System

Abstract

An object of a system according to an embodiment is to enable a user of a ticket vending machine to obtain a quick and appropriate answer.SOLUTION: A system includes a ticket vending machine, a question analysis part, an answer generation part, and a display part. The ticket vending machine is equipped with a generation AI. The question analysis unit analyzes a question of a user. The answer generation unit generates an answer based on the question analyzed by the question analysis unit. The display unit displays the answer generated by the answer generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult for ticket vending machine users to receive prompt and appropriate answers to their questions.

[0005] The system according to the embodiment aims to enable users of ticket vending machines to receive prompt and appropriate answers. [Means for solving the problem]

[0006] The system according to the embodiment includes a ticket vending machine, a question analysis unit, an answer generation unit, and a display unit. The ticket vending machine is equipped with a generation AI. The question analysis unit analyzes a user's question. The answer generation unit generates an answer based on the question analyzed by the question analysis unit. The display unit displays the answer generated by the answer generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows the user of the ticket vending machine to receive a prompt and appropriate response. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The ticket vending machine system according to the embodiment of the present invention is equipped with a generation AI that generates appropriate answers to users' questions and requests and supports the operation of the ticket vending machine. This enables the ticket vending machine system to improve user convenience and provide prompt and accurate support.

[0029] A ticket vending machine system according to an embodiment includes a generation AI, a question analysis unit, an answer generation unit, and a display unit. The generation AI analyzes a user's question and generates an appropriate answer. For example, the generation AI uses a text generation AI (e.g., GPT-3) to analyze the user's question and generate an answer. The generation AI can also analyze voice input using speech recognition technology and generate an answer. The generation AI can also analyze the intent of the question and generate an appropriate answer using natural language processing technology. The question analysis unit provides information to the answer generation unit based on the question analyzed by the generation AI. For example, the question analysis unit analyzes the user's question as text data and sends it to the answer generation unit. The question analysis unit can also analyze voice data, convert it into text data, and send it to the answer generation unit. The question analysis unit can also analyze the intent of the question and provide appropriate information to the answer generation unit. The answer generation unit generates an appropriate answer based on the information provided by the question analysis unit. For example, the answer generation unit sends the answer generated by the generation AI to the display unit. The answer generation unit can also output the answer generated by the generation AI as audio data. The answer generation unit can also transmit the answer generated by the generation AI to the display unit as text data. The display unit displays the answer generated by the answer generation unit. For example, the display unit displays the answer generated by the generation AI on a screen. The display unit can also output the answer generated by the generation AI as audio. The display unit can also display the answer generated by the generation AI as text data. As a result, the ticket vending machine system according to the embodiment can generate appropriate answers to user questions and support the operation of the ticket vending machine. For example, if a user asks, "I want to buy a Shinkansen ticket to Tokyo," the generation AI analyzes the question and generates an appropriate answer. Furthermore, if the user is unsure how to operate the ticket vending machine, the generation AI provides support in real time, enabling smooth operation. Furthermore, providing quick and accurate answers to user questions can improve user satisfaction.

[0030] The question analysis unit can learn the user's past question history and provide personalized answers. For example, the question analysis unit stores the user's past question history in a database, and when the same user asks a question again, provides personalized answers based on the past history. For example, the question analysis unit learns the content of questions the user has previously asked and provides related information. For example, the question analysis unit supports the next purchase based on information about previously purchased tickets. The question analysis unit also analyzes the user's question history and generates more detailed and personalized answers for frequently asked questions. In this way, the user's convenience can be improved by learning the user's past question history and providing personalized answers.

[0031] The question analysis unit can analyze the user's gestures or facial expressions to support non-verbal communication. For example, the question analysis unit analyzes the user's gestures using a camera and generates an answer based on actions such as pointing or waving. For example, it provides information about the direction indicated by the pointing. The question analysis unit also analyzes the user's facial expressions and generates an answer based on a smiling or confused expression. For example, it responds in a friendly manner to a smiling user and provides a detailed explanation to a confused user. The question analysis unit also analyzes the user's non-verbal communication and provides an answer at an appropriate time. For example, if the user is deep in thought, it provides additional information. In this way, by analyzing the user's gestures and facial expressions and supporting non-verbal communication, it is possible to improve user convenience.

[0032] The question analysis unit supports multiple languages ​​and can provide appropriate answers to foreign users as well. For example, the generation AI in the question analysis unit supports multiple languages, allowing foreign users to ask questions in their native language. For example, it supports English, Chinese, Korean, etc. The question analysis unit also allows the generation AI to generate appropriate answers according to the language selected by the user. For example, if a foreign user asks a question in Japanese, the answer will be provided in English. The question analysis unit also uses a multilingual generation AI to support foreign users so that they can operate the ticket machine smoothly. For example, it provides guidance displays and audio guides in foreign languages. This multilingual support makes it possible to provide appropriate answers to foreign users as well.

[0033] The new ticket vending machine is equipped with a facial recognition function, enabling rapid user authentication. The new ticket vending machine is equipped with, for example, a facial recognition camera, and automatically authenticates the user when they approach the ticket vending machine. For example, it provides a login function using facial recognition. The new ticket vending machine also uses the facial recognition function to quickly authenticate the user and provide personalized services. For example, it suggests tickets based on past usage history. The new ticket vending machine also uses the facial recognition function to authenticate the user and strengthen security. For example, it introduces an authentication process to prevent fraudulent use. As a result, the inclusion of the facial recognition function allows rapid user authentication.

[0034] New ticket vending machines can be equipped with a touchless operation function, improving convenience from a hygienic standpoint. For example, new ticket vending machines can be equipped with a touchless operation function, allowing users to operate the machine without touching the screen. For example, gesture operation or voice operation can be provided. Furthermore, new ticket vending machines can use the touchless operation function to enable users to use the machine in a hygienic manner. For example, an interface can be provided that allows operations to be completed by simply waving one's hand. Furthermore, new ticket vending machines can be equipped with a touchless operation function, allowing users to avoid contact. For example, a contactless payment system can be provided. Thus, by introducing the touchless operation function, convenience from a hygienic standpoint can be improved.

[0035] The new ticket vending machines are equipped with AR functionality and can provide visual guidance. For example, the new ticket vending machines are equipped with AR functionality, allowing users to receive visual guidance through a camera. For example, guidance signs within a station are displayed in AR. The new ticket vending machines also use AR functionality to allow users to visually understand how to operate the ticket vending machine. For example, operating procedures are displayed in AR. The new ticket vending machines are also equipped with AR functionality, allowing users to visually confirm information. For example, seating arrangements on tickets and route maps are displayed in AR. In this way, the AR functionality makes it possible to provide users with visual guidance.

[0036] New ticket vending machines can be equipped with a voice assistant function to accommodate visually impaired people. New ticket vending machines can be equipped with a voice assistant function to enable visually impaired people to operate the machine by voice. For example, voice guidance can be used to guide users through the operation procedures. New ticket vending machines can also use the voice assistant function to enable visually impaired people to use the machine smoothly. For example, voice guidance can be used to support the ticket purchasing process. New ticket vending machines can also be equipped with a voice assistant function to enable visually impaired people to use the machine independently. For example, voice guidance can be used to guide users through payment methods. In this way, adding a voice assistant function can accommodate visually impaired people.

[0037] The generation AI can analyze the content of the question and extract the optimal answer from FAQs and manuals. For example, the generation AI analyzes the content of a user's question and extracts the optimal answer from a related FAQ database. For example, if the user asks, "How do I purchase a Shinkansen ticket?", the generation AI provides the appropriate answer from the FAQ. The generation AI also analyzes the user's question and extracts the optimal answer from a manual database. For example, if the user asks, "How do I change my express ticket?", the generation AI provides the appropriate procedure from the manual. The generation AI also analyzes the content of a user's question and automatically extracts relevant information to generate an answer. For example, if the user asks, "What time does the next Shinkansen depart?", the generation AI provides an answer from timetable data. This improves user convenience by analyzing the content of the question and extracting the optimal answer from related FAQs and manuals.

[0038] The generation AI can present multiple answer options to a question, allowing the user to select one. For example, the generation AI presents multiple answer options to a user's question, allowing the user to select one. For example, when asked "How do I purchase a Shinkansen ticket?", multiple purchase methods are presented. The generation AI also analyzes the user's question and generates multiple answer options. For example, when asked "How do I change my express ticket?", options for the change procedure are presented. The generation AI also presents multiple answer options to a user's question, allowing the user to select the best answer. For example, when asked "What time does the next Shinkansen train depart?", multiple departure times are presented. This allows the user to select the best answer by presenting multiple answer options.

[0039] Generative AI can provide answers to questions using visuals or videos. For example, generative AI provides answers to user questions using visuals or videos. For example, if a user asks, "How do I purchase a Shinkansen ticket?", a video showing the operation steps will be provided. Generative AI can also analyze a user's question and generate answers using visuals or videos. For example, if a user asks, "How do I change my express ticket?", a visual guide to the change procedure will be provided. Generative AI can also provide answers to user questions using visuals or videos, providing information that is visually easy to understand. For example, if a user asks, "What time does the next Shinkansen train depart?", a visual display of the timetable will be provided. In this way, providing answers using visuals or videos can help users understand.

[0040] The generation AI can provide answers to questions by referring to the reviews and ratings of other users. For example, the generation AI provides answers to user questions by referring to the reviews and ratings of other users. For example, when asked "How do I purchase a Shinkansen ticket?", the generation AI will present methods that have been highly rated by other users. The generation AI also analyzes the user's question and generates an answer based on the reviews and ratings of other users. For example, when asked "How do I change my express ticket?", the generation AI will provide procedures that have been highly rated by other users. The generation AI also provides answers to user questions by referring to the reviews and ratings of other users, thereby providing highly reliable information. For example, when asked "What time does the next Shinkansen train depart?", the generation AI will provide information that has been highly rated by other users. In this way, by providing answers that refer to the reviews and ratings of other users, highly reliable information can be provided.

[0041] The generation AI can analyze past purchase history and suggest the most suitable ticket. For example, the generation AI stores the user's past purchase history in a database and suggests the most suitable ticket for the next purchase. For example, it suggests tickets for the same route based on information about tickets previously purchased. The generation AI also analyzes the user's past purchase history and suggests tickets that suit the user's preferences. For example, it suggests tickets for the same seat based on information about seats previously used. The generation AI also analyzes the user's purchase history and suggests tickets that suit frequently used routes and time periods. For example, it suggests commuter passes that suit commuting hours. In this way, by analyzing past purchase history and suggesting the most suitable tickets, it is possible to improve user convenience.

[0042] The generation AI can suggest the nearest station and route based on current location information. For example, the generation AI acquires the user's current location information and suggests the nearest station and route. For example, it displays the route from the current location to the nearest station. The generation AI also analyzes the user's current location information and suggests the optimal route. For example, it displays the shortest route from the current location to the destination. The generation AI also builds a system that suggests the nearest station and route based on the user's current location information. For example, it provides transfer information from the current location to the destination. This improves user convenience by suggesting the nearest station and route based on the current location information.

[0043] The generative AI can provide an interactive tutorial that guides the user through the purchasing process. For example, the generative AI can provide an interactive tutorial that guides the user through the purchasing process. For example, it can support the user so that they can purchase a ticket smoothly by showing them the operating procedures on the screen. The generative AI can also use an interactive tutorial to help the user visually understand how to operate a ticket machine. For example, it can display the operating procedures using animations. The generative AI can also guide the user through the purchasing process in real time, allowing them to purchase a ticket without hesitation. For example, it can provide audio guidance on the operating procedures. In this way, the provision of an interactive tutorial allows the user to proceed smoothly through the purchasing process.

[0044] The generation AI can provide discount information and campaign information in real time for purchases. For example, the generation AI provides discount information and campaign information in real time during the user's purchase process. For example, it displays discount information for specific time periods or routes. The generation AI also analyzes the user's purchase history and provides appropriate discount information and campaign information. For example, it displays discount information for routes that have been used in the past. The generation AI also provides discount information and campaign information in real time to support the user in purchasing the most suitable ticket. For example, it displays information on campaigns that are currently being carried out. This makes it possible to improve user convenience by providing discount information and campaign information in real time.

[0045] Generative AI can analyze behavioral data, learn patterns, and provide optimal services. For example, generative AI collects user behavioral data and analyzes that data to learn the user's patterns. For example, it identifies the routes and time periods that the user frequently uses. Generative AI also learns the user's behavioral patterns and provides optimal services based on them. For example, it prioritizes suggesting tickets for routes that the user frequently uses. Generative AI also analyzes the user's behavioral data, learns the user's patterns, and provides customized services. For example, it suggests tickets that match the user's preferences. In this way, it is possible to analyze behavioral data, learn patterns, and provide optimal services, thereby improving user convenience.

[0046] The generation AI can provide customized services tailored to the user's preferences based on behavioral data. For example, the generation AI analyzes the user's behavioral data and provides customized services tailored to the user's preferences. For example, it suggests tickets with seats that the user prefers. The generation AI also provides services tailored to the user's preferences based on the user's behavioral data. For example, it provides discount information for routes that the user frequently uses. The generation AI also analyzes the user's behavioral data and builds a system that provides customized services tailored to the user's preferences. For example, it suggests travel plans tailored to the user's preferences. In this way, user satisfaction can be improved by providing customized services tailored to the user's preferences based on behavioral data.

[0047] Generative AI can integrate behavioral data with other datasets to gain new insights. For example, generative AI can integrate user behavioral data with other datasets to gain new insights. For example, it can integrate it with user purchasing data to analyze the user's purchasing trends. Generative AI can also integrate user behavioral data with other datasets to provide new services. For example, it can integrate it with the user's travel history to suggest optimal travel plans. Generative AI can also integrate user behavioral data with other datasets to build a system that gains new insights. For example, it can integrate it with the user's health data to provide health-conscious services. In this way, by integrating behavioral data with other datasets and gaining new insights, it is possible to provide more appropriate services to users.

[0048] Generative AI can predict future behavior based on behavioral data and provide services in advance. Generative AI, for example, analyzes user behavioral data and predicts future behavior. For example, it predicts the next time of use based on past usage history. Generative AI can also predict future behavior based on user behavioral data and provide services in advance. For example, it can predict the next travel schedule and suggest the most suitable ticket. Generative AI can also analyze user behavioral data and build a system that predicts future behavior. For example, it can predict a user's purchasing trends and provide discount information in advance. This makes it possible to predict future behavior based on behavioral data and provide services in advance, thereby improving user convenience.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The question analysis unit can learn the user's past question history and provide personalized answers. For example, it can provide related information based on the content of questions the user has previously asked. It can also analyze the user's question history and generate more detailed and personalized answers for frequently asked questions. Furthermore, it can provide a quick and appropriate answer to the next question based on the user's past question history. This can improve user convenience.

[0051] The question analysis unit can analyze the user's gestures or facial expressions to support non-verbal communication. For example, the question analysis unit can analyze the user's gestures using a camera and generate answers based on actions such as pointing or waving. It can also analyze the user's facial expressions to generate answers based on smiling or confused expressions. It can also analyze the user's non-verbal communication and provide answers at an appropriate time. This allows the system to analyze the user's gestures and facial expressions to support non-verbal communication, thereby improving user convenience.

[0052] The question analysis unit supports multiple languages ​​and can provide appropriate answers to foreign users. For example, the generation AI can support multiple languages, allowing foreign users to ask questions in their native language. The generation AI can also generate appropriate answers depending on the language selected by the user. Furthermore, it can provide guidance displays and audio guides in foreign languages. This makes it possible to provide appropriate answers to foreign users by supporting multiple languages.

[0053] The new ticket vending machine is equipped with a facial recognition function, which allows for quick user authentication. For example, a facial recognition camera can be installed, allowing for automatic authentication when a user approaches the ticket vending machine. Furthermore, the facial recognition function can be used to quickly authenticate users and provide personalized services. Furthermore, the facial recognition function can be used to authenticate users and strengthen security. Thus, the inclusion of the facial recognition function allows for quick user authentication.

[0054] The new ticket vending machine can be equipped with a touchless operation function to improve convenience in terms of hygiene. For example, the touchless operation function can be installed to allow users to operate the machine without touching the screen. The touchless operation function can also be used to allow users to use the ticket vending machine in a hygienic manner. Furthermore, the touchless operation function can be installed to allow users to avoid contact. Thus, the installation of the touchless operation function can improve convenience in terms of hygiene.

[0055] New ticket vending machines can be equipped with AR functions to provide visual guidance. For example, the machine can be equipped with AR functions to allow users to receive visual guidance through a camera. The AR function can also be used to allow users to visually understand how to operate the ticket vending machine. Furthermore, the AR function can be introduced to allow users to visually confirm information. Thus, by incorporating the AR function, visual guidance can be provided to users.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The question analysis unit analyzes the user's question. For example, the question analysis unit uses a generation AI (e.g., GPT-3) to analyze the user's question as text data and send it to the answer generation unit. The question analysis unit can also analyze voice data, convert it into text data, and send it to the answer generation unit. Furthermore, the question analysis unit can analyze the intent of the question and provide appropriate information to the answer generation unit. Step 2: The answer generation unit generates an appropriate answer based on the information provided by the question analysis unit. For example, the answer generation unit sends the answer generated by the generation AI to the display unit. The answer generation unit can also output the answer generated by the generation AI as audio data. Step 3: The display unit displays the answer generated by the answer generation unit. For example, the display unit displays the answer generated by the generation AI on a screen. The display unit can also output the answer generated by the generation AI by voice.

[0058] (Example 2) The ticket vending machine system according to the embodiment of the present invention is equipped with a generation AI that generates appropriate answers to users' questions and requests and supports the operation of the ticket vending machine. This enables the ticket vending machine system to improve user convenience and provide prompt and accurate support.

[0059] A ticket vending machine system according to an embodiment includes a generation AI, a question analysis unit, an answer generation unit, and a display unit. The generation AI analyzes a user's question and generates an appropriate answer. For example, the generation AI uses a text generation AI (e.g., GPT-3) to analyze the user's question and generate an answer. The generation AI can also analyze voice input using speech recognition technology and generate an answer. The generation AI can also analyze the intent of the question and generate an appropriate answer using natural language processing technology. The question analysis unit provides information to the answer generation unit based on the question analyzed by the generation AI. For example, the question analysis unit analyzes the user's question as text data and sends it to the answer generation unit. The question analysis unit can also analyze voice data, convert it into text data, and send it to the answer generation unit. The question analysis unit can also analyze the intent of the question and provide appropriate information to the answer generation unit. The answer generation unit generates an appropriate answer based on the information provided by the question analysis unit. For example, the answer generation unit sends the answer generated by the generation AI to the display unit. The answer generation unit can also output the answer generated by the generation AI as audio data. The answer generation unit can also transmit the answer generated by the generation AI to the display unit as text data. The display unit displays the answer generated by the answer generation unit. For example, the display unit displays the answer generated by the generation AI on a screen. The display unit can also output the answer generated by the generation AI as audio. The display unit can also display the answer generated by the generation AI as text data. As a result, the ticket vending machine system according to the embodiment can generate appropriate answers to user questions and support the operation of the ticket vending machine. For example, if a user asks, "I want to buy a Shinkansen ticket to Tokyo," the generation AI analyzes the question and generates an appropriate answer. Furthermore, if the user is unsure how to operate the ticket vending machine, the generation AI provides support in real time, enabling smooth operation. Furthermore, providing quick and accurate answers to user questions can improve user satisfaction.

[0060] The question analysis unit can analyze the tone and speed of the user's voice and generate an answer that corresponds to the level of urgency and emotional state. For example, the question analysis unit analyzes the tone and speed of the user's voice when speaking to the ticket machine, and generates a quick answer if the level of urgency is high. For example, the question analysis unit suggests the shortest route to a user who is in a hurry. The question analysis unit also analyzes the tone of the user's voice and generates an answer that relaxes the user if the user is feeling stressed. For example, the question analysis unit provides directions in a gentle tone. The question analysis unit also analyzes the speed of the user's voice and provides a polite and detailed explanation to a user who speaks slowly, and generates a concise answer to a user who speaks quickly. In this way, user satisfaction can be improved by generating an answer that corresponds to the level of urgency and emotional state of the user.

[0061] The question analysis unit can learn the user's past question history and provide personalized answers. For example, the question analysis unit stores the user's past question history in a database, and when the same user asks a question again, provides personalized answers based on the past history. For example, the question analysis unit learns the content of questions the user has previously asked and provides related information. For example, the question analysis unit supports the next purchase based on information about previously purchased tickets. The question analysis unit also analyzes the user's question history and generates more detailed and personalized answers for frequently asked questions. In this way, the user's convenience can be improved by learning the user's past question history and providing personalized answers.

[0062] The question analysis unit can use the emotion estimation function to estimate the user's emotional state in real time and generate an answer to reduce stress. The question analysis unit, for example, analyzes the user's facial expression and tone of voice, and if the user is feeling stressed, generates an answer that will relax the user using the emotion estimation function. For example, it calculates an emotion score based on changes in facial expression and provides an answer that has a relaxing effect. Furthermore, if the user is feeling anxious, the question analysis unit generates an answer that will give the user a sense of security. For example, it provides a detailed explanation or words of encouragement. Furthermore, the question analysis unit monitors the user's emotional state in real time and generates an answer that will elicit positive emotions. For example, it provides information tailored to the user's preferences. In this way, the user's emotional state can be estimated in real time and an answer to reduce stress can be generated, thereby improving user satisfaction.

[0063] The question analysis unit can analyze the user's gestures or facial expressions to support non-verbal communication. For example, the question analysis unit analyzes the user's gestures using a camera and generates an answer based on actions such as pointing or waving. For example, it provides information about the direction indicated by the pointing. The question analysis unit also analyzes the user's facial expressions and generates an answer based on a smiling or confused expression. For example, it responds in a friendly manner to a smiling user and provides a detailed explanation to a confused user. The question analysis unit also analyzes the user's non-verbal communication and provides an answer at an appropriate time. For example, if the user is deep in thought, it provides additional information. In this way, by analyzing the user's gestures and facial expressions and supporting non-verbal communication, it is possible to improve user convenience.

[0064] The question analysis unit supports multiple languages ​​and can provide appropriate answers to foreign users as well. For example, the generation AI in the question analysis unit supports multiple languages, allowing foreign users to ask questions in their native language. For example, it supports English, Chinese, Korean, etc. The question analysis unit also allows the generation AI to generate appropriate answers according to the language selected by the user. For example, if a foreign user asks a question in Japanese, the answer will be provided in English. The question analysis unit also uses a multilingual generation AI to support foreign users so that they can operate the ticket machine smoothly. For example, it provides guidance displays and audio guides in foreign languages. This multilingual support makes it possible to provide appropriate answers to foreign users as well.

[0065] The question analysis unit can use the emotion estimation function to provide entertainment information or tourist information according to the user's emotions. The question analysis unit, for example, analyzes the user's emotional state, and if the user is relaxed, provides entertainment information. For example, it displays movie screening information or event information. Furthermore, if the user's purpose is sightseeing, the question analysis unit uses the emotion estimation function to provide tourist information. For example, it displays information about recommended tourist spots and restaurants. Furthermore, the question analysis unit customizes and provides entertainment information or tourist information according to the user's emotions. For example, it displays information tailored to the user's preferences. In this way, by providing entertainment information or tourist information according to the user's emotions, it is possible to improve user satisfaction.

[0066] The new ticket vending machine is equipped with a facial recognition function, enabling rapid user authentication. The new ticket vending machine is equipped with, for example, a facial recognition camera, and automatically authenticates the user when they approach the ticket vending machine. For example, it provides a login function using facial recognition. The new ticket vending machine also uses the facial recognition function to quickly authenticate the user and provide personalized services. For example, it suggests tickets based on past usage history. The new ticket vending machine also uses the facial recognition function to authenticate the user and strengthen security. For example, it introduces an authentication process to prevent fraudulent use. As a result, the inclusion of the facial recognition function allows rapid user authentication.

[0067] New ticket vending machines can be equipped with a touchless operation function, improving convenience from a hygienic standpoint. For example, new ticket vending machines can be equipped with a touchless operation function, allowing users to operate the machine without touching the screen. For example, gesture operation or voice operation can be provided. Furthermore, new ticket vending machines can use the touchless operation function to enable users to use the machine in a hygienic manner. For example, an interface can be provided that allows operations to be completed by simply waving one's hand. Furthermore, new ticket vending machines can be equipped with a touchless operation function, allowing users to avoid contact. For example, a contactless payment system can be provided. Thus, by introducing the touchless operation function, convenience from a hygienic standpoint can be improved.

[0068] The new ticket vending machine can use the emotion estimation function to automatically change the interface design according to the user's emotions. The new ticket vending machine, for example, analyzes the user's emotional state and automatically changes the interface design according to the emotion. For example, if the user is relaxed, a calm design is displayed. The new ticket vending machine also uses the emotion estimation function to provide a customized interface according to the user's emotions. For example, if the user is feeling stressed, a simple and easy-to-understand design is displayed. The new ticket vending machine also builds a system that dynamically changes the interface design according to the user's emotions. For example, the theme and color are automatically set to match the user's preferences. In this way, by automatically changing the interface design using the emotion estimation function, user satisfaction can be improved.

[0069] The new ticket vending machines are equipped with AR functionality and can provide visual guidance. For example, the new ticket vending machines are equipped with AR functionality, allowing users to receive visual guidance through a camera. For example, guidance signs within a station are displayed in AR. The new ticket vending machines also use AR functionality to allow users to visually understand how to operate the ticket vending machine. For example, operating procedures are displayed in AR. The new ticket vending machines are also equipped with AR functionality, allowing users to visually confirm information. For example, seating arrangements on tickets and route maps are displayed in AR. In this way, the AR functionality makes it possible to provide users with visual guidance.

[0070] New ticket vending machines can be equipped with a voice assistant function to accommodate visually impaired people. New ticket vending machines can be equipped with a voice assistant function to enable visually impaired people to operate the machine by voice. For example, voice guidance can be used to guide users through the operation procedures. New ticket vending machines can also use the voice assistant function to enable visually impaired people to use the machine smoothly. For example, voice guidance can be used to support the ticket purchasing process. New ticket vending machines can also be equipped with a voice assistant function to enable visually impaired people to use the machine independently. For example, voice guidance can be used to guide users through payment methods. In this way, adding a voice assistant function can accommodate visually impaired people.

[0071] The new ticket vending machine can use the emotion estimation function to display advertisements and promotions that correspond to the user's emotions. The new ticket vending machine, for example, analyzes the user's emotional state and displays advertisements and promotions that correspond to the emotion. For example, if the user is relaxed, it displays an advertisement for a product that has a relaxing effect. The new ticket vending machine also uses the emotion estimation function to provide advertisements customized to the user's emotions. For example, if the user is feeling stressed, it displays an advertisement for a product that has a refreshing effect. The new ticket vending machine also builds a system that dynamically changes advertisements and promotions according to the user's emotions. For example, it automatically displays advertisements that match the user's preferences. In this way, by displaying advertisements and promotions using the emotion estimation function, it is possible to improve user satisfaction.

[0072] The generation AI can analyze the content of the question and extract the optimal answer from FAQs and manuals. For example, the generation AI analyzes the content of a user's question and extracts the optimal answer from a related FAQ database. For example, if the user asks, "How do I purchase a Shinkansen ticket?", the generation AI provides the appropriate answer from the FAQ. The generation AI also analyzes the user's question and extracts the optimal answer from a manual database. For example, if the user asks, "How do I change my express ticket?", the generation AI provides the appropriate procedure from the manual. The generation AI also analyzes the content of a user's question and automatically extracts relevant information to generate an answer. For example, if the user asks, "What time does the next Shinkansen depart?", the generation AI provides an answer from timetable data. This improves user convenience by analyzing the content of the question and extracting the optimal answer from related FAQs and manuals.

[0073] The generation AI can present multiple answer options to a question, allowing the user to select one. For example, the generation AI presents multiple answer options to a user's question, allowing the user to select one. For example, when asked "How do I purchase a Shinkansen ticket?", multiple purchase methods are presented. The generation AI also analyzes the user's question and generates multiple answer options. For example, when asked "How do I change my express ticket?", options for the change procedure are presented. The generation AI also presents multiple answer options to a user's question, allowing the user to select the best answer. For example, when asked "What time does the next Shinkansen train depart?", multiple departure times are presented. This allows the user to select the best answer by presenting multiple answer options.

[0074] The generation AI can use the emotion estimation function to adjust the tone and expression of answers to user questions. For example, the generation AI can analyze the user's emotional state and generate answers using a tone and expression that corresponds to the emotion. For example, a user who is feeling stressed can be provided with an answer in a gentle tone. The generation AI can also use the emotion estimation function to provide answers customized to the user's emotions. For example, a relaxed user can be provided with an answer in a friendly tone. The generation AI can also monitor the user's emotional state in real time and adjust the tone and expression of answers according to the emotion. For example, a confused user can be provided with a polite and detailed explanation. In this way, by using the emotion estimation function to adjust the tone and expression of answers, user satisfaction can be improved.

[0075] Generative AI can provide answers to questions using visuals or videos. For example, generative AI provides answers to user questions using visuals or videos. For example, if a user asks, "How do I purchase a Shinkansen ticket?", a video showing the operation steps will be provided. Generative AI can also analyze a user's question and generate answers using visuals or videos. For example, if a user asks, "How do I change my express ticket?", a visual guide to the change procedure will be provided. Generative AI can also provide answers to user questions using visuals or videos, providing information that is visually easy to understand. For example, if a user asks, "What time does the next Shinkansen train depart?", a visual display of the timetable will be provided. In this way, providing answers using visuals or videos can help users understand.

[0076] The generation AI can provide answers to questions by referring to the reviews and ratings of other users. For example, the generation AI provides answers to user questions by referring to the reviews and ratings of other users. For example, when asked "How do I purchase a Shinkansen ticket?", the generation AI will present methods that have been highly rated by other users. The generation AI also analyzes the user's question and generates an answer based on the reviews and ratings of other users. For example, when asked "How do I change my express ticket?", the generation AI will provide procedures that have been highly rated by other users. The generation AI also provides answers to user questions by referring to the reviews and ratings of other users, thereby providing highly reliable information. For example, when asked "What time does the next Shinkansen train depart?", the generation AI will provide information that has been highly rated by other users. In this way, by providing answers that refer to the reviews and ratings of other users, highly reliable information can be provided.

[0077] The generation AI can use the emotion estimation function to provide additional information that takes emotions into consideration when answering questions. For example, the generation AI can analyze the user's emotional state and provide additional information according to the emotion. For example, a user who is feeling stressed can be provided with information that has a relaxing effect. The generation AI can also use the emotion estimation function to provide additional information customized to the user's emotions. For example, a user who is relaxed can be provided with entertainment information. The generation AI can also monitor the user's emotional state in real time and build a system that provides additional information according to the emotion. For example, a confused user can be provided with detailed explanations or support information. In this way, providing additional information that takes emotions into consideration can improve user satisfaction.

[0078] The generation AI can analyze past purchase history and suggest the most suitable ticket. For example, the generation AI stores the user's past purchase history in a database and suggests the most suitable ticket for the next purchase. For example, it suggests tickets for the same route based on information about tickets previously purchased. The generation AI also analyzes the user's past purchase history and suggests tickets that suit the user's preferences. For example, it suggests tickets for the same seat based on information about seats previously used. The generation AI also analyzes the user's purchase history and suggests tickets that suit frequently used routes and time periods. For example, it suggests commuter passes that suit commuting hours. In this way, by analyzing past purchase history and suggesting the most suitable tickets, it is possible to improve user convenience.

[0079] The generation AI can suggest the nearest station and route based on current location information. For example, the generation AI acquires the user's current location information and suggests the nearest station and route. For example, it displays the route from the current location to the nearest station. The generation AI also analyzes the user's current location information and suggests the optimal route. For example, it displays the shortest route from the current location to the destination. The generation AI also builds a system that suggests the nearest station and route based on the user's current location information. For example, it provides transfer information from the current location to the destination. This improves user convenience by suggesting the nearest station and route based on the current location information.

[0080] The generation AI can use its emotion estimation function to provide purchasing support that is tailored to the user's emotions. For example, the generation AI can analyze the user's emotional state and provide purchasing support tailored to the user's emotions. For example, a user who is feeling stressed can be provided with support that has a relaxing effect. The generation AI can also use its emotion estimation function to provide customized purchasing support tailored to the user's emotions. For example, a relaxed user can be provided with entertainment information. The generation AI can also monitor the user's emotional state in real time and build a system that provides purchasing support tailored to the user's emotions. For example, a confused user can be provided with detailed explanations and support information. In this way, user satisfaction can be improved by providing purchasing support using the emotion estimation function.

[0081] The generative AI can provide an interactive tutorial that guides the user through the purchasing process. For example, the generative AI can provide an interactive tutorial that guides the user through the purchasing process. For example, it can support the user so that they can purchase a ticket smoothly by showing them the operating procedures on the screen. The generative AI can also use an interactive tutorial to help the user visually understand how to operate a ticket machine. For example, it can display the operating procedures using animations. The generative AI can also guide the user through the purchasing process in real time, allowing them to purchase a ticket without hesitation. For example, it can provide audio guidance on the operating procedures. In this way, the provision of an interactive tutorial allows the user to proceed smoothly through the purchasing process.

[0082] The generation AI can provide discount information and campaign information in real time for purchases. For example, the generation AI provides discount information and campaign information in real time during the user's purchase process. For example, it displays discount information for specific time periods or routes. The generation AI also analyzes the user's purchase history and provides appropriate discount information and campaign information. For example, it displays discount information for routes that have been used in the past. The generation AI also provides discount information and campaign information in real time to support the user in purchasing the most suitable ticket. For example, it displays information on campaigns that are currently being carried out. This makes it possible to improve user convenience by providing discount information and campaign information in real time.

[0083] The generation AI can use its emotion estimation function to provide post-purchase follow-up services that correspond to the user's emotions. For example, the generation AI can analyze the user's emotional state and provide post-purchase follow-up services that correspond to the user's emotions. For example, a user who is feeling stressed can be provided with follow-up services that have a relaxing effect. The generation AI can also use its emotion estimation function to provide customized follow-up services that correspond to the user's emotions. For example, a user who is relaxed can be provided with entertainment information. The generation AI can also monitor the user's emotional state in real time and build a system that provides follow-up services that correspond to the user's emotions. For example, a confused user can be provided with detailed explanations and support information. In this way, user satisfaction can be improved by providing post-purchase follow-up services using the emotion estimation function.

[0084] Generative AI can analyze behavioral data, learn patterns, and provide optimal services. For example, generative AI collects user behavioral data and analyzes that data to learn the user's patterns. For example, it identifies the routes and time periods that the user frequently uses. Generative AI also learns the user's behavioral patterns and provides optimal services based on them. For example, it prioritizes suggesting tickets for routes that the user frequently uses. Generative AI also analyzes the user's behavioral data, learns the user's patterns, and provides customized services. For example, it suggests tickets that match the user's preferences. In this way, it is possible to analyze behavioral data, learn patterns, and provide optimal services, thereby improving user convenience.

[0085] The generation AI can provide customized services tailored to the user's preferences based on behavioral data. For example, the generation AI analyzes the user's behavioral data and provides customized services tailored to the user's preferences. For example, it suggests tickets with seats that the user prefers. The generation AI also provides services tailored to the user's preferences based on the user's behavioral data. For example, it provides discount information for routes that the user frequently uses. The generation AI also analyzes the user's behavioral data and builds a system that provides customized services tailored to the user's preferences. For example, it suggests travel plans tailored to the user's preferences. In this way, user satisfaction can be improved by providing customized services tailored to the user's preferences based on behavioral data.

[0086] The generative AI can use its emotion estimation function to analyze behavior based on emotions and provide optimal services. For example, the generative AI analyzes a user's emotional state and analyzes behavior based on emotions. For example, it can provide a service with a relaxing effect to a user who is feeling stressed. The generative AI can also use its emotion estimation function to analyze behavior based on a user's emotions and provide customized services. For example, it can provide entertainment information to a user who is relaxed. The generative AI can also build a system that monitors a user's emotional state in real time and analyzes behavior based on emotions. For example, it can provide a confused user with detailed explanations and support information. This allows for behavior analysis based on emotions and the provision of optimal services, thereby improving user satisfaction.

[0087] Generative AI can integrate behavioral data with other datasets to gain new insights. For example, generative AI can integrate user behavioral data with other datasets to gain new insights. For example, it can integrate it with user purchasing data to analyze the user's purchasing trends. Generative AI can also integrate user behavioral data with other datasets to provide new services. For example, it can integrate it with the user's travel history to suggest optimal travel plans. Generative AI can also integrate user behavioral data with other datasets to build a system that gains new insights. For example, it can integrate it with the user's health data to provide health-conscious services. In this way, by integrating behavioral data with other datasets and gaining new insights, it is possible to provide more appropriate services to users.

[0088] Generative AI can predict future behavior based on behavioral data and provide services in advance. Generative AI, for example, analyzes user behavioral data and predicts future behavior. For example, it predicts the next time of use based on past usage history. Generative AI can also predict future behavior based on user behavioral data and provide services in advance. For example, it can predict the next travel schedule and suggest the most suitable ticket. Generative AI can also analyze user behavioral data and build a system that predicts future behavior. For example, it can predict a user's purchasing trends and provide discount information in advance. This makes it possible to predict future behavior based on behavioral data and provide services in advance, thereby improving user convenience.

[0089] The generative AI can use its emotion estimation function to predict behavior based on emotions and provide optimal services. For example, the generative AI analyzes the user's emotional state and predicts behavior based on emotions. For example, it provides a service with a relaxing effect to a user who is feeling stressed. The generative AI can also use its emotion estimation function to predict behavior based on the user's emotions and provide customized services. For example, it provides entertainment information to a user who is relaxed. The generative AI can also build a system that monitors the user's emotional state in real time and predicts behavior based on emotions. For example, it provides detailed explanations and support information to a confused user. This makes it possible to predict behavior based on emotions and provide optimal services, thereby improving user satisfaction.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The question analysis unit can analyze the tone and speed of the user's voice and generate an answer that corresponds to the level of urgency and emotional state. For example, if the user is in a hurry, the shortest route can be suggested. Also, if the user is feeling stressed, an answer that relaxes the user can be generated. Furthermore, if the user speaks slowly, a detailed and polite explanation can be provided. In this way, by generating an answer that corresponds to the level of urgency and emotional state of the user, user satisfaction can be improved.

[0092] The question analysis unit can learn the user's past question history and provide personalized answers. For example, it can provide related information based on the content of questions the user has previously asked. It can also analyze the user's question history and generate more detailed and personalized answers for frequently asked questions. Furthermore, it can provide a quick and appropriate answer to the next question based on the user's past question history. This can improve user convenience.

[0093] The question analysis unit can use the emotion estimation function to estimate the user's emotional state in real time and generate answers to reduce stress. For example, by analyzing the user's facial expression and tone of voice, if the user is feeling stressed, it can provide an answer that has a relaxing effect. Also, if the user is feeling anxious, it can generate an answer that gives a sense of security. Furthermore, it can monitor the user's emotional state in real time and generate answers that elicit positive emotions. In this way, by estimating the user's emotional state in real time and generating answers to reduce stress, it is possible to improve user satisfaction.

[0094] The question analysis unit can analyze the user's gestures or facial expressions to support non-verbal communication. For example, the question analysis unit can analyze the user's gestures using a camera and generate answers based on actions such as pointing or waving. It can also analyze the user's facial expressions to generate answers based on smiling or confused expressions. It can also analyze the user's non-verbal communication and provide answers at an appropriate time. This allows the system to analyze the user's gestures and facial expressions to support non-verbal communication, thereby improving user convenience.

[0095] The question analysis unit supports multiple languages ​​and can provide appropriate answers to foreign users. For example, the generation AI can support multiple languages, allowing foreign users to ask questions in their native language. The generation AI can also generate appropriate answers depending on the language selected by the user. Furthermore, it can provide guidance displays and audio guides in foreign languages. This makes it possible to provide appropriate answers to foreign users by supporting multiple languages.

[0096] The question analysis unit can use the emotion estimation function to provide entertainment information or tourist information according to the user's emotions. For example, by analyzing the user's emotional state, entertainment information can be provided if the user is relaxed. Furthermore, tourist information can be provided if the user's purpose is sightseeing using the emotion estimation function. Furthermore, entertainment information or tourist information can be customized and provided according to the user's emotions. This can improve user satisfaction by providing entertainment information or tourist information according to the user's emotions.

[0097] The new ticket vending machine is equipped with a facial recognition function, which allows for quick user authentication. For example, a facial recognition camera can be installed, allowing for automatic authentication when a user approaches the ticket vending machine. Furthermore, the facial recognition function can be used to quickly authenticate users and provide personalized services. Furthermore, the facial recognition function can be used to authenticate users and strengthen security. Thus, the inclusion of the facial recognition function allows for quick user authentication.

[0098] The new ticket vending machine can be equipped with a touchless operation function to improve convenience in terms of hygiene. For example, the touchless operation function can be installed to allow users to operate the machine without touching the screen. The touchless operation function can also be used to allow users to use the ticket vending machine in a hygienic manner. Furthermore, the touchless operation function can be installed to allow users to avoid contact. Thus, the installation of the touchless operation function can improve convenience in terms of hygiene.

[0099] The new ticket vending machine can use the emotion estimation function to automatically change the interface design according to the user's emotions. For example, it can analyze the user's emotional state and automatically change the interface design according to the emotion. The emotion estimation function can also be used to provide a customized interface according to the user's emotions. Furthermore, a system can be built that dynamically changes the interface design according to the user's emotions. This can improve user satisfaction by automatically changing the interface design using the emotion estimation function.

[0100] New ticket vending machines can be equipped with AR functions to provide visual guidance. For example, the machine can be equipped with AR functions to allow users to receive visual guidance through a camera. The AR function can also be used to allow users to visually understand how to operate the ticket vending machine. Furthermore, the AR function can be introduced to allow users to visually confirm information. Thus, by incorporating the AR function, visual guidance can be provided to users.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The question analysis unit analyzes the user's question. For example, the question analysis unit uses a generation AI (e.g., GPT-3) to analyze the user's question as text data and send it to the answer generation unit. The question analysis unit can also analyze voice data, convert it into text data, and send it to the answer generation unit. Furthermore, the question analysis unit can analyze the intent of the question and provide appropriate information to the answer generation unit. Step 2: The answer generation unit generates an appropriate answer based on the information provided by the question analysis unit. For example, the answer generation unit sends the answer generated by the generation AI to the display unit. The answer generation unit can also output the answer generated by the generation AI as audio data. Step 3: The display unit displays the answer generated by the answer generation unit. For example, the display unit displays the answer generated by the generation AI on a screen. The display unit can also output the answer generated by the generation AI by voice.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A ticket vending machine equipped with generative AI, The ticket vending machine is a question analysis unit that analyzes a user's question; an answer generation unit that generates an answer based on the question analyzed by the question analysis unit; a display unit that displays the answer generated by the answer generation unit. A system characterized by:

2. The question analysis unit Analyzing the tone and speed of the user's voice to generate a response that corresponds to the level of urgency and emotional state 2. The system of claim 1.

3. The question analysis unit Learns the user's past question history and provides personalized answers 2. The system of claim 1.

4. The question analysis unit Estimating the user's emotional state in real time and generating a response to reduce stress 2. The system of claim 1.

5. The question analysis unit Analyzing the user's gestures or facial expressions to support non-verbal communication 2. The system of claim 1.

Images