts based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned submission section, When posting, the system prioritizes posting highly relevant examples by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned submission section, When posting, the system analyzes the user's social media activity and automatically posts relevant trouble cases. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis methods are applied depending on the category of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on the submission date of the submitted cases. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant literature and databases to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the level of detail of the simulation generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the generation algorithm is optimized by referring to past generation data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, different generation methods are applied depending on the category of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and determines the priority of simulations to generate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the system prioritizes generating simulations that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, we refer to relevant literature and databases to improve the accuracy of the generation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the simulation is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing a service, the service algorithm is optimized by referring to past service data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the simulation will be customized based on the user's store information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the user's emotions and determines the order in which simulations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing data, we refer to relevant literature and databases to improve the accuracy of the data provided. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A posting section for submitting examples of complainers and troubles, An analysis unit that analyzes the cases submitted by the aforementioned submission unit, A generation unit that generates a simulation based on the case analyzed by the aforementioned analysis unit, The system comprises a providing unit that provides the simulation generated by the generation unit. A system characterized by the following features.

2. The generating unit is The AI ​​generates a fictional, domineering avatar. The system according to feature 1.

3. The aforementioned supply unit is, Providing simulations using VR and video games The system according to feature 1.

4. The aforementioned submission section, Describe the specific nature and circumstances of the problem in detail. The system according to feature 1.

5. The aforementioned analysis unit, Analyze the submitted text data. The system according to feature 1.

6. The aforementioned supply unit is, We provide simulations tailored to the user's store information. The system according to feature 1.

7. The aforementioned submission section, It estimates the user's sentiment and adjusts the level of detail in the post based on the estimated sentiment. The system according to feature 1.

8. The aforementioned submission section, When you post, the system will automatically suggest similar trouble cases by referring to your past posting history. The system according to feature 1.

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