system

Abstract

The system according to the embodiment aims to simulate in advance conversations with local staff and store clerks when traveling abroad. [Solution] A system according to an embodiment includes a registration unit, a generation unit, and a provision unit. The registration unit registers a travel itinerary, airline, belongings, and planned destinations. The generation unit automatically generates conversations with airport staff, cabin attendants, immigration officers, hotel staff, and shop clerks based on the information registered by the registration unit. The provision unit provides the conversations generated by the generation unit to the traveler.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to simulate conversations with local staff or store clerks when traveling abroad, which can make travelers feel anxious about communicating with them locally.

[0005] The system according to the embodiment aims to simulate in advance conversations with local staff and store clerks when traveling abroad. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration unit, a generation unit, and a provision unit. The registration unit registers the travel itinerary, airline, belongings, and planned destinations. The generation unit automatically generates conversations with airport staff, cabin attendants, immigration officers, hotel staff, and shop clerks based on the information registered by the registration unit. The provision unit provides the conversations generated by the generation unit to the traveler. [Effects of the Invention]

[0007] The system according to the embodiment can simulate in advance conversations with local staff and store clerks when traveling abroad. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A travel simulation system according to an embodiment of the present invention allows travelers to register their travel itinerary, airline, belongings, and planned destinations in advance. The system automatically generates conversations with airport staff, cabin attendants, immigration officials, hotel staff, shop clerks, and other parties based on the travelers' destinations. The generated conversations are different each time to simulate realistic conversations. This allows travelers to practice conversations throughout their entire trip in advance, enabling them to respond smoothly during the actual trip. For example, conversations are generated for various situations, such as checking in at the airport, checking in at a hotel, and purchasing tickets at tourist attractions. The generated conversations are customized based on the travelers' input information, enabling realistic simulations tailored to individual travel plans. First, a "registration unit" is required in advance for travelers to register their travel itinerary, airline, belongings, and planned destinations. Next, a "generation unit" is required to automatically generate conversations with airport staff, cabin attendants, immigration officials, hotel staff, shop clerks, and other parties based on the registered information. Finally, a "providing unit" is also required to provide the generated conversations to travelers. This allows travelers to practice conversations in advance. These components are interrelated; for example, the generation unit generates conversations based on information registered in the registration unit, and the provision unit provides those conversations to travelers. It is also important to note that the generated conversations are different each time, allowing travelers to adapt to a variety of situations. It should also be emphasized that the generated conversations are customized based on the information entered by the travelers, enabling realistic simulations tailored to individual travel plans. This allows travelers to practice conversations for the entire trip in advance, allowing them to respond smoothly during the actual trip.

[0029] A travel simulation system according to an embodiment includes a registration unit, a generation unit, and a provision unit. The registration unit allows a traveler to register a travel itinerary, airline, belongings, and planned destinations. The travel itinerary includes, but is not limited to, a departure date, a return date, and a length of stay. The airline includes, but is not limited to, a specific airline name or alliance. The belongings include, but are not limited to, clothing, electronic devices, and travel documents. The planned destinations include, but are not limited to, tourist attractions, restaurants, shopping malls, and the like. The generation unit automatically generates conversations with airport staff, cabin attendants, immigration officials, hotel staff, shop clerks, and the like based on the information registered by the registration unit. The generated conversations are generated using, for example, natural language processing technology or machine learning algorithms, but are not limited to, examples. The generation unit generates conversations based on information entered by the traveler using, for example, natural language processing technology. The generation unit can also generate conversations based on the traveler's past conversation history and travel plans using machine learning algorithms. The generation unit can also generate conversations so that the content is different each time. For example, the generation unit generates different conversations each time using methods such as random generation or scenario-based generation. The provision unit provides the conversations generated by the generation unit to the traveler. The provision unit allows the traveler to practice the conversations in advance using methods such as a simulation mode or interactive training. The provision unit can also estimate the traveler's emotions and adjust the display method of the conversations to be provided based on the estimated traveler's emotions. For example, if the traveler is nervous, the provision unit can provide a simple, highly visible display method. If the traveler is relaxed, the provision unit can provide a display method that includes detailed information. This allows the travel simulation system according to the embodiment to allow the traveler to practice conversations for the entire trip in advance, allowing for smooth responses during the actual trip. Some or all of the above-described processing by the provision unit may be performed using, for example, AI, or may be performed without using AI.For example, the providing unit can use techniques such as facial expression recognition and voice analysis to estimate the traveler's emotions, and can use AI models to adjust how the conversation is displayed based on the traveler's emotions.

[0030] The generation unit can generate conversations so that the content is different each time. The generation unit generates different conversations each time, for example, using methods such as random generation or scenario-based generation. For example, the generation unit uses random generation to randomly generate conversations based on information input by the traveler. The generation unit can also use scenario-based generation to generate conversations based on pre-prepared scenarios. For example, the generation unit prepares scenarios corresponding to various situations, such as checking in at an airport, checking in at a hotel, and purchasing tickets at a tourist destination, and generates conversations based on the scenarios. This allows the generation unit to enable the traveler to respond to various situations. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may use an AI model that uses natural language processing technology to randomly generate conversations based on information input by the traveler. The generation unit may also use an AI model that uses scenario-based generation to generate conversations based on pre-prepared scenarios.

[0031] The generation unit can generate customized conversations based on information input by the traveler. The generation unit generates conversations based on information such as the travel itinerary, airline, belongings, and planned destinations input by the traveler. For example, the generation unit generates conversations for airport check-in procedures and conversations with immigration officials based on the traveler's departure date, return date, and length of stay input by the traveler. The generation unit can also generate conversations with cabin attendants based on the traveler's airline input. For example, the generation unit generates conversations about the services of a specific airline. The generation unit can also generate conversations with airport staff based on the traveler's belongings input by the traveler. For example, the generation unit generates conversations about how to handle liquids if they are included in the traveler's belongings. The generation unit can also generate conversations with hotel staff or shop clerks based on the traveler's planned destinations input by the traveler. For example, the generation unit generates conversations about specific tourist attractions and restaurants. This allows the generation unit to provide a realistic simulation tailored to individual travel plans. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generator may use an AI model that generates a conversation based on the information entered by the traveler, or may use natural language processing techniques or machine learning algorithms to generate a customized conversation based on the information entered by the traveler.

[0032] The providing unit can provide the generated conversation to the traveler, allowing the traveler to practice the conversation in advance. The providing unit can enable the traveler to practice the conversation in advance, for example, using a method such as a simulation mode or interactive training. For example, the providing unit can use the simulation mode to allow the traveler to practice the conversation while simulating an actual travel situation. The providing unit can also use interactive training to allow the traveler to practice responding to the generated conversation. For example, the providing unit can provide an interface through which the traveler responds to the generated conversation by voice or text. This allows the providing unit to allow the traveler to practice the conversation in advance. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without AI. For example, the providing unit can use technologies such as facial expression recognition and voice analysis to estimate the traveler's emotions. The providing unit can also use an AI model to adjust the display method of the conversation based on the traveler's emotions. For example, if the traveler is nervous, the providing unit can provide a simple, highly visible display method. If the traveler is relaxed, the providing unit can provide a display method including detailed information. This allows the providing unit to allow the traveler to practice conversation in advance.

[0033] The generation unit can generate conversations corresponding to a variety of situations, such as checking in at an airport, checking in at a hotel, and purchasing tickets at a tourist attraction. The generation unit generates, for example, a conversation related to checking in at an airport. For example, the generation unit generates a conversation when a traveler goes to a check-in counter at an airport. The generation unit can also generate a conversation related to checking in at a hotel. For example, the generation unit generates a conversation when a traveler checks in at a hotel front desk. The generation unit can also generate a conversation related to purchasing tickets at a tourist attraction. For example, the generation unit generates a conversation when a traveler purchases tickets at a ticket counter at a tourist attraction. This allows the generation unit to enable travelers to practice conversations corresponding to various situations. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can use natural language processing technology or a machine learning algorithm to generate a conversation related to checking in at an airport. The generation unit can also use an AI model to generate a conversation related to checking in at a hotel. Furthermore, the generation unit can use natural language processing techniques and machine learning algorithms to generate conversations about purchasing tickets at tourist attractions.

[0034] The registration unit can analyze the traveler's past travel history and select the optimal registration method. The registration unit, for example, automatically registers related information based on places the traveler has visited in the past. For example, the registration unit automatically registers related information based on information about tourist spots the traveler has visited in the past and hotels where the traveler has stayed. The registration unit can also preferentially register preferred airlines and hotels based on the traveler's past travel history. For example, the registration unit preferentially registers preferred airlines and hotels based on information about airlines the traveler has used in the past and hotels where the traveler has stayed. The registration unit can also analyze the traveler's past travel patterns and suggest an optimal packing list. For example, the registration unit analyzes the traveler's past travel patterns and suggests a packing list that the traveler needs. This allows the registration unit to register optimal information based on the traveler's past travel history. Some or all of the above-described processing in the registration unit may be performed using, or without, AI. For example, the registration unit can use a machine learning algorithm to analyze the traveler's past travel history. The registration unit can also use an AI model to register optimal information based on the traveler's past travel history.

[0035] The registration unit can perform filtering based on the traveler's current interests and concerns at the time of registration. For example, the registration unit prioritizes registering tourist attractions and activities in which the traveler is currently interested. For example, the registration unit prioritizes registering information about tourist attractions and activities in which the traveler is currently interested. The registration unit can also filter and register information related to culture and history in which the traveler is interested. For example, the registration unit filters and registers information related to culture and history in which the traveler is interested. The registration unit can also register the latest tourist attraction and event information if the traveler is interested in current trends. For example, the registration unit registers the latest tourist attraction and event information if the traveler is interested in current trends. This allows the registration unit to register appropriate information based on the traveler's current interests and concerns. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can analyze data such as social media activity and search history to estimate the traveler's current interests and concerns. The registration unit can also use an AI model to perform filtering based on the traveler's current interests and concerns.

[0036] During registration, the registration unit can prioritize registering highly relevant information taking into consideration the traveler's geographical location information. For example, the registration unit prioritizes registering tourist attractions and activities close to the traveler's current location. For example, the registration unit prioritizes registering information about tourist attractions and activities close to the traveler's current location. The registration unit can also provide information about transportation means and routes that are easily accessible from the traveler's current location. For example, the registration unit provides information about transportation means and routes that are easily accessible from the traveler's current location. The registration unit can also suggest an appropriate packing list based on the climate and weather of the traveler's current location. For example, the registration unit suggests an appropriate packing list based on the climate and weather of the traveler's current location. This allows the registration unit to provide appropriate information based on the traveler's current location. Some or all of the above-described processing in the registration unit may be performed using, or without, AI. For example, the registration unit can use GPS data, address information, etc. to acquire the traveler's geographical location information. The registration unit can also use an AI model to prioritize registering highly relevant information based on the traveler's geographical location information.

[0037] The registration unit can analyze the traveler's social media activity at the time of registration and register related information. For example, the registration unit registers related tourist attractions and activities based on travel destination information shared by the traveler on social media. For example, the registration unit registers information on related tourist attractions and activities based on travel destination information shared by the traveler on social media. The registration unit can also register information based on travel-related accounts followed by the traveler on social media. For example, the registration unit registers related information based on travel-related accounts followed by the traveler on social media. The registration unit can also register information on events and festivals in which the traveler expressed interest on social media. For example, the registration unit registers information on events and festivals in which the traveler expressed interest on social media. This allows the registration unit to provide appropriate information based on the traveler's social media activity. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can use natural language processing technology or a machine learning algorithm to analyze the traveler's social media activity. The registration unit can also use an AI model to register related information based on the traveler's social media activity.

[0038] During generation, the generation unit can generate appropriate conversations by referring to the traveler's past conversation history. The generation unit generates natural conversations, for example, based on phrases and expressions used by the traveler in the past. For example, the generation unit generates natural conversations based on phrases and expressions used by the traveler in the past. The generation unit can also prioritize incorporating frequently used phrases from the traveler's past conversation history. For example, the generation unit prioritizes incorporating frequently used phrases from the traveler's past conversation history. The generation unit can also analyze the traveler's past conversation patterns to generate an optimal conversation scenario. For example, the generation unit analyzes the traveler's past conversation patterns to generate an optimal conversation scenario. This allows the generation unit to generate natural conversations based on the traveler's past conversation history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can use natural language processing technology or a machine learning algorithm to analyze the traveler's past conversation history. The generation unit can also use an AI model to generate appropriate conversations based on the traveler's past conversation history.

[0039] The generation unit can customize the content of the conversation based on the traveler's current travel plan when generating the conversation. The generation unit, for example, generates a conversation about tourist spots that the traveler plans to visit. For example, the generation unit generates a conversation about tourist spots that the traveler plans to visit. The generation unit can also customize conversations at airports and hotels based on the traveler's travel plan. For example, the generation unit customizes conversations about check-in procedures at airports and check-in procedures at hotels based on the traveler's travel plan. The generation unit can also generate conversations about restaurants and shops based on the traveler's schedule. For example, the generation unit generates conversations about restaurants and shops based on the traveler's schedule. This allows the generation unit to generate appropriate conversations based on the traveler's current travel plan. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can use natural language processing technology or a machine learning algorithm to analyze the traveler's current travel plan. The generation unit can also use an AI model to customize the content of the conversation based on the traveler's current travel plan.

[0040] The generation unit can generate highly relevant conversations by taking into account the traveler's geographical location information during generation. The generation unit, for example, generates conversations about tourist attractions and activities close to the traveler's current location. For example, the generation unit generates conversations about tourist attractions and activities close to the traveler's current location. The generation unit can also generate conversations about transportation means and routes that are easily accessible from the traveler's current location. For example, the generation unit generates conversations about transportation means and routes that are easily accessible from the traveler's current location. The generation unit can also generate appropriate conversations based on the climate and weather of the traveler's current location. For example, the generation unit generates appropriate conversations based on the climate and weather of the traveler's current location. This allows the generation unit to generate appropriate conversations based on the traveler's current location. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can use GPS data, address information, etc. to acquire the traveler's geographical location information. The generation unit can also use an AI model to generate highly relevant conversations based on the traveler's geographical location information.

[0041] The generation unit can analyze the traveler's social media activity and generate related conversations during generation. The generation unit can generate related conversations based on, for example, travel destination information shared by the traveler on social media. For example, the generation unit can generate related conversations based on travel destination information shared by the traveler on social media. The generation unit can also generate conversations by referring to information on travel-related accounts followed by the traveler on social media. For example, the generation unit can generate conversations by referring to information on travel-related accounts followed by the traveler on social media. The generation unit can also generate conversations related to events or festivals in which the traveler expressed interest on social media. For example, the generation unit generates conversations related to events or festivals in which the traveler expressed interest on social media. This allows the generation unit to generate appropriate conversations based on the traveler's social media activity. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can use natural language processing technology or machine learning algorithms to analyze the traveler's social media activity. The generation unit can also use an AI model to generate related conversations based on the traveler's social media activity.

[0042] The providing unit can provide optimal conversations by referring to the traveler's past practice history when providing the conversations. For example, the providing unit provides related conversations based on conversations the traveler has practiced in the past. For example, the providing unit provides related conversations based on conversations the traveler has practiced in the past. The providing unit can also prioritize providing frequently used phrases from the traveler's past practice history. For example, the providing unit can prioritize providing frequently used phrases from the traveler's past practice history. The providing unit can also analyze the traveler's past practice patterns and provide an optimal conversation scenario. For example, the providing unit can analyze the traveler's past practice patterns and provide an optimal conversation scenario. This allows the providing unit to provide appropriate conversations based on the traveler's past practice history. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can use natural language processing technology or machine learning algorithms to analyze the traveler's past practice history. The providing unit can also use an AI model to provide optimal conversations based on the traveler's past practice history.

[0043] The providing unit can customize the conversation provision method based on the traveler's current travel plan when providing the conversation. The providing unit, for example, provides a conversation about tourist spots that the traveler plans to visit. For example, the providing unit provides a conversation about tourist spots that the traveler plans to visit. The providing unit can also customize conversations at airports and hotels based on the traveler's travel plan. For example, the providing unit customizes conversations about check-in procedures at airports and check-in procedures at hotels based on the traveler's travel plan. The providing unit can also provide conversations at restaurants and shops based on the traveler's schedule. For example, the providing unit provides conversations about restaurants and shops based on the traveler's schedule. This allows the providing unit to provide appropriate conversations based on the traveler's current travel plan. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can use natural language processing technology or a machine learning algorithm to analyze the traveler's current travel plan. The providing unit can also use an AI model to customize the conversation provision method based on the traveler's current travel plan.

[0044] The providing unit can provide highly relevant conversations by taking into account the traveler's geographical location information when providing the conversations. The providing unit, for example, provides conversations about tourist attractions and activities close to the traveler's current location. For example, the providing unit provides conversations about tourist attractions and activities close to the traveler's current location. The providing unit can also provide conversations about transportation means and routes that are easily accessible from the traveler's current location. For example, the providing unit provides conversations about transportation means and routes that are easily accessible from the traveler's current location. The providing unit can also provide appropriate conversations based on the climate and weather of the traveler's current location. For example, the providing unit provides appropriate conversations based on the climate and weather of the traveler's current location. This allows the providing unit to provide appropriate conversations based on the traveler's current location. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can use GPS data, address information, etc. to acquire the traveler's geographical location information. The providing unit can also use an AI model to provide highly relevant conversations based on the traveler's geographical location information.

[0045] The providing unit can analyze the traveler's social media activity and provide related conversations when providing the data. The providing unit can provide related conversations based on, for example, travel destination information shared by the traveler on social media. For example, the providing unit can provide related conversations based on travel destination information shared by the traveler on social media. The providing unit can also provide conversations based on information about travel-related accounts followed by the traveler on social media. For example, the providing unit can provide conversations based on information about travel-related accounts followed by the traveler on social media. The providing unit can also provide conversations related to events or festivals in which the traveler expressed interest on social media. For example, the providing unit can provide conversations related to events or festivals in which the traveler expressed interest on social media. This allows the providing unit to provide appropriate conversations based on the traveler's social media activity. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can use natural language processing technology or machine learning algorithms to analyze the traveler's social media activity. The providing unit can also use an AI model to provide related conversations based on the traveler's social media activity.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The providing unit can refer to the traveler's past travel history and provide related conversations based on places the traveler has visited and activities the traveler has experienced in the past. For example, the providing unit can provide conversations about tourist spots the traveler has visited in the past. The providing unit can also provide conversations about similar activities based on activities the traveler has experienced in the past. Furthermore, the providing unit can also provide related conversations based on transportation means and accommodations the traveler has used in the past. This allows the providing unit to provide more personalized conversations based on the traveler's past travel history.

[0048] The generation unit can generate appropriate conversations taking into account the traveler's current health condition. For example, if the traveler is tired, the generation unit can generate conversations about relaxing tourist spots and activities. If the traveler is in good health, the generation unit can also generate conversations about active activities. Furthermore, if the traveler has a specific health problem, the generation unit can also generate conversations that address that problem. This allows the generation unit to provide appropriate conversations according to the traveler's health condition.

[0049] The generation unit can generate appropriate conversations taking into account the traveler's current weather information. For example, the generation unit generates conversations about tourist spots that the traveler plans to visit on a rainy day. The generation unit can also generate conversations about activities that the traveler plans to visit on a sunny day. Furthermore, the generation unit can generate conversations about places that the traveler plans to visit in the cold season. This allows the generation unit to provide appropriate conversations based on the traveler's current weather information.

[0050] The generation unit can generate appropriate conversations based on the traveler's current interests and concerns. For example, the generation unit generates conversations about tourist spots that the traveler is currently interested in. The generation unit can also generate conversations related to culture and history that the traveler is interested in. Furthermore, if the traveler is interested in current trends, the generation unit can also generate conversations about the latest tourist spots and events. This allows the generation unit to provide appropriate conversations based on the traveler's current interests and concerns.

[0051] The generation unit can analyze the travel history of the traveler and generate related conversations based on the places the traveler has visited and the activities the traveler has experienced in the past. For example, the generation unit generates conversations about tourist spots the traveler has visited in the past. The generation unit can also generate conversations about similar activities based on the activities the traveler has experienced in the past. Furthermore, the generation unit can generate related conversations based on the means of transportation and accommodations the traveler has used in the past. This allows the generation unit to provide more personalized conversations based on the traveler's travel history in the past.

[0052] The generation unit can generate appropriate conversations taking into account the traveler's current geographical location information. For example, the generation unit generates conversations about tourist spots close to the traveler's current location. The generation unit can also generate conversations about transportation methods and routes that are easily accessible from the traveler's current location. Furthermore, the generation unit can generate appropriate conversations based on the climate and weather of the traveler's current location. This allows the generation unit to provide appropriate conversations based on the traveler's current geographical location information.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: In the registration section, the traveler registers their travel itinerary, airline, belongings, and planned destinations. Travel itinerary includes departure date, return date, and length of stay, while airline includes specific airline name and alliance. Possessions include clothing, electronic devices, travel documents, etc., and planned destinations include tourist attractions, restaurants, shopping malls, etc. Step 2: The generation unit automatically generates conversations with airport staff, cabin attendants, immigration officials, hotel staff, shop clerks, etc. based on the information registered by the registration unit. The conversations are generated using natural language processing technology and machine learning algorithms. The generation unit generates conversations based on information entered by the traveler, and can also generate conversations based on past conversation history and travel plans. The generation unit also generates different conversations each time using methods such as random generation and scenario-based generation. Step 3: The providing unit provides the traveler with the conversation generated by the generating unit. The providing unit allows the traveler to practice the conversation in advance using methods such as a simulation mode or interactive training. The providing unit also estimates the traveler's emotions and adjusts the display method of the conversation to be provided based on the estimated traveler's emotions. For example, if the traveler is nervous, a simple, highly visible display method is provided, and if the traveler is relaxed, a display method including detailed information is provided.

[0055] (Example 2) A travel simulation system according to an embodiment of the present invention allows travelers to register their travel itinerary, airline, belongings, and planned destinations in advance. The system automatically generates conversations with airport staff, cabin attendants, immigration officials, hotel staff, shop clerks, and other parties based on the travelers' destinations. The generated conversations are different each time to simulate realistic conversations. This allows travelers to practice conversations throughout their entire trip in advance, enabling them to respond smoothly during the actual trip. For example, conversations are generated for various situations, such as checking in at the airport, checking in at a hotel, and purchasing tickets at tourist attractions. The generated conversations are customized based on the travelers' input information, enabling realistic simulations tailored to individual travel plans. First, a "registration unit" is required in advance for travelers to register their travel itinerary, airline, belongings, and planned destinations. Next, a "generation unit" is required to automatically generate conversations with airport staff, cabin attendants, immigration officials, hotel staff, shop clerks, and other parties based on the registered information. Finally, a "providing unit" is also required to provide the generated conversations to travelers. This allows travelers to practice conversations in advance. These components are interrelated; for example, the generation unit generates conversations based on information registered in the registration unit, and the provision unit provides those conversations to travelers. It is also important to note that the generated conversations are different each time, allowing travelers to adapt to a variety of situations. It should also be emphasized that the generated conversations are customized based on the information entered by the travelers, enabling realistic simulations tailored to individual travel plans. This allows travelers to practice conversations for the entire trip in advance, allowing them to respond smoothly during the actual trip.

[0056] A travel simulation system according to an embodiment includes a registration unit, a generation unit, and a provision unit. The registration unit allows a traveler to register a travel itinerary, airline, belongings, and planned destinations. The travel itinerary includes, but is not limited to, a departure date, a return date, and a length of stay. The airline includes, but is not limited to, a specific airline name or alliance. The belongings include, but are not limited to, clothing, electronic devices, and travel documents. The planned destinations include, but are not limited to, tourist attractions, restaurants, shopping malls, and the like. The generation unit automatically generates conversations with airport staff, cabin attendants, immigration officials, hotel staff, shop clerks, and the like based on the information registered by the registration unit. The generated conversations are generated using, for example, natural language processing technology or machine learning algorithms, but are not limited to, examples. The generation unit generates conversations based on information entered by the traveler using, for example, natural language processing technology. The generation unit can also generate conversations based on the traveler's past conversation history and travel plans using machine learning algorithms. The generation unit can also generate conversations so that the content is different each time. For example, the generation unit generates different conversations each time using methods such as random generation or scenario-based generation. The provision unit provides the conversations generated by the generation unit to the traveler. The provision unit allows the traveler to practice the conversations in advance using methods such as a simulation mode or interactive training. The provision unit can also estimate the traveler's emotions and adjust the display method of the conversations to be provided based on the estimated traveler's emotions. For example, if the traveler is nervous, the provision unit can provide a simple, highly visible display method. If the traveler is relaxed, the provision unit can provide a display method that includes detailed information. This allows the travel simulation system according to the embodiment to allow the traveler to practice conversations for the entire trip in advance, allowing for smooth responses during the actual trip. Some or all of the above-described processing by the provision unit may be performed using, for example, AI, or may be performed without using AI.For example, the providing unit can use techniques such as facial expression recognition and voice analysis to estimate the traveler's emotions, and can use AI models to adjust how the conversation is displayed based on the traveler's emotions.

[0057] The generation unit can generate conversations so that the content is different each time. The generation unit generates different conversations each time, for example, using methods such as random generation or scenario-based generation. For example, the generation unit uses random generation to randomly generate conversations based on information input by the traveler. The generation unit can also use scenario-based generation to generate conversations based on pre-prepared scenarios. For example, the generation unit prepares scenarios corresponding to various situations, such as checking in at an airport, checking in at a hotel, and purchasing tickets at a tourist destination, and generates conversations based on the scenarios. This allows the generation unit to enable the traveler to respond to various situations. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may use an AI model that uses natural language processing technology to randomly generate conversations based on information input by the traveler. The generation unit may also use an AI model that uses scenario-based generation to generate conversations based on pre-prepared scenarios.

[0058] The generation unit can generate customized conversations based on information input by the traveler. The generation unit generates conversations based on information such as the travel itinerary, airline, belongings, and planned destinations input by the traveler. For example, the generation unit generates conversations for airport check-in procedures and conversations with immigration officials based on the traveler's departure date, return date, and length of stay input by the traveler. The generation unit can also generate conversations with cabin attendants based on the traveler's airline input. For example, the generation unit generates conversations about the services of a specific airline. The generation unit can also generate conversations with airport staff based on the traveler's belongings input by the traveler. For example, the generation unit generates conversations about how to handle liquids if they are included in the traveler's belongings. The generation unit can also generate conversations with hotel staff or shop clerks based on the traveler's planned destinations input by the traveler. For example, the generation unit generates conversations about specific tourist attractions and restaurants. This allows the generation unit to provide a realistic simulation tailored to individual travel plans. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generator may use an AI model that generates a conversation based on the information entered by the traveler, or may use natural language processing techniques or machine learning algorithms to generate a customized conversation based on the information entered by the traveler.

[0059] The providing unit can provide the generated conversation to the traveler, allowing the traveler to practice the conversation in advance. The providing unit can enable the traveler to practice the conversation in advance, for example, using a method such as a simulation mode or interactive training. For example, the providing unit can use the simulation mode to allow the traveler to practice the conversation while simulating an actual travel situation. The providing unit can also use interactive training to allow the traveler to practice responding to the generated conversation. For example, the providing unit can provide an interface through which the traveler responds to the generated conversation by voice or text. This allows the providing unit to allow the traveler to practice the conversation in advance. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without AI. For example, the providing unit can use technologies such as facial expression recognition and voice analysis to estimate the traveler's emotions. The providing unit can also use an AI model to adjust the display method of the conversation based on the traveler's emotions. For example, if the traveler is nervous, the providing unit can provide a simple, highly visible display method. If the traveler is relaxed, the providing unit can provide a display method including detailed information. This allows the providing unit to allow the traveler to practice conversation in advance.

[0060] The generation unit can generate conversations corresponding to a variety of situations, such as checking in at an airport, checking in at a hotel, and purchasing tickets at a tourist attraction. The generation unit generates, for example, a conversation related to checking in at an airport. For example, the generation unit generates a conversation when a traveler goes to a check-in counter at an airport. The generation unit can also generate a conversation related to checking in at a hotel. For example, the generation unit generates a conversation when a traveler checks in at a hotel front desk. The generation unit can also generate a conversation related to purchasing tickets at a tourist attraction. For example, the generation unit generates a conversation when a traveler purchases tickets at a ticket counter at a tourist attraction. This allows the generation unit to enable travelers to practice conversations corresponding to various situations. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can use natural language processing technology or a machine learning algorithm to generate a conversation related to checking in at an airport. The generation unit can also use an AI model to generate a conversation related to checking in at a hotel. Furthermore, the generation unit can use natural language processing techniques and machine learning algorithms to generate conversations about purchasing tickets at tourist attractions.

[0061] The registration unit can estimate the traveler's emotions and determine the priority of information to be registered based on the estimated traveler's emotions. For example, if the traveler is excited, the registration unit prioritizes registering tourist attraction information at the travel destination. For example, if the traveler is excited, the registration unit prioritizes registering detailed information about tourist attractions and activity information. Furthermore, if the traveler is feeling anxious, the registration unit can prioritize registering information about airport and hotel procedures. For example, if the traveler is feeling anxious, the registration unit prioritizes registering information about airport check-in procedures and hotel check-in procedures. Furthermore, if the traveler is relaxed, the registration unit can prioritize registering a packing list and a travel itinerary. For example, if the traveler is relaxed, the registration unit prioritizes registering information about the packing list and the travel itinerary. This allows the registration unit to prioritize registering appropriate information according to the traveler's emotions. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit may use technologies such as facial expression recognition and voice analysis to estimate the traveler's emotions. The registration unit can also use AI models to prioritize the information to be registered based on traveler sentiment.

[0062] The registration unit can analyze the traveler's past travel history and select the optimal registration method. The registration unit, for example, automatically registers related information based on places the traveler has visited in the past. For example, the registration unit automatically registers related information based on information about tourist spots the traveler has visited in the past and hotels where the traveler has stayed. The registration unit can also preferentially register preferred airlines and hotels based on the traveler's past travel history. For example, the registration unit preferentially registers preferred airlines and hotels based on information about airlines the traveler has used in the past and hotels where the traveler has stayed. The registration unit can also analyze the traveler's past travel patterns and suggest an optimal packing list. For example, the registration unit analyzes the traveler's past travel patterns and suggests a packing list that the traveler needs. This allows the registration unit to register optimal information based on the traveler's past travel history. Some or all of the above-described processing in the registration unit may be performed using, or without, AI. For example, the registration unit can use a machine learning algorithm to analyze the traveler's past travel history. The registration unit can also use an AI model to register optimal information based on the traveler's past travel history.

[0063] The registration unit can perform filtering based on the traveler's current interests and concerns at the time of registration. For example, the registration unit prioritizes registering tourist attractions and activities in which the traveler is currently interested. For example, the registration unit prioritizes registering information about tourist attractions and activities in which the traveler is currently interested. The registration unit can also filter and register information related to culture and history in which the traveler is interested. For example, the registration unit filters and registers information related to culture and history in which the traveler is interested. The registration unit can also register the latest tourist attraction and event information if the traveler is interested in current trends. For example, the registration unit registers the latest tourist attraction and event information if the traveler is interested in current trends. This allows the registration unit to register appropriate information based on the traveler's current interests and concerns. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can analyze data such as social media activity and search history to estimate the traveler's current interests and concerns. The registration unit can also use an AI model to perform filtering based on the traveler's current interests and concerns.

[0064] The registration unit can estimate the traveler's emotions and adjust the level of detail of the information to be registered based on the estimated traveler's emotions. For example, if the traveler is excited, the registration unit provides detailed information about tourist attractions and descriptions of activities. For example, if the traveler is excited, the registration unit provides detailed information about tourist attractions and descriptions of activities. Furthermore, if the traveler is anxious, the registration unit can provide concise and to-the-point information. For example, if the traveler is anxious, the registration unit can provide concise and to-the-point information about airport and hotel procedures. Furthermore, if the traveler is relaxed, the registration unit can provide a detailed packing list and travel itinerary. For example, if the traveler is relaxed, the registration unit provides a detailed packing list and travel itinerary. This allows the registration unit to provide information with an appropriate level of detail depending on the traveler's emotions. Some or all of the above-described processing in the registration unit may be performed using AI, or may be performed without AI. For example, the registration unit may use technologies such as facial expression recognition and voice analysis to estimate the traveler's emotions. The registration unit can also use AI models to adjust the level of detail of the registered information based on the traveler's emotions.

[0065] During registration, the registration unit can prioritize registering highly relevant information taking into consideration the traveler's geographical location information. For example, the registration unit prioritizes registering tourist attractions and activities close to the traveler's current location. For example, the registration unit prioritizes registering information about tourist attractions and activities close to the traveler's current location. The registration unit can also provide information about transportation means and routes that are easily accessible from the traveler's current location. For example, the registration unit provides information about transportation means and routes that are easily accessible from the traveler's current location. The registration unit can also suggest an appropriate packing list based on the climate and weather of the traveler's current location. For example, the registration unit suggests an appropriate packing list based on the climate and weather of the traveler's current location. This allows the registration unit to provide appropriate information based on the traveler's current location. Some or all of the above-described processing in the registration unit may be performed using, or without, AI. For example, the registration unit can use GPS data, address information, etc. to acquire the traveler's geographical location information. The registration unit can also use an AI model to prioritize registering highly relevant information based on the traveler's geographical location information.

[0066] The registration unit can analyze the traveler's social media activity at the time of registration and register related information. For example, the registration unit registers related tourist attractions and activities based on travel destination information shared by the traveler on social media. For example, the registration unit registers information on related tourist attractions and activities based on travel destination information shared by the traveler on social media. The registration unit can also register information based on travel-related accounts followed by the traveler on social media. For example, the registration unit registers related information based on travel-related accounts followed by the traveler on social media. The registration unit can also register information on events and festivals in which the traveler expressed interest on social media. For example, the registration unit registers information on events and festivals in which the traveler expressed interest on social media. This allows the registration unit to provide appropriate information based on the traveler's social media activity. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can use natural language processing technology or a machine learning algorithm to analyze the traveler's social media activity. The registration unit can also use an AI model to register related information based on the traveler's social media activity.

[0067] The generation unit can estimate the traveler's emotions and adjust the tone of the conversation to be generated based on the estimated traveler's emotions. For example, if the traveler is relaxed, the generation unit generates conversation in a calm and friendly tone. For example, if the traveler is relaxed, the generation unit generates conversation in a calm and friendly tone. Furthermore, if the traveler is nervous, the generation unit can generate conversation in a reassuring tone. For example, if the traveler is nervous, the generation unit generates conversation in a reassuring tone. Furthermore, if the traveler is excited, the generation unit can generate conversation in a lively tone. For example, if the traveler is excited, the generation unit generates conversation in a lively tone. This allows the generation unit to generate conversation in an appropriate tone depending on the traveler's emotions. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can use technologies such as facial expression recognition and voice analysis to estimate the traveler's emotions. Furthermore, the generation unit can use an AI model to adjust the tone of the conversation to be generated based on the traveler's emotions.

[0068] During generation, the generation unit can generate appropriate conversations by referring to the traveler's past conversation history. The generation unit generates natural conversations, for example, based on phrases and expressions used by the traveler in the past. For example, the generation unit generates natural conversations based on phrases and expressions used by the traveler in the past. The generation unit can also prioritize incorporating frequently used phrases from the traveler's past conversation history. For example, the generation unit prioritizes incorporating frequently used phrases from the traveler's past conversation history. The generation unit can also analyze the traveler's past conversation patterns to generate an optimal conversation scenario. For example, the generation unit analyzes the traveler's past conversation patterns to generate an optimal conversation scenario. This allows the generation unit to generate natural conversations based on the traveler's past conversation history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can use natural language processing technology or a machine learning algorithm to analyze the traveler's past conversation history. The generation unit can also use an AI model to generate appropriate conversations based on the traveler's past conversation history.

[0069] The generation unit can customize the content of the conversation based on the traveler's current travel plan when generating the conversation. The generation unit, for example, generates a conversation about tourist spots that the traveler plans to visit. For example, the generation unit generates a conversation about tourist spots that the traveler plans to visit. The generation unit can also customize conversations at airports and hotels based on the traveler's travel plan. For example, the generation unit customizes conversations about check-in procedures at airports and check-in procedures at hotels based on the traveler's travel plan. The generation unit can also generate conversations about restaurants and shops based on the traveler's schedule. For example, the generation unit generates conversations about restaurants and shops based on the traveler's schedule. This allows the generation unit to generate appropriate conversations based on the traveler's current travel plan. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can use natural language processing technology or a machine learning algorithm to analyze the traveler's current travel plan. The generation unit can also use an AI model to customize the content of the conversation based on the traveler's current travel plan.

[0070] The generation unit can estimate the traveler's emotions and adjust the length of the conversation to be generated based on the estimated traveler's emotions. For example, if the traveler is in a hurry, the generation unit generates a short and to-the-point conversation. For example, if the traveler is in a hurry, the generation unit generates a short and to-the-point conversation. Furthermore, if the traveler is relaxed, the generation unit can generate a longer conversation including detailed explanations. For example, if the traveler is relaxed, the generation unit generates a longer conversation including detailed explanations. Furthermore, if the traveler is excited, the generation unit can generate a conversation with visually stimulating effects added. For example, if the traveler is excited, the generation unit generates a conversation with visually stimulating effects added. This allows the generation unit to generate a conversation of an appropriate length depending on the traveler's emotions. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can use technologies such as facial expression recognition and voice analysis to estimate the traveler's emotions. Furthermore, the generation unit can use an AI model to adjust the length of the conversation to be generated based on the traveler's emotions.

[0071] The generation unit can generate highly relevant conversations by taking into account the traveler's geographical location information during generation. The generation unit, for example, generates conversations about tourist attractions and activities close to the traveler's current location. For example, the generation unit generates conversations about tourist attractions and activities close to the traveler's current location. The generation unit can also generate conversations about transportation means and routes that are easily accessible from the traveler's current location. For example, the generation unit generates conversations about transportation means and routes that are easily accessible from the traveler's current location. The generation unit can also generate appropriate conversations based on the climate and weather of the traveler's current location. For example, the generation unit generates appropriate conversations based on the climate and weather of the traveler's current location. This allows the generation unit to generate appropriate conversations based on the traveler's current location. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can use GPS data, address information, etc. to acquire the traveler's geographical location information. The generation unit can also use an AI model to generate highly relevant conversations based on the traveler's geographical location information.

[0072] The generation unit can analyze the traveler's social media activity and generate related conversations during generation. The generation unit can generate related conversations based on, for example, travel destination information shared by the traveler on social media. For example, the generation unit can generate related conversations based on travel destination information shared by the traveler on social media. The generation unit can also generate conversations by referring to information on travel-related accounts followed by the traveler on social media. For example, the generation unit can generate conversations by referring to information on travel-related accounts followed by the traveler on social media. The generation unit can also generate conversations related to events or festivals in which the traveler expressed interest on social media. For example, the generation unit generates conversations related to events or festivals in which the traveler expressed interest on social media. This allows the generation unit to generate appropriate conversations based on the traveler's social media activity. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can use natural language processing technology or machine learning algorithms to analyze the traveler's social media activity. The generation unit can also use an AI model to generate related conversations based on the traveler's social media activity.

[0073] The providing unit can estimate the traveler's emotions and adjust the display method of the conversation to be provided based on the estimated traveler's emotions. For example, if the traveler is nervous, the providing unit provides a simple, highly visible display method. For example, if the traveler is nervous, the providing unit provides a simple, highly visible display method. Furthermore, if the traveler is relaxed, the providing unit can provide a display method including detailed information. For example, if the traveler is relaxed, the providing unit provides a display method including detailed information. Furthermore, if the traveler is in a hurry, the providing unit can provide a display method that focuses on the main points. For example, if the traveler is in a hurry, the providing unit provides a display method that focuses on the main points. This allows the providing unit to provide the conversation in an appropriate display method depending on the traveler's emotions. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may use technologies such as facial expression recognition and voice analysis to estimate the traveler's emotions. Furthermore, the providing unit may use an AI model to adjust the display method of the conversation to be provided based on the traveler's emotions.

[0074] The providing unit can provide optimal conversations by referring to the traveler's past practice history when providing the conversations. For example, the providing unit provides related conversations based on conversations the traveler has practiced in the past. For example, the providing unit provides related conversations based on conversations the traveler has practiced in the past. The providing unit can also prioritize providing frequently used phrases from the traveler's past practice history. For example, the providing unit can prioritize providing frequently used phrases from the traveler's past practice history. The providing unit can also analyze the traveler's past practice patterns and provide an optimal conversation scenario. For example, the providing unit can analyze the traveler's past practice patterns and provide an optimal conversation scenario. This allows the providing unit to provide appropriate conversations based on the traveler's past practice history. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can use natural language processing technology or machine learning algorithms to analyze the traveler's past practice history. The providing unit can also use an AI model to provide optimal conversations based on the traveler's past practice history.

[0075] The providing unit can customize the conversation provision method based on the traveler's current travel plan when providing the conversation. The providing unit, for example, provides a conversation about tourist spots that the traveler plans to visit. For example, the providing unit provides a conversation about tourist spots that the traveler plans to visit. The providing unit can also customize conversations at airports and hotels based on the traveler's travel plan. For example, the providing unit customizes conversations about check-in procedures at airports and check-in procedures at hotels based on the traveler's travel plan. The providing unit can also provide conversations at restaurants and shops based on the traveler's schedule. For example, the providing unit provides conversations about restaurants and shops based on the traveler's schedule. This allows the providing unit to provide appropriate conversations based on the traveler's current travel plan. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can use natural language processing technology or a machine learning algorithm to analyze the traveler's current travel plan. The providing unit can also use an AI model to customize the conversation provision method based on the traveler's current travel plan.

[0076] The providing unit can estimate the traveler's emotions and determine the priority of conversations to be provided based on the estimated traveler's emotions. For example, if the traveler is nervous, the providing unit can prioritize providing conversations that give a sense of security. For example, if the traveler is nervous, the providing unit can prioritize providing conversations that give a sense of security. Furthermore, if the traveler is relaxed, the providing unit can prioritize providing conversations that include detailed information. For example, if the traveler is relaxed, the providing unit can prioritize providing conversations that include detailed information. Furthermore, if the traveler is in a hurry, the providing unit can prioritize providing conversations that focus on the main points. For example, if the traveler is in a hurry, the providing unit can prioritize providing conversations that focus on the main points. This allows the providing unit to prioritize providing appropriate conversations according to the traveler's emotions. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can use technologies such as facial expression recognition and voice analysis to estimate the traveler's emotions. Furthermore, the providing unit can use an AI model to prioritize conversations to be provided based on the traveler's emotions.

[0077] The providing unit can provide highly relevant conversations by taking into account the traveler's geographical location information when providing the conversations. The providing unit, for example, provides conversations about tourist attractions and activities close to the traveler's current location. For example, the providing unit provides conversations about tourist attractions and activities close to the traveler's current location. The providing unit can also provide conversations about transportation means and routes that are easily accessible from the traveler's current location. For example, the providing unit provides conversations about transportation means and routes that are easily accessible from the traveler's current location. The providing unit can also provide appropriate conversations based on the climate and weather of the traveler's current location. For example, the providing unit provides appropriate conversations based on the climate and weather of the traveler's current location. This allows the providing unit to provide appropriate conversations based on the traveler's current location. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can use GPS data, address information, etc. to acquire the traveler's geographical location information. The providing unit can also use an AI model to provide highly relevant conversations based on the traveler's geographical location information.

[0078] The providing unit can analyze the traveler's social media activity and provide related conversations when providing the data. The providing unit can provide related conversations based on, for example, travel destination information shared by the traveler on social media. For example, the providing unit can provide related conversations based on travel destination information shared by the traveler on social media. The providing unit can also provide conversations based on information about travel-related accounts followed by the traveler on social media. For example, the providing unit can provide conversations based on information about travel-related accounts followed by the traveler on social media. The providing unit can also provide conversations related to events or festivals in which the traveler expressed interest on social media. For example, the providing unit can provide conversations related to events or festivals in which the traveler expressed interest on social media. This allows the providing unit to provide appropriate conversations based on the traveler's social media activity. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can use natural language processing technology or machine learning algorithms to analyze the traveler's social media activity. The providing unit can also use an AI model to provide related conversations based on the traveler's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned registration unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart device 14, and the traveler registers the travel itinerary, airline, belongings, and planned destinations. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates conversations based on the registered information. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the generated conversations to the traveler. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned registration unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart glasses 214, and the traveler registers the travel itinerary, airline, belongings, and planned destinations. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates a conversation based on the registered information. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the generated conversation to the traveler. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned registration unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the headset type terminal 314, and the traveler registers their travel itinerary, airline, belongings, and planned destinations. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates conversations based on the registered information. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the generated conversations to the traveler. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned registration unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the robot 414, and the traveler registers the travel itinerary, airline, belongings, and planned places. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically generates a conversation based on the registered information. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the generated conversation to the traveler.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The providing unit can refer to the traveler's past travel history and provide related conversations based on places the traveler has visited and activities the traveler has experienced in the past. For example, the providing unit can provide conversations about tourist spots the traveler has visited in the past. The providing unit can also provide conversations about similar activities based on activities the traveler has experienced in the past. Furthermore, the providing unit can also provide related conversations based on transportation means and accommodations the traveler has used in the past. This allows the providing unit to provide more personalized conversations based on the traveler's past travel history.

[0081] The generation unit can generate appropriate conversations taking into account the traveler's current health condition. For example, if the traveler is tired, the generation unit can generate conversations about relaxing tourist spots and activities. If the traveler is in good health, the generation unit can also generate conversations about active activities. Furthermore, if the traveler has a specific health problem, the generation unit can also generate conversations that address that problem. This allows the generation unit to provide appropriate conversations according to the traveler's health condition.

[0082] The providing unit can estimate the traveler's emotions and provide relaxing music and images to the traveler based on the estimated traveler's emotions. For example, if the traveler is nervous, the providing unit can provide relaxing music. Also, if the traveler is relaxed, the providing unit can provide images showing beautiful scenery at the travel destination. Furthermore, if the traveler is excited, the providing unit can provide energetic music and images. In this way, the providing unit can provide appropriate music and images according to the traveler's emotions.

[0083] The generation unit can generate appropriate conversations taking into account the traveler's current weather information. For example, the generation unit generates conversations about tourist spots that the traveler plans to visit on a rainy day. The generation unit can also generate conversations about activities that the traveler plans to visit on a sunny day. Furthermore, the generation unit can generate conversations about places that the traveler plans to visit in the cold season. This allows the generation unit to provide appropriate conversations based on the traveler's current weather information.

[0084] The providing unit can estimate the traveler's emotions and suggest relaxing activities to the traveler based on the estimated traveler's emotions. For example, if the traveler is tense, the providing unit can suggest relaxing spa or massage activities. Also, if the traveler is relaxed, the providing unit can suggest relaxing beach or park activities. Furthermore, if the traveler is excited, the providing unit can suggest exciting activities. In this way, the providing unit can suggest appropriate activities according to the traveler's emotions.

[0085] The generation unit can generate appropriate conversations based on the traveler's current interests and concerns. For example, the generation unit generates conversations about tourist spots that the traveler is currently interested in. The generation unit can also generate conversations related to culture and history that the traveler is interested in. Furthermore, if the traveler is interested in current trends, the generation unit can also generate conversations about the latest tourist spots and events. This allows the generation unit to provide appropriate conversations based on the traveler's current interests and concerns.

[0086] The provision unit can estimate the traveler's emotions and suggest relaxing meals and drinks to the traveler based on the estimated traveler's emotions. For example, if the traveler is nervous, the provision unit can suggest relaxing herbal tea or light meals. Also, if the traveler is relaxed, the provision unit can suggest relaxing cafes or restaurants. Furthermore, if the traveler is excited, the provision unit can suggest energizing meals and drinks. In this way, the provision unit can suggest appropriate meals and drinks according to the traveler's emotions.

[0087] The generation unit can analyze the travel history of the traveler and generate related conversations based on the places the traveler has visited and the activities the traveler has experienced in the past. For example, the generation unit generates conversations about tourist spots the traveler has visited in the past. The generation unit can also generate conversations about similar activities based on the activities the traveler has experienced in the past. Furthermore, the generation unit can generate related conversations based on the means of transportation and accommodations the traveler has used in the past. This allows the generation unit to provide more personalized conversations based on the traveler's travel history in the past.

[0088] The providing unit can estimate the traveler's emotions and provide the traveler with a relaxing environment based on the estimated traveler's emotions. For example, if the traveler is nervous, the providing unit can provide relaxing lighting and music. Also, if the traveler is relaxed, the providing unit can provide relaxing interior and decorations. Furthermore, if the traveler is excited, the providing unit can provide an energetic environment. In this way, the providing unit can provide an appropriate environment according to the traveler's emotions.

[0089] The generation unit can generate appropriate conversations taking into account the traveler's current geographical location information. For example, the generation unit generates conversations about tourist spots close to the traveler's current location. The generation unit can also generate conversations about transportation methods and routes that are easily accessible from the traveler's current location. Furthermore, the generation unit can generate appropriate conversations based on the climate and weather of the traveler's current location. This allows the generation unit to provide appropriate conversations based on the traveler's current geographical location information.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: In the registration section, the traveler registers their travel itinerary, airline, belongings, and planned destinations. Travel itinerary includes departure date, return date, and length of stay, while airline includes specific airline name and alliance. Possessions include clothing, electronic devices, travel documents, etc., and planned destinations include tourist attractions, restaurants, shopping malls, etc. Step 2: The generation unit automatically generates conversations with airport staff, cabin attendants, immigration officials, hotel staff, shop clerks, etc. based on the information registered by the registration unit. The conversations are generated using natural language processing technology and machine learning algorithms. The generation unit generates conversations based on information entered by the traveler, and can also generate conversations based on past conversation history and travel plans. The generation unit also generates different conversations each time using methods such as random generation and scenario-based generation. Step 3: The providing unit provides the traveler with the conversation generated by the generating unit. The providing unit allows the traveler to practice the conversation in advance using methods such as a simulation mode or interactive training. The providing unit also estimates the traveler's emotions and adjusts the display method of the conversation to be provided based on the estimated traveler's emotions. For example, if the traveler is nervous, a simple, highly visible display method is provided, and if the traveler is relaxed, a display method including detailed information is provided.

[0092] 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.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0094] 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.

[0095] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0110] 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.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0123] 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.

[0124] 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.

[0125] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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, in order to avoid confusion and to 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.

[0162] 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.

[0163] [Explanation of symbols]

[0164] 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 registration section where you can register your travel dates, airline, belongings, and places you plan to go; a generation unit that automatically generates conversations with airport staff, cabin attendants, immigration officials, hotel staff, and shop clerks based on the information registered by the registration unit; a providing unit that provides the conversation generated by the generating unit to the traveler. A system characterized by:

2. The generation unit Generate conversations so that they are different each time 2. The system of claim 1.

3. The generation unit Generate customized conversations based on traveler input 2. The system of claim 1.

4. The providing unit Provide generated conversations to travelers so they can practice them in advance 2. The system of claim 1.

5. The generation unit Generate conversations for multiple situations, such as checking in at the airport, checking in at a hotel, and purchasing tickets at a tourist spot 2. The system of claim 1.

6. The registration unit Estimate traveler sentiment and prioritize information to be registered based on the estimated traveler sentiment 2. The system of claim 1.

7. The registration unit Analyze travelers' past travel history and select the appropriate registration method 2. The system of claim 1.

8. The registration unit Filtering based on a traveler's current interests upon registration 2. The system of claim 1.

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