system

Abstract

The system according to this embodiment aims to provide comprehensive multilingual travel support based on the user's travel preferences. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, an assistance unit, an advice unit, and a support unit. The reception unit receives the user's travel preferences. The generation unit creates a travel plan based on the information entered by the reception unit. The assistance unit provides language assistance based on the travel plan created by the generation unit. The advice unit provides cultural information and specific advice on health and safety based on the language assistance provided by the assistance unit. The support unit provides video support based on the advice provided by the advice unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to comprehensively provide multilingual travel support based on the travel wish conditions of users.

[0005] The system according to the embodiment aims to comprehensively provide multilingual travel support based on the travel wish conditions of users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, an assistance unit, an advice unit, and a support unit. The reception unit receives the user's travel preferences. The generation unit creates a travel plan based on the information entered by the reception unit. The assistance unit provides language assistance based on the travel plan created by the generation unit. The advice unit provides cultural information and specific advice on health and safety based on the language assistance provided by the assistance unit. The support unit provides video support based on the advice provided by the advice unit. [Effects of the Invention]

[0007] The system according to this embodiment can comprehensively provide multilingual travel support based on the user's travel preferences. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 5 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The multilingual overseas travel support system according to an embodiment of the present invention is a system that creates an optimal travel plan based on the user's travel preferences and provides language assistance, cultural information, health and safety advice, and video support. This system starts with the user inputting their travel preferences (destination, budget, travel period, activities of interest, etc.). This information is analyzed by a generating AI, and an optimal travel plan is created for the user. For example, if the user inputs "I want to visit museums in Paris," the generating AI will suggest information on museums in Paris, nearby tourist attractions, and accommodations that fit the budget. Next, it provides language assistance necessary during the trip. If the user does not understand the local language, the generating AI will translate in real time to support the user in communicating smoothly. For example, it can translate menus in restaurants or provide phrases for asking for directions. Furthermore, it also provides cultural information and health and safety advice. The generating AI provides information on cultural manners and precautions in the country or region the user is visiting, as well as health information (for example, the need for vaccinations and information on local medical facilities). This allows the user to enjoy their trip with peace of mind. In addition, it enables video support in the destination country while protecting privacy. When users encounter difficulties or emergencies, the AI-generated support system provides assistance via video call. For example, if a user gets lost or encounters trouble, they can speak directly with local support staff via video call. In this way, the multilingual overseas travel support system is a service that allows users to enjoy overseas travel with peace of mind by creating travel plans tailored to the user's preferences and budget, and providing language assistance, cultural information, and health and safety advice through an interactive UI. As a result, the multilingual overseas travel support system can create optimal travel plans based on the user's travel preferences and provide language assistance, cultural information, health and safety advice, and video support.

[0029] The multilingual overseas travel support system according to this embodiment comprises a reception unit, a generation unit, an assistance unit, an advice unit, and a support unit. The reception unit receives the user's travel preferences. These preferences include, but are not limited to, destination, budget, travel duration, and activities of interest. The reception unit provides, for example, an interface for the user to input their travel preferences. The generation unit uses a generation AI to create a travel plan based on the information entered by the reception unit. The generation unit analyzes, for example, the information entered by the user, such as destination, budget, and travel duration, and proposes an optimal travel plan. The generation unit can automatically generate a travel plan based on the user's preferences using the generation AI. The assistance unit provides language assistance based on the travel plan created by the generation unit. The assistance unit provides, for example, real-time translation if the user does not understand the local language. The assistance unit can use the generation AI to support the user in communicating smoothly. The advice unit provides cultural information and specific advice on health and safety based on the language assistance provided by the assistance unit. The advice section provides, for example, cultural manners and precautions, as well as health information, for the country or region the user is visiting. The advice section can use generative AI to provide information that allows the user to enjoy their trip with peace of mind. The support section provides video support based on the advice provided by the advice section. The support section provides support via video call, for example, when the user is in trouble or in an emergency. The support section can use generative AI to enable the user to receive support locally. As a result, the multilingual overseas travel support system according to this embodiment can create an optimal travel plan based on the user's travel preferences and provide language assistance, cultural information, health and safety advice, and video support.

[0030] The reception desk can analyze the user's past travel history and suggest the optimal input method. For example, the reception desk can automatically display destinations that the user has frequently visited in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest destinations related to specific seasons or events based on the user's past travel history. This allows the user to efficiently input their travel preferences by suggesting the optimal input method based on their past travel history. Past travel history includes information such as places visited, accommodations, and modes of transportation used. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input the user's past travel history data into a generative AI and have the generative AI suggest the optimal input method.

[0031] The reception desk can perform specific filtering based on the user's current living situation and areas of interest when the user enters their travel preferences. For example, the reception desk can suggest relevant travel destinations based on activities the user has recently become interested in. It can also suggest appropriate travel destinations and activities based on the user's current health status. Furthermore, the reception desk can suggest the optimal travel plan based on the user's occupation and lifestyle. In this way, by filtering based on the user's current living situation and areas of interest, the reception desk can suggest the most suitable travel plan for the user. Current living situation includes information such as occupation, family structure, and health status. Areas of interest include information such as hobbies, activities of interest, and topics being studied. Filtering is performed, for example, by selecting plans that match the criteria and excluding unnecessary information. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception desk can input data on the user's current living situation and areas of interest into a generative AI and have the generative AI perform the filtering.

[0032] The reception desk can prioritize inputting specific and highly relevant conditions when users enter their travel preferences, taking into account their geographical location. For example, the reception desk can prioritize suggesting travel destinations close to the user's current location. It can also suggest appropriate travel destinations based on the climate of the user's current location. Furthermore, it can suggest the optimal travel destination considering the user's means of transportation from their current location. In this way, by prioritizing the input of highly relevant conditions based on the user's geographical location, the reception desk can propose the most suitable travel plan to the user. Geographical location information includes, for example, the user's current location, past visited locations, and destination. Highly relevant conditions include, for example, information on nearby tourist attractions, local events, and means of transportation. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input the user's geographical location information into a generative AI and have the generative AI prioritize the input of highly relevant conditions.

[0033] The reception desk can analyze the user's social media activity and input specific relevant conditions when the user enters their travel preferences. For example, the reception desk can suggest relevant travel plans based on destinations the user has shared on social media. It can also suggest optimal travel destinations based on the user's interests on social media. Furthermore, the reception desk can suggest travel plans by referring to the travel destinations of the user's friends on social media. In this way, by inputting relevant conditions based on the user's social media activity, the reception desk can suggest the most suitable travel plan for the user. Social media activity includes information such as the content of posts, the history of likes, and the accounts followed. Relevant conditions include information such as places of interest, recommended activities, and popular tourist spots. Some or all of the above processing in the reception desk may be performed using a generative AI, or not. For example, the reception desk can input the user's social media activity data into a generative AI and have the generative AI input the relevant conditions.

[0034] The generation unit can suggest appropriate travel plans by referring to the user's past travel history when creating a travel plan. For example, the generation unit can suggest relevant travel plans based on places the user has visited in the past. The generation unit can also suggest plans that avoid crowds based on the user's past travel history. Furthermore, the generation unit can analyze the user's past travel history and suggest the most efficient plan. In this way, by suggesting the optimal plan based on the user's past travel history, it can provide the user with the best possible travel plan. Past travel history includes information such as places visited, accommodations, and means of transportation used. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's past travel history data into a generation AI and have the generation AI suggest the optimal plan.

[0035] The generation unit can customize specific travel plans based on the user's current living situation when creating a travel plan. For example, the generation unit can suggest an appropriate travel plan based on the user's current health condition. It can also suggest an optimal travel plan based on the user's occupation and lifestyle. Furthermore, it can suggest a customized travel plan based on the user's current interests. This allows the system to provide the user with the most suitable travel plan by customizing it based on the user's current living situation. Current living situation includes information such as occupation, family structure, and health status. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's current living situation data into a generation AI and have the generation AI perform the plan customization.

[0036] The generation unit can propose an appropriate travel plan when creating one, taking into account the user's geographical location. For example, the generation unit can prioritize suggesting travel destinations close to the user's current location. It can also suggest appropriate travel destinations based on the climate of the user's current location. Furthermore, the generation unit can propose the optimal travel plan considering the means of transportation from the user's current location. In this way, by proposing the optimal plan based on the user's geographical location, it can provide the user with the best possible travel plan. Geographical location information includes, for example, the current location, previously visited places, and destinations. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI propose the optimal plan.

[0037] The generation unit can analyze the user's social media activity and customize specific travel plans when creating them. For example, the generation unit can suggest relevant travel plans based on destinations shared by the user on social media. It can also suggest optimal travel plans based on the user's interests on social media. Furthermore, the generation unit can suggest travel plans by referencing the travel destinations of the user's friends on social media. In this way, by customizing plans based on the user's social media activity, it can provide the user with the most suitable travel plan. Social media activity includes information such as the content of posts, the history of likes, and the accounts followed. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the plan customization.

[0038] The assistance unit can suggest appropriate assistance methods by referring to the user's past language usage history during language assistance. For example, the assistance unit can suggest the optimal translation method based on the languages ​​the user has used in the past. The assistance unit can also suggest ways to avoid congestion based on the user's past language usage history. Furthermore, the assistance unit can analyze the user's past language usage history and suggest the most efficient assistance method. This allows the assistance unit to provide the best possible language assistance to the user by suggesting the optimal assistance method based on the user's past language usage history. Past language usage history includes information such as the languages ​​used, frequency, and content of past assistance. Some or all of the above processing in the assistance unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the assistance unit can input the user's past language usage history data into a generative AI and have the generative AI suggest the optimal assistance method.

[0039] The assistance unit can customize specific assistance methods based on the user's current living situation during language assistance. For example, the assistance unit can suggest appropriate language assistance based on the user's current health status. It can also suggest optimal language assistance based on the user's occupation and lifestyle. Furthermore, it can suggest customized language assistance based on the user's current interests. This allows the assistance unit to provide the most suitable language assistance for the user by customizing the assistance method based on the user's current living situation. Current living situation includes information such as occupation, family structure, and health status. Some or all of the above processing in the assistance unit may be performed using a generative AI, or not. For example, the assistance unit can input the user's current living situation data into a generative AI and have the generative AI perform the customization of the assistance method.

[0040] The assistance unit can propose appropriate assistance methods when providing language assistance, taking into account the user's geographical location. For example, the assistance unit can propose the optimal translation method based on the language of the user's current location. It can also propose appropriate language assistance based on the culture of the user's current location. Furthermore, the assistance unit can propose the optimal language assistance considering the user's means of transportation from their current location. This allows the assistance unit to provide the best possible language assistance to the user by proposing the optimal assistance method based on the user's geographical location. Geographical location information includes, for example, the user's current location, past visited locations, and destinations. Some or all of the above processing in the assistance unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the assistance unit can input the user's geographical location information into a generative AI and have the generative AI propose the optimal assistance method.

[0041] The assistance unit can analyze a user's social media activity during language assistance to customize specific assistance methods. For example, the assistance unit can suggest relevant language assistance based on the language the user has shared on social media. It can also suggest optimal language assistance based on the user's interests on social media. Furthermore, the assistance unit can suggest language assistance by referring to the language usage of the user's friends on social media. This allows the assistance unit to provide the most suitable language assistance to the user by customizing the assistance method based on the user's social media activity. Social media activity includes information such as the content of posts, the history of likes, and the accounts followed. Some or all of the above processing in the assistance unit may be performed using generative AI, or not. For example, the assistance unit can input the user's social media activity data into a generative AI and have the generative AI perform the customization of the assistance method.

[0042] The advice unit can suggest appropriate advice by referring to the user's past travel history when providing advice. For example, the advice unit can provide relevant advice based on places the user has visited in the past. The advice unit can also provide advice to avoid crowds based on the user's past travel history. Furthermore, the advice unit can analyze the user's past travel history and provide the most efficient advice. In this way, by suggesting the best advice based on the user's past travel history, it can provide the best advice for the user. Past travel history includes information such as places visited, accommodations, and means of transportation used. Some or all of the above processing in the advice unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the advice unit can input the user's past travel history data into a generative AI and have the generative AI suggest the best advice.

[0043] The advice unit can customize specific advice based on the user's current living situation when providing advice. For example, the advice unit can provide appropriate advice based on the user's current health condition. The advice unit can also provide optimal advice based on the user's occupation and lifestyle. Furthermore, the advice unit can provide customized advice based on the user's current interests. This allows the advice unit to provide the most suitable advice for the user by customizing it based on the user's current living situation. Current living situation includes information such as occupation, family structure, and health status. Some or all of the above processing in the advice unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the advice unit can input the user's current living situation data into a generative AI and have the generative AI perform the customization of the advice.

[0044] The advice unit can propose appropriate advice by considering the user's geographical location information when providing advice. For example, the advice unit can provide appropriate advice based on the culture of the user's current location. It can also provide appropriate advice based on the climate of the user's current location. Furthermore, the advice unit can provide optimal advice by considering the means of transportation from the user's current location. In this way, by proposing optimal advice based on the user's geographical location information, the advice unit can provide the best possible advice for the user. Geographical location information includes, for example, information such as the current location, past visited locations, and destinations. Some or all of the above processing in the advice unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the advice unit can input the user's geographical location information into a generative AI and have the generative AI propose optimal advice.

[0045] The advice unit can analyze the user's social media activity and customize specific advice when providing it. For example, the advice unit can provide relevant advice based on information the user has shared on social media. It can also provide optimal advice based on the user's interests on social media. Furthermore, the advice unit can provide advice by referring to the activities of the user's friends on social media. In this way, by customizing advice based on the user's social media activity, it can provide the most suitable advice for the user. Social media activity includes information such as the content of posts, the history of likes, and the accounts followed. Some or all of the above processing in the advice unit may be performed using a generative AI, or not. For example, the advice unit can input the user's social media activity data into a generative AI and have the generative AI perform the customization of the advice.

[0046] The support department can suggest appropriate support methods by referring to the user's past support history when providing video support. For example, the support department can provide relevant support based on the support the user has received in the past. The support department can also suggest ways to avoid congestion based on the user's past support history. Furthermore, the support department can analyze the user's past support history and suggest the most efficient support method. This allows the support department to provide the best possible support to the user by suggesting the optimal support method based on the user's past support history. Past support history includes information such as the content of support received in the past, the frequency of support, and the results of the support. Some or all of the above processing in the support department may be performed using or without a generative AI. For example, the support department can input the user's past support history data into a generative AI and have the generative AI suggest the optimal support method.

[0047] The support unit can customize specific support methods based on the user's current living situation when providing video support. For example, the support unit can provide appropriate support based on the user's current health condition. It can also provide optimal support based on the user's occupation and lifestyle. Furthermore, the support unit can provide customized support based on the user's current interests. This allows for the provision of optimal support by customizing support methods based on the user's current living situation. Current living situation includes information such as occupation, family structure, and health status. Some or all of the above processing in the support unit may be performed using or without a generative AI. For example, the support unit can input the user's current living situation data into a generative AI and have the generative AI perform the customization of support methods.

[0048] The support unit can propose appropriate support methods when providing video support, taking into account the user's geographical location. For example, the support unit can provide appropriate support based on the culture of the user's current location. It can also provide appropriate support based on the climate of the user's current location. Furthermore, the support unit can provide optimal support by considering the user's means of transportation from their current location. This allows the support unit to provide the best possible support to the user by proposing the optimal support method based on the user's geographical location. Geographical location information includes, for example, the user's current location, past visited locations, and destinations. Some or all of the above processing in the support unit may be performed using or without a generative AI. For example, the support unit can input the user's geographical location information into a generative AI and have the generative AI propose the optimal support method.

[0049] The support department can analyze the user's social media activity when providing video support and customize specific support methods. For example, the support department can provide relevant support based on information the user has shared on social media. It can also provide optimal support based on the user's interests on social media. Furthermore, the support department can provide support by referring to the activities of the user's friends on social media. In this way, by customizing support methods based on the user's social media activity, the support department can provide the best possible support for the user. Social media activity includes information such as the content of posts, the history of likes, and the accounts followed. Some or all of the above processing in the support department may be performed using or without a generative AI. For example, the support department can input the user's social media activity data into a generative AI and have the generative AI perform the customization of support methods.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The generation unit can customize travel plans by considering the user's past reviews and ratings when analyzing the user's travel preferences. For example, it can prioritize suggesting accommodations and restaurants that the user has given high ratings to in the past. It can also adjust the plan to avoid places that the user has given low ratings to in the past. Furthermore, it can extract specific preferences and dislikes from the user's reviews and optimize the travel plan based on that. This allows the system to provide more satisfying travel plans based on the user's past ratings.

[0052] The support department can conduct simulations in advance of potential emergencies that users may encounter during their trip and propose appropriate responses. For example, they can provide guidance on what to do if a user gets lost and emergency contact information. They can also provide proactive advice on how to handle health problems that users may face. Furthermore, they can offer advice to help users avoid cultural misunderstandings. This allows users to prepare for emergencies in advance, ensuring they can travel with peace of mind.

[0053] The advice section can collect user behavior data in real time during their trip and adjust the travel plan accordingly. For example, if a user visits a tourist destination earlier than planned, it can suggest the next destination sooner. It can also suggest additional activities related to a particular location if the user spends an extended period there. Furthermore, it can dynamically adjust the entire travel plan based on the user's travel speed and length of stay. This allows for flexible adjustment of the travel plan based on the user's actual behavior, providing a more comfortable travel experience.

[0054] The support team can monitor the battery level of the devices the user is using during their trip and suggest battery-saving modes as needed. For example, if the user's smartphone battery is low, it can be set to display only important notifications. It can also provide battery-efficient route guidance. Furthermore, it can suggest nearby charging spots and places to buy portable power banks. This allows users to enjoy their trip with peace of mind, without worrying about their devices running out of battery.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The reception desk enters the user's travel preferences. These preferences include destination, budget, travel duration, and activities of interest. The reception desk provides an interface for the user to enter their travel preferences. Step 2: The generation unit creates a travel plan based on the information entered by the reception unit. The generation unit uses generation AI to analyze information such as destination, budget, and travel period entered by the user and proposes the optimal travel plan. Step 3: The assistance department provides language assistance based on the travel plan created by the generation department. The assistance department provides real-time translation if the user does not understand the local language. It uses generation AI to support the user in communicating smoothly. Step 4: The Advice Department provides specific advice on cultural information and health and safety based on the language assistance provided by the Assistance Department. The Advice Department provides information on cultural manners and precautions, as well as health information, for the country or region the user is visiting. Step 5: The support department provides video support based on the advice provided by the advice department. The support department provides support via video call when the user is having trouble or in an emergency.

[0057] (Example of form 2) The multilingual overseas travel support system according to an embodiment of the present invention is a system that creates an optimal travel plan based on the user's travel preferences and provides language assistance, cultural information, health and safety advice, and video support. This system starts with the user inputting their travel preferences (destination, budget, travel period, activities of interest, etc.). This information is analyzed by a generating AI, and an optimal travel plan is created for the user. For example, if the user inputs "I want to visit museums in Paris," the generating AI will suggest information on museums in Paris, nearby tourist attractions, and accommodations that fit the budget. Next, it provides language assistance necessary during the trip. If the user does not understand the local language, the generating AI will translate in real time to support the user in communicating smoothly. For example, it can translate menus in restaurants or provide phrases for asking for directions. Furthermore, it also provides cultural information and health and safety advice. The generating AI provides information on cultural manners and precautions in the country or region the user is visiting, as well as health information (for example, the need for vaccinations and information on local medical facilities). This allows the user to enjoy their trip with peace of mind. In addition, it enables video support in the destination country while protecting privacy. When users encounter difficulties or emergencies, the AI-generated support system provides assistance via video call. For example, if a user gets lost or encounters trouble, they can speak directly with local support staff via video call. In this way, the multilingual overseas travel support system is a service that allows users to enjoy overseas travel with peace of mind by creating travel plans tailored to the user's preferences and budget, and providing language assistance, cultural information, and health and safety advice through an interactive UI. As a result, the multilingual overseas travel support system can create optimal travel plans based on the user's travel preferences and provide language assistance, cultural information, health and safety advice, and video support.

[0058] The multilingual overseas travel support system according to this embodiment comprises a reception unit, a generation unit, an assistance unit, an advice unit, and a support unit. The reception unit receives the user's travel preferences. These preferences include, but are not limited to, destination, budget, travel duration, and activities of interest. The reception unit provides, for example, an interface for the user to input their travel preferences. The generation unit uses a generation AI to create a travel plan based on the information entered by the reception unit. The generation unit analyzes, for example, the information entered by the user, such as destination, budget, and travel duration, and proposes an optimal travel plan. The generation unit can automatically generate a travel plan based on the user's preferences using the generation AI. The assistance unit provides language assistance based on the travel plan created by the generation unit. The assistance unit provides, for example, real-time translation if the user does not understand the local language. The assistance unit can use the generation AI to support the user in communicating smoothly. The advice unit provides cultural information and specific advice on health and safety based on the language assistance provided by the assistance unit. The advice section provides, for example, cultural manners and precautions, as well as health information, for the country or region the user is visiting. The advice section can use generative AI to provide information that allows the user to enjoy their trip with peace of mind. The support section provides video support based on the advice provided by the advice section. The support section provides support via video call, for example, when the user is in trouble or in an emergency. The support section can use generative AI to enable the user to receive support locally. As a result, the multilingual overseas travel support system according to this embodiment can create an optimal travel plan based on the user's travel preferences and provide language assistance, cultural information, health and safety advice, and video support.

[0059] The reception desk can estimate the user's emotions and adjust the input method for travel preferences based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of travel preferences. This allows users to input their travel preferences without stress by adjusting the input method according to their emotions. Emotion estimation is performed using technologies such as facial recognition, speech analysis, and text analysis. Some or all of the above processing in the reception desk may be performed using generative AI, or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0060] The reception desk can analyze the user's past travel history and suggest the optimal input method. For example, the reception desk can automatically display destinations that the user has frequently visited in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest destinations related to specific seasons or events based on the user's past travel history. This allows the user to efficiently input their travel preferences by suggesting the optimal input method based on their past travel history. Past travel history includes information such as places visited, accommodations, and modes of transportation used. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input the user's past travel history data into a generative AI and have the generative AI suggest the optimal input method.

[0061] The reception desk can perform specific filtering based on the user's current living situation and areas of interest when the user enters their travel preferences. For example, the reception desk can suggest relevant travel destinations based on activities the user has recently become interested in. It can also suggest appropriate travel destinations and activities based on the user's current health status. Furthermore, the reception desk can suggest the optimal travel plan based on the user's occupation and lifestyle. In this way, by filtering based on the user's current living situation and areas of interest, the reception desk can suggest the most suitable travel plan for the user. Current living situation includes information such as occupation, family structure, and health status. Areas of interest include information such as hobbies, activities of interest, and topics being studied. Filtering is performed, for example, by selecting plans that match the criteria and excluding unnecessary information. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception desk can input data on the user's current living situation and areas of interest into a generative AI and have the generative AI perform the filtering.

[0062] The reception desk can estimate the user's emotions and, based on those emotions, prioritize the travel preferences to be entered. For example, if the user is relaxed, the reception desk may prioritize detailed travel plans. If the user is in a hurry, the reception desk may prioritize only the most important conditions. Furthermore, if the user is excited, the reception desk may prioritize conditions that include entertainment elements. This allows the user to prioritize important conditions by determining the priority of travel preferences according to their emotions. Emotion estimation is performed using technologies such as facial recognition, speech analysis, and text analysis. Some or all of the above processing in the reception desk may be performed using generative AI, or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0063] The reception desk can prioritize inputting specific and highly relevant conditions when users enter their travel preferences, taking into account their geographical location. For example, the reception desk can prioritize suggesting travel destinations close to the user's current location. It can also suggest appropriate travel destinations based on the climate of the user's current location. Furthermore, it can suggest the optimal travel destination considering the user's means of transportation from their current location. In this way, by prioritizing the input of highly relevant conditions based on the user's geographical location, the reception desk can propose the most suitable travel plan to the user. Geographical location information includes, for example, the user's current location, past visited locations, and destination. Highly relevant conditions include, for example, information on nearby tourist attractions, local events, and means of transportation. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input the user's geographical location information into a generative AI and have the generative AI prioritize the input of highly relevant conditions.

[0064] The reception desk can analyze the user's social media activity and input specific relevant conditions when the user enters their travel preferences. For example, the reception desk can suggest relevant travel plans based on destinations the user has shared on social media. It can also suggest optimal travel destinations based on the user's interests on social media. Furthermore, the reception desk can suggest travel plans by referring to the travel destinations of the user's friends on social media. In this way, by inputting relevant conditions based on the user's social media activity, the reception desk can suggest the most suitable travel plan for the user. Social media activity includes information such as the content of posts, the history of likes, and the accounts followed. Relevant conditions include information such as places of interest, recommended activities, and popular tourist spots. Some or all of the above processing in the reception desk may be performed using a generative AI, or not. For example, the reception desk can input the user's social media activity data into a generative AI and have the generative AI input the relevant conditions.

[0065] The generation unit can estimate the user's emotions and adjust how the travel plan is presented based on those emotions. For example, if the user is relaxed, the generation unit can generate a travel plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate a travel plan that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a travel plan with visually stimulating effects. By adjusting how the travel plan is presented according to the user's emotions, the system can provide the user with the most suitable travel plan. Emotion estimation is performed using technologies such as facial recognition, speech analysis, and text analysis. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the generation unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0066] The generation unit can suggest appropriate travel plans by referring to the user's past travel history when creating a travel plan. For example, the generation unit can suggest relevant travel plans based on places the user has visited in the past. The generation unit can also suggest plans that avoid crowds based on the user's past travel history. Furthermore, the generation unit can analyze the user's past travel history and suggest the most efficient plan. In this way, by suggesting the optimal plan based on the user's past travel history, it can provide the user with the best possible travel plan. Past travel history includes information such as places visited, accommodations, and means of transportation used. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's past travel history data into a generation AI and have the generation AI suggest the optimal plan.

[0067] The generation unit can customize specific travel plans based on the user's current living situation when creating a travel plan. For example, the generation unit can suggest an appropriate travel plan based on the user's current health condition. It can also suggest an optimal travel plan based on the user's occupation and lifestyle. Furthermore, it can suggest a customized travel plan based on the user's current interests. This allows the system to provide the user with the most suitable travel plan by customizing it based on the user's current living situation. Current living situation includes information such as occupation, family structure, and health status. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's current living situation data into a generation AI and have the generation AI perform the plan customization.

[0068] The generation unit can estimate the user's emotions and prioritize travel plans based on those emotions. For example, if the user is relaxed, the generation unit will prioritize creating a detailed travel plan. If the user is in a hurry, the generation unit can also prioritize creating only the essential elements. Furthermore, if the user is excited, the generation unit can prioritize creating a plan that includes entertainment elements. This allows the system to provide the user with the most suitable travel plan by prioritizing it according to their emotions. Emotion estimation is performed using technologies such as facial recognition, speech analysis, and text analysis. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the generation unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0069] The generation unit can propose an appropriate travel plan when creating one, taking into account the user's geographical location. For example, the generation unit can prioritize suggesting travel destinations close to the user's current location. It can also suggest appropriate travel destinations based on the climate of the user's current location. Furthermore, the generation unit can propose the optimal travel plan considering the means of transportation from the user's current location. In this way, by proposing the optimal plan based on the user's geographical location, it can provide the user with the best possible travel plan. Geographical location information includes, for example, the current location, previously visited places, and destinations. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI propose the optimal plan.

[0070] The generation unit can analyze the user's social media activity and customize specific travel plans when creating them. For example, the generation unit can suggest relevant travel plans based on destinations shared by the user on social media. It can also suggest optimal travel plans based on the user's interests on social media. Furthermore, the generation unit can suggest travel plans by referencing the travel destinations of the user's friends on social media. In this way, by customizing plans based on the user's social media activity, it can provide the user with the most suitable travel plan. Social media activity includes information such as the content of posts, the history of likes, and the accounts followed. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the plan customization.

[0071] The assistance unit can estimate the user's emotions and adjust the method of verbal assistance based on the estimated emotions. For example, if the user is nervous, the assistance unit can provide a simple and highly visible display method. If the user is relaxed, the assistance unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the assistance unit can provide a display method that gets straight to the point. In this way, by adjusting the method of verbal assistance according to the user's emotions, the assistance unit can provide the most suitable verbal assistance for the user. Emotion estimation is performed using technologies such as facial recognition, speech analysis, and text analysis. Some or all of the above processing in the assistance unit may be performed using generative AI, or it may be performed without generative AI. For example, the assistance unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0072] The assistance unit can suggest appropriate assistance methods by referring to the user's past language usage history during language assistance. For example, the assistance unit can suggest the optimal translation method based on the languages ​​the user has used in the past. The assistance unit can also suggest ways to avoid congestion based on the user's past language usage history. Furthermore, the assistance unit can analyze the user's past language usage history and suggest the most efficient assistance method. This allows the assistance unit to provide the best possible language assistance to the user by suggesting the optimal assistance method based on the user's past language usage history. Past language usage history includes information such as the languages ​​used, frequency, and content of past assistance. Some or all of the above processing in the assistance unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the assistance unit can input the user's past language usage history data into a generative AI and have the generative AI suggest the optimal assistance method.

[0073] The assistance unit can customize specific assistance methods based on the user's current living situation during language assistance. For example, the assistance unit can suggest appropriate language assistance based on the user's current health status. It can also suggest optimal language assistance based on the user's occupation and lifestyle. Furthermore, it can suggest customized language assistance based on the user's current interests. This allows the assistance unit to provide the most suitable language assistance for the user by customizing the assistance method based on the user's current living situation. Current living situation includes information such as occupation, family structure, and health status. Some or all of the above processing in the assistance unit may be performed using a generative AI, or not. For example, the assistance unit can input the user's current living situation data into a generative AI and have the generative AI perform the customization of the assistance method.

[0074] The assistance unit can estimate the user's emotions and prioritize verbal assistance based on the estimated emotions. For example, if the user is relaxed, the assistance unit may prioritize providing detailed verbal assistance. If the user is in a hurry, the assistance unit may also prioritize providing only the most important phrases. Furthermore, if the user is excited, the assistance unit may prioritize providing verbal assistance that includes entertainment elements. This allows the assistance unit to provide the most appropriate verbal assistance for the user by prioritizing verbal assistance according to the user's emotions. Emotion estimation is performed using technologies such as facial recognition, speech analysis, and text analysis. Some or all of the above processing in the assistance unit may be performed using generative AI, or not. For example, the assistance unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0075] The assistance unit can propose appropriate assistance methods when providing language assistance, taking into account the user's geographical location. For example, the assistance unit can propose the optimal translation method based on the language of the user's current location. It can also propose appropriate language assistance based on the culture of the user's current location. Furthermore, the assistance unit can propose the optimal language assistance considering the user's means of transportation from their current location. This allows the assistance unit to provide the best possible language assistance to the user by proposing the optimal assistance method based on the user's geographical location. Geographical location information includes, for example, the user's current location, past visited locations, and destinations. Some or all of the above processing in the assistance unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the assistance unit can input the user's geographical location information into a generative AI and have the generative AI propose the optimal assistance method.

[0076] The assistance unit can analyze a user's social media activity during language assistance to customize specific assistance methods. For example, the assistance unit can suggest relevant language assistance based on the language the user has shared on social media. It can also suggest optimal language assistance based on the user's interests on social media. Furthermore, the assistance unit can suggest language assistance by referring to the language usage of the user's friends on social media. This allows the assistance unit to provide the most suitable language assistance to the user by customizing the assistance method based on the user's social media activity. Social media activity includes information such as the content of posts, the history of likes, and the accounts followed. Some or all of the above processing in the assistance unit may be performed using generative AI, or not. For example, the assistance unit can input the user's social media activity data into a generative AI and have the generative AI perform the customization of the assistance method.

[0077] The advice unit can estimate the user's emotions and adjust the way it presents advice based on those emotions. For example, if the user is relaxed, the advice unit can provide detailed advice. If the user is in a hurry, it can provide concise advice. Furthermore, if the user is excited, the advice unit can provide advice with visually stimulating effects. By adjusting the way advice is presented according to the user's emotions, the system can provide the most appropriate advice for the user. Emotion estimation is performed using technologies such as facial recognition, speech analysis, and text analysis. Some or all of the above-described processes in the advice unit may be performed using generative AI, or they may not be performed using generative AI. For example, the advice unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0078] The advice unit can suggest appropriate advice by referring to the user's past travel history when providing advice. For example, the advice unit can provide relevant advice based on places the user has visited in the past. The advice unit can also provide advice to avoid crowds based on the user's past travel history. Furthermore, the advice unit can analyze the user's past travel history and provide the most efficient advice. In this way, by suggesting the best advice based on the user's past travel history, it can provide the best advice for the user. Past travel history includes information such as places visited, accommodations, and means of transportation used. Some or all of the above processing in the advice unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the advice unit can input the user's past travel history data into a generative AI and have the generative AI suggest the best advice.

[0079] The advice unit can customize specific advice based on the user's current living situation when providing advice. For example, the advice unit can provide appropriate advice based on the user's current health condition. The advice unit can also provide optimal advice based on the user's occupation and lifestyle. Furthermore, the advice unit can provide customized advice based on the user's current interests. This allows the advice unit to provide the most suitable advice for the user by customizing it based on the user's current living situation. Current living situation includes information such as occupation, family structure, and health status. Some or all of the above processing in the advice unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the advice unit can input the user's current living situation data into a generative AI and have the generative AI perform the customization of the advice.

[0080] The advice unit can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is relaxed, the advice unit may prioritize providing detailed advice. If the user is in a hurry, the advice unit may prioritize providing only the most important elements. Furthermore, if the user is excited, the advice unit may prioritize providing advice that includes entertainment elements. By prioritizing advice according to the user's emotions, the system can provide the user with the most appropriate advice. Emotion estimation is performed using technologies such as facial recognition, speech analysis, and text analysis. Some or all of the above-described processes in the advice unit may be performed using generative AI, or they may not be performed using generative AI. For example, the advice unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0081] The advice unit can propose appropriate advice by considering the user's geographical location information when providing advice. For example, the advice unit can provide appropriate advice based on the culture of the user's current location. It can also provide appropriate advice based on the climate of the user's current location. Furthermore, the advice unit can provide optimal advice by considering the means of transportation from the user's current location. In this way, by proposing optimal advice based on the user's geographical location information, the advice unit can provide the best possible advice for the user. Geographical location information includes, for example, information such as the current location, past visited locations, and destinations. Some or all of the above processing in the advice unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the advice unit can input the user's geographical location information into a generative AI and have the generative AI propose optimal advice.

[0082] The advice unit can analyze the user's social media activity and customize specific advice when providing it. For example, the advice unit can provide relevant advice based on information the user has shared on social media. It can also provide optimal advice based on the user's interests on social media. Furthermore, the advice unit can provide advice by referring to the activities of the user's friends on social media. In this way, by customizing advice based on the user's social media activity, it can provide the most suitable advice for the user. Social media activity includes information such as the content of posts, the history of likes, and the accounts followed. Some or all of the above processing in the advice unit may be performed using a generative AI, or not. For example, the advice unit can input the user's social media activity data into a generative AI and have the generative AI perform the customization of the advice.

[0083] The support unit can estimate the user's emotions and adjust the video support method based on the estimated emotions. For example, if the user is nervous, the support unit can provide support in a calm voice. If the user is relaxed, the support unit can provide support in a cheerful voice. Furthermore, if the user is in a hurry, the support unit can provide quick and concise support. In this way, by adjusting the video support method according to the user's emotions, the support unit can provide the best possible support for the user. Emotion estimation is performed using technologies such as facial recognition, speech analysis, and text analysis. Some or all of the above processing in the support unit may be performed using generative AI, or it may be performed without generative AI. For example, the support unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0084] The support department can suggest appropriate support methods by referring to the user's past support history when providing video support. For example, the support department can provide relevant support based on the support the user has received in the past. The support department can also suggest ways to avoid congestion based on the user's past support history. Furthermore, the support department can analyze the user's past support history and suggest the most efficient support method. This allows the support department to provide the best possible support to the user by suggesting the optimal support method based on the user's past support history. Past support history includes information such as the content of support received in the past, the frequency of support, and the results of the support. Some or all of the above processing in the support department may be performed using or without a generative AI. For example, the support department can input the user's past support history data into a generative AI and have the generative AI suggest the optimal support method.

[0085] The support unit can customize specific support methods based on the user's current living situation when providing video support. For example, the support unit can provide appropriate support based on the user's current health condition. It can also provide optimal support based on the user's occupation and lifestyle. Furthermore, the support unit can provide customized support based on the user's current interests. This allows for the provision of optimal support by customizing support methods based on the user's current living situation. Current living situation includes information such as occupation, family structure, and health status. Some or all of the above processing in the support unit may be performed using or without a generative AI. For example, the support unit can input the user's current living situation data into a generative AI and have the generative AI perform the customization of support methods.

[0086] The support unit can estimate the user's emotions and prioritize video support based on those emotions. For example, if the user is relaxed, the support unit may prioritize detailed support. If the user is in a hurry, the support unit may prioritize providing only the essential elements. Furthermore, if the user is excited, the support unit may prioritize support that includes entertainment elements. This allows the support unit to provide optimal support to the user by prioritizing video support according to their emotions. Emotion estimation is performed using technologies such as facial recognition, speech analysis, and text analysis. Some or all of the above processing in the support unit may be performed using generative AI, or it may be performed without generative AI. For example, the support unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0087] The support unit can propose appropriate support methods when providing video support, taking into account the user's geographical location. For example, the support unit can provide appropriate support based on the culture of the user's current location. It can also provide appropriate support based on the climate of the user's current location. Furthermore, the support unit can provide optimal support by considering the user's means of transportation from their current location. This allows the support unit to provide the best possible support to the user by proposing the optimal support method based on the user's geographical location. Geographical location information includes, for example, the user's current location, past visited locations, and destinations. Some or all of the above processing in the support unit may be performed using or without a generative AI. For example, the support unit can input the user's geographical location information into a generative AI and have the generative AI propose the optimal support method.

[0088] The support department can analyze the user's social media activity when providing video support and customize specific support methods. For example, the support department can provide relevant support based on information the user has shared on social media. It can also provide optimal support based on the user's interests on social media. Furthermore, the support department can provide support by referring to the activities of the user's friends on social media. In this way, by customizing support methods based on the user's social media activity, the support department can provide the best possible support for the user. Social media activity includes information such as the content of posts, the history of likes, and the accounts followed. Some or all of the above processing in the support department may be performed using or without a generative AI. For example, the support department can input the user's social media activity data into a generative AI and have the generative AI perform the customization of support methods. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, generation unit, assistance unit, advice unit, and support unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to input their desired travel conditions. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and creates a travel plan using generation AI. The assistance unit is implemented, for example, by the control unit 46A of the smart device 14 and performs real-time translation. The advice unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides cultural information and advice on health and safety. The support unit is implemented, for example, by the control unit 46A of the smart device 14 and provides support through video calls. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, generation unit, assistance unit, advice unit, and support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for the user to input their travel preferences. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and creates a travel plan using generation AI. The assistance unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides real-time translation. The advice unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides cultural information and advice on health and safety. The support unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides support through video calls. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, generation unit, assistance unit, advice unit, and support unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for the user to input their desired travel conditions. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and creates a travel plan using generation AI. The assistance unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides real-time translation. The advice unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides cultural information and advice on health and safety. The support unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides support through video calls. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, generation unit, assistance unit, advice unit, and support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for the user to input their travel preferences. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates a travel plan using generation AI. The assistance unit is implemented by, for example, the control unit 46A of the robot 414 and performs real-time translation. The advice unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides cultural information and advice on health and safety. The support unit is implemented by, for example, the control unit 46A of the robot 414 and provides support through video calls.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] The reception desk can dynamically change the interface design based on the user's current mood when they enter their travel preferences. For example, if the user is relaxed, a colorful and visually appealing interface can be provided. If the user is stressed, a simple and calming interface can be offered. Furthermore, if the user is in a hurry, the interface can be optimized to allow them to complete the input with minimal clicks. In this way, by adjusting the interface according to the user's emotions, it is possible to ensure that users can comfortably enter their travel preferences.

[0091] The generation unit can customize travel plans by considering the user's past reviews and ratings when analyzing the user's travel preferences. For example, it can prioritize suggesting accommodations and restaurants that the user has given high ratings to in the past. It can also adjust the plan to avoid places that the user has given low ratings to in the past. Furthermore, it can extract specific preferences and dislikes from the user's reviews and optimize the travel plan based on that. This allows the system to provide more satisfying travel plans based on the user's past ratings.

[0092] The support department can conduct simulations in advance of potential emergencies that users may encounter during their trip and propose appropriate responses. For example, they can provide guidance on what to do if a user gets lost and emergency contact information. They can also provide proactive advice on how to handle health problems that users may face. Furthermore, they can offer advice to help users avoid cultural misunderstandings. This allows users to prepare for emergencies in advance, ensuring they can travel with peace of mind.

[0093] The advice section can collect user behavior data in real time during their trip and adjust the travel plan accordingly. For example, if a user visits a tourist destination earlier than planned, it can suggest the next destination sooner. It can also suggest additional activities related to a particular location if the user spends an extended period there. Furthermore, it can dynamically adjust the entire travel plan based on the user's travel speed and length of stay. This allows for flexible adjustment of the travel plan based on the user's actual behavior, providing a more comfortable travel experience.

[0094] The support team can monitor the battery level of the devices the user is using during their trip and suggest battery-saving modes as needed. For example, if the user's smartphone battery is low, it can be set to display only important notifications. It can also provide battery-efficient route guidance. Furthermore, it can suggest nearby charging spots and places to buy portable power banks. This allows users to enjoy their trip with peace of mind, without worrying about their devices running out of battery.

[0095] The reception desk can estimate the user's emotions and adjust the input method for travel preferences based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of travel preferences. This allows users to input their travel preferences without stress by adjusting the input method according to their emotions. Emotion estimation is performed using technologies such as facial recognition, speech analysis, and text analysis. Some or all of the above processing in the reception desk may be performed using generative AI, or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0096] The generation unit can estimate the user's emotions and adjust how the travel plan is presented based on those emotions. For example, if the user is relaxed, it can generate a travel plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate a travel plan that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a travel plan with visually stimulating effects. By adjusting how the travel plan is presented according to the user's emotions, the system can provide the user with the most suitable travel plan. Emotion estimation is performed using technologies such as facial recognition, speech analysis, and text analysis. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the generation unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0097] The assistance unit can estimate the user's emotions and adjust the method of verbal assistance based on the estimated emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. The assistance unit can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, it can provide a concise display method. This allows the assistance unit to provide optimal verbal assistance to the user by adjusting the method of verbal assistance according to their emotions. Emotion estimation is performed using technologies such as facial recognition, speech analysis, and text analysis. Some or all of the above-described processes in the assistance unit may be performed using generative AI, or they may not. For example, the assistance unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0098] The advice unit can estimate the user's emotions and adjust the way it presents advice based on those emotions. For example, if the user is relaxed, it can provide detailed advice. If the user is in a hurry, it can provide concise advice. Furthermore, if the user is excited, it can provide advice with visually stimulating effects. By adjusting the way advice is presented according to the user's emotions, the system can provide the most appropriate advice for the user. Emotion estimation is performed using technologies such as facial recognition, speech analysis, and text analysis. Some or all of the above-described processes in the advice unit may be performed using generative AI, or they may not. For example, the advice unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0099] The support unit can estimate the user's emotions and adjust the video support method based on the estimated emotions. For example, if the user is nervous, the support unit can provide support in a calm voice. The support unit can also provide support in a cheerful voice if the user is relaxed. Furthermore, if the user is in a hurry, the support unit can provide quick and concise support. This allows the support unit to provide optimal support to the user by adjusting the video support method according to their emotions. Emotion estimation is performed using technologies such as facial recognition, speech analysis, and text analysis. Some or all of the above processing in the support unit may be performed using generative AI, or it may be performed without generative AI. For example, the support unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The reception desk enters the user's travel preferences. These preferences include destination, budget, travel duration, and activities of interest. The reception desk provides an interface for the user to enter their travel preferences. Step 2: The generation unit creates a travel plan based on the information entered by the reception unit. The generation unit uses generation AI to analyze information such as destination, budget, and travel period entered by the user and proposes the optimal travel plan. Step 3: The assistance department provides language assistance based on the travel plan created by the generation department. The assistance department provides real-time translation if the user does not understand the local language. It uses generation AI to support the user in communicating smoothly. Step 4: The Advice Department provides specific advice on cultural information and health and safety based on the language assistance provided by the Assistance Department. The Advice Department provides information on cultural manners and precautions, as well as health information, for the country or region the user is visiting. Step 5: The support department provides video support based on the advice provided by the advice department. The support department provides support via video call when the user is having trouble or in an emergency.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0104] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

[0112] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0113] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

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

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

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

[0117] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0119] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0121] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0125] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0126] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0128] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0129] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0130] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0131] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0132] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0146] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0155] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0157] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0158] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0159] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0163] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0164] 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.

[0165] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0166] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0167] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0168] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0170] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0171] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0172] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0173] [Explanation of Symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users enter their travel preferences, A generation unit that creates a travel plan based on the information entered by the reception unit, An assistance unit that provides language assistance based on the travel plan created by the generation unit, Based on the language assistance provided by the aforementioned assistance department, the advice department provides cultural information and specific advice on health and safety, The system includes a support unit that provides video support based on advice provided by the aforementioned advice unit. A system characterized by the following features.

2. The aforementioned reception unit is The system estimates the user's emotions and adjusts the input method for travel preferences based on those estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is It analyzes the user's past travel history and suggests the appropriate input method. The system according to feature 1.

4. The aforementioned reception unit is When users enter their travel preferences, specific filtering is performed based on their current living situation and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and determines the priority of their travel preferences based on those emotions. The system according to feature 1.

6. The aforementioned reception unit is When users enter their travel preferences, the system prioritizes specific and relevant criteria, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When users enter their travel preferences, the system analyzes their social media activity and inputs specific relevant criteria. The system according to feature 1.

8. The generating unit is The system estimates the user's emotions and adjusts how the travel plan is presented based on those estimated emotions. The system according to feature 1.

9. The generating unit is When creating a travel plan, the system suggests a suitable plan by referring to the user's past travel history. The system according to feature 1.

10. The generating unit is When creating a travel plan, customize the specific plan based on the user's current living situation. The system according to feature 1.

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