system

Abstract

The system according to this embodiment aims to provide a comprehensive service for users, from travel planning and booking to support at the travel destination. [Solution] The system according to the embodiment comprises a reception unit, a collection unit, a proposal unit, a reservation unit, and a support unit. The reception unit accepts a user's desired travel conditions. The collection unit collects information using multiple services or platforms based on the information accepted by the reception unit. The proposal unit proposes an appropriate travel plan based on the information collected by the collection unit. The reservation unit carries out the reservation procedure based on the plan proposed by the proposal unit. The support unit provides real-time support at the travel destination.

Description

[Technical Field]

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

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

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

[0004] Conventional technology lacks a system that consistently provides support for travel planning, booking, and at the destination, which makes the process time-consuming for users.

[0005] The system according to this embodiment aims to provide a comprehensive service for users, from travel planning and booking to support at the travel destination. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, a proposal unit, a reservation unit, and a support unit. The reception unit accepts a user's desired travel conditions. The collection unit collects information using multiple services or platforms based on the information accepted by the reception unit. The proposal unit proposes an appropriate travel plan based on the information collected by the collection unit. The reservation unit carries out reservation procedures based on the plan proposed by the proposal unit. The support unit provides real-time support at the travel destination. [Effects of the Invention]

[0007] The system according to the embodiment can provide a user with a comprehensive range of support, from travel planning to reservations and at the travel destination. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An embodiment of the present invention provides a travel total assistant AI system that collects and makes reservations for flights, hotels, activities, meals, and other services on behalf of the user. The system allows the user to input their travel preferences, and the AI ​​utilizes multiple services and platforms to collect information tailored to the user's preferences and proposes an optimal travel plan. Furthermore, the AI ​​supports multiple languages ​​and proceeds with the reservation process while interacting with the user. At the travel destination, the AI ​​provides real-time support, ensuring a smooth, safe, and comfortable travel experience. For example, the user inputs their travel preferences, including departure point, destination, travel dates, budget, and desired activities. This information is then input into the AI. Next, the AI ​​utilizes multiple services and platforms to collect information tailored to the user's preferences. For example, it collects information from flight price comparison sites, hotel booking sites, activity booking sites, restaurant booking sites, etc. The AI ​​considers the user's past travel history and preferences to propose an optimal travel plan. Furthermore, the AI ​​supports multiple languages ​​and proceeds with the reservation process while interacting with the user. For example, if the user inputs in Japanese, the AI ​​translates it into other languages ​​such as English or Chinese and proceeds with the reservation process. Furthermore, when a user enters a question or request, the AI ​​responds in real time and provides appropriate information. At the travel destination, the AI ​​provides real-time support. For example, it provides local weather information, traffic information, and information on tourist attractions. In case of emergency, the AI ​​will suggest the best course of action to ensure the user's safety. In this way, the travel total assistant AI collects information on behalf of the user, handles booking procedures, and provides support at the travel destination, thereby realizing a smooth, safe, and comfortable travel experience. Thus, the travel total assistant AI system can accept the user's travel preferences, collect information, propose the best travel plan, handle booking procedures, and provide real-time support at the travel destination, thereby realizing a smooth, safe, and comfortable travel experience.

[0029] A travel total assistant AI system according to an embodiment includes a reception unit, a collection unit, a proposal unit, a reservation unit, and a support unit. The reception unit accepts a user's desired travel conditions. The user's desired travel conditions include, but are not limited to, a departure point, a destination, a travel itinerary, a budget, and desired activities. The reception unit, for example, stores the user's desired travel conditions in a database and can use them for subsequent processing. The collection unit collects information using multiple services and platforms based on the information accepted by the reception unit. The collection unit collects information from, for example, airline ticket price comparison sites, hotel reservation sites, activity reservation sites, restaurant reservation sites, etc. The collection unit can collect optimal information taking into account the user's past travel history and preferences. The proposal unit proposes an optimal travel plan based on the information collected by the collection unit. The proposal unit proposes, for example, a travel plan that combines optimal airline tickets, hotels, activities, meals, etc. within the user's budget. The proposal unit can also customize the travel plan based on the user's preferences. The reservation unit performs a reservation procedure based on the plan proposed by the proposal unit. The reservation unit, for example, makes airline ticket reservations, hotel reservations, activity reservations, and restaurant reservations. The reservation unit can proceed with the reservation procedure while interacting with the user. The support unit provides real-time support at the travel destination. The support unit provides, for example, local weather information, traffic information, and information on tourist attractions. In the event of an emergency, the support unit can propose optimal countermeasures and ensure the user's safety. As a result, the travel total assistant AI system according to the embodiment can accept the user's desired travel conditions, collect information, propose optimal travel plans, complete the reservation procedure, and provide real-time support at the travel destination.

[0030] The data collection unit can collect information on flights, hotels, activities, and meals by utilizing multiple services or platforms. For example, the data collection unit can use flight price comparison sites to collect information on the best flights. For instance, it can compare prices from multiple airlines and select the cheapest flight. The data collection unit can also use hotel booking sites to collect information on the best hotels. For example, it can select the best hotel based on the user's budget and preferences. Furthermore, the data collection unit can use activity booking sites to collect information on the best activities. For example, it can select the best activities based on the user's interests. The data collection unit can also use restaurant booking sites to collect information on the best meals. For example, it can select the best restaurant based on the user's preferences and budget. In this way, the data collection unit can provide users with the best travel plans by utilizing multiple services and platforms to collect information on flights, hotels, activities, meals, and more.

[0031] The suggestion department can propose appropriate travel plans based on the user's preferences. For example, the suggestion department can identify user preferences by analyzing the user's past travel history. For instance, it can identify user preferences based on destinations the user has visited and services the user has used in the past. The suggestion department can also identify user preferences based on travel preferences entered by the user. For example, it can identify user preferences based on information such as departure point, destination, travel itinerary, budget, and desired activities entered by the user. Furthermore, the suggestion department can identify user preferences by analyzing the user's social media activity. For example, it can identify user preferences based on destinations and activities the user has shared on social media. As a result, the suggestion department can improve user satisfaction by proposing the optimal travel plan based on the user's preferences.

[0032] The support department can provide local weather information, traffic information, and tourist information. For example, the support department can provide local weather information. For instance, it can obtain local weather information using data from the Japan Meteorological Agency or weather forecast APIs and provide it to users. The support department can also provide local traffic information. For example, it can obtain public transport operation information and traffic congestion information and provide it to users. Furthermore, the support department can also provide information on local tourist attractions. For example, it can obtain tourist attraction information using data from tourist guidebooks or local tourist information centers and provide it to users. In this way, the support department can improve the user's travel experience by providing local weather information, traffic information, and tourist attraction information.

[0033] The support department can propose appropriate countermeasures in emergencies. For example, in the event of a natural disaster, the support department can provide guidance on evacuation sites. For instance, the support department can obtain information on local evacuation sites and provide it to users. The support department can also provide emergency contact information in the event of an accident. For example, the support department can obtain information on local emergency contacts and provide it to users. Furthermore, the support department can refer users to medical facilities in the event of illness. For example, the support department can obtain information on local medical facilities and provide it to users. In this way, the support department can ensure the safety of users by proposing the most appropriate countermeasures in emergencies.

[0034] The proposal department supports multiple languages ​​and can facilitate the reservation process while interacting with the user. For example, if a user enters information in Japanese, the proposal department will translate it into other languages ​​such as English or Chinese and proceed with the reservation process. For example, the proposal department can use a translation API to translate information entered by the user into other languages. Furthermore, the proposal department can respond in real time to questions and requests entered by the user and provide appropriate information. For example, the proposal department can use a chatbot to facilitate the reservation process while interacting with the user. In this way, the proposal department can improve user convenience by supporting multiple languages ​​and facilitating the reservation process while interacting with the user.

[0035] The input system can analyze a user's past travel history and provide appropriate input formats. For example, it can automatically display frequently visited travel destinations as suggestions. For instance, it can retrieve a user's past travel history from a database and prioritize displaying frequently visited destinations. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, if a user has used voice input in the past, it can prioritize suggesting voice input. Furthermore, based on the user's past travel history, the input system can suggest input fields related to specific seasons or events. For example, if a user has traveled during a specific season or event in the past, it can prioritize displaying input fields related to that season or event. In this way, by analyzing a user's past travel history, the input system can provide the optimal input format and improve input efficiency.

[0036] The reception unit can customize input items based on the user's current living situation and areas of interest when inputting desired travel conditions. The reception unit, for example, can prioritize displaying related input items based on activities in which the user has recently taken an interest. For example, the reception unit can analyze the user's social media activity and search history and prioritize displaying input items related to activities in which the user has recently taken an interest. The reception unit can also provide input items for proposing an appropriate travel plan based on the user's current living situation (e.g., family composition and work situation). For example, if the user is planning a family trip, the reception unit can prioritize displaying input items related to family activities and accommodations. Furthermore, the reception unit can customize input items for related travel destinations and activities based on the user's areas of interest (e.g., history, nature, art). For example, if the user is interested in history, the reception unit can prioritize displaying input items related to historical tourist spots and activities. In this way, the reception unit can propose a more appropriate travel plan by customizing input items based on the user's current living situation and areas of interest.

[0037] The reception system can prioritize displaying input fields that are highly relevant to the user's geographical location when they enter their travel preferences. For example, the reception system can automatically set the departure point based on the user's current location. For instance, it can use the user's GPS data or IP address to identify the current location and automatically set the departure point. The reception system can also prioritize displaying travel destinations close to the user's current location, narrowing down the options. For example, it can retrieve travel destinations close to the user's current location from its database and display them preferentially. Furthermore, the reception system can prioritize displaying events and activities related to the user's current location. For example, it can retrieve information on events and activities related to the user's current location and display it preferentially. In this way, the reception system can improve the efficiency of input by prioritizing the display of highly relevant input fields while considering the user's geographical location.

[0038] The reception desk can analyze the user's social media activity when they enter their travel preferences and suggest relevant input fields. For example, the reception desk can suggest relevant input fields based on travel destinations and activities the user has shared on social media. For instance, it can analyze the content of the user's social media posts and suggest input fields related to the shared travel destinations and activities. The reception desk can also customize input fields based on the user's interests on social media (for example, specific events or places). For instance, it can analyze the user's social media likes and followers and suggest input fields related to those interests. Furthermore, the reception desk can suggest relevant travel destinations and activities based on the activity of the user's friends on social media. For example, it can suggest relevant input fields based on travel destinations and activities shared by the user's friends. In this way, the reception desk can analyze the user's social media activity, suggest relevant input fields, and improve the efficiency of data entry.

[0039] When collecting information, the collection unit can select an appropriate information source by referring to the user's past travel history. The collection unit, for example, prioritizes collecting information about airlines and hotels used by the user in the past. For example, the collection unit can obtain the user's past travel history from a database and prioritize collecting information about airlines and hotels used in the past. The collection unit can also collect information about preferred activities and restaurants from the user's past travel history. For example, the collection unit can analyze the user's past travel history and prioritize collecting information about preferred activities and restaurants. Furthermore, the collection unit can analyze the user's past travel history and select the most reliable information source. For example, the collection unit can collect information from reliable websites and official databases based on the user's past travel history. In this way, the collection unit can select the optimal information source by referring to the user's past travel history, thereby improving the efficiency of information collection.

[0040] When collecting information, the collection unit can filter information based on the user's current living situation and areas of interest. For example, if the user is planning a family trip, the collection unit can prioritize collecting information on family activities and accommodations. For example, the collection unit can prioritize collecting information on family activities and accommodations by taking into account the user's current living situation (e.g., family composition and work situation). Furthermore, if the user is planning a business trip, the collection unit can also collect information on business hotels and conference facilities. For example, the collection unit can prioritize collecting information on business hotels and conference facilities by taking into account the user's current living situation. Furthermore, the collection unit can filter related information based on the user's areas of interest (e.g., history, nature, art). For example, the collection unit can prioritize collecting related information by taking into account the user's areas of interest. In this way, the collection unit can provide more appropriate information by filtering information based on the user's current living situation and areas of interest.

[0041] When collecting information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. The collection unit, for example, prioritizes collecting information related to a departure point based on the user's current location. For example, the collection unit can identify the user's current location using the user's GPS data or IP address and prioritize collecting information related to the departure point. The collection unit can also prioritize collecting information related to travel destinations close to the user's current location. For example, the collection unit can acquire information about travel destinations close to the user's current location from a database and prioritize collecting it. Furthermore, the collection unit can also prioritize collecting information about events and activities related to the user's current location. For example, the collection unit can acquire information about events and activities related to the user's current location and prioritize collecting it. This allows the collection unit to prioritize collecting highly relevant information in consideration of the user's geographical location information, thereby improving the efficiency of information collection.

[0042] When collecting information, the collection unit can analyze the user's social media activities and collect related information. The collection unit can collect related information, for example, based on travel destinations and activities shared by the user on social media. For example, the collection unit can analyze the content of the user's social media posts and collect information related to the shared travel destinations and activities. The collection unit can also filter information based on the user's social media interests (e.g., specific events or places). For example, the collection unit can analyze the user's social media like history and followers to collect information related to the interests. Furthermore, the collection unit can also collect information on related travel destinations and activities based on the activities of the user's friends on social media. For example, the collection unit can collect related information based on travel destinations and activities shared by the user's friends. In this way, the collection unit can collect related information by analyzing the user's social media activities, thereby improving the efficiency of information collection.

[0043] The suggestion function can make appropriate suggestions by referring to the user's past travel history. For example, the suggestion function can make optimal suggestions based on the airlines and hotels the user has used in the past. For example, the suggestion function can retrieve the user's past travel history from a database and make suggestions based on the airlines and hotels the user has used in the past. The suggestion function can also suggest preferred activities and restaurants based on the user's past travel history. For example, the suggestion function can analyze the user's past travel history and suggest preferred activities and restaurants. Furthermore, the suggestion function can analyze the user's past travel history and make suggestions that will provide the highest level of satisfaction. For example, the suggestion function can make suggestions that will provide the highest level of satisfaction based on the user's past travel history. In this way, the suggestion function can make optimal suggestions by referring to the user's past travel history and improve user satisfaction.

[0044] The suggestion department can customize its suggestions based on the user's current lifestyle and areas of interest. For example, if a user is planning a family trip, the suggestion department can suggest family-friendly activities and accommodations. For example, the suggestion department can consider the user's current lifestyle (e.g., family structure and work situation) to suggest family-friendly activities and accommodations. Similarly, if a user is planning a business trip, the suggestion department can suggest business-oriented hotels and conference facilities. For example, the suggestion department can consider the user's current lifestyle to suggest business-oriented hotels and conference facilities. Furthermore, the suggestion department can suggest relevant travel destinations and activities based on the user's areas of interest (e.g., history, nature, art). For example, the suggestion department can consider the user's areas of interest to suggest relevant travel destinations and activities. In this way, the suggestion department can provide more appropriate suggestions by customizing its recommendations based on the user's current lifestyle and areas of interest.

[0045] When making a suggestion, the suggestion unit can prioritize highly relevant suggestions based on the user's geographical location information. The suggestion unit can prioritize suggestions related to the departure point, for example, based on the user's current location. For example, the suggestion unit can identify the user's current location using the user's GPS data or IP address and prioritize suggestions related to the departure point. The suggestion unit can also prioritize suggestions related to travel destinations close to the user's current location. For example, the suggestion unit can acquire information about travel destinations close to the user's current location from a database and prioritize suggestions. Furthermore, the suggestion unit can prioritize suggestions about events and activities related to the user's current location. For example, the suggestion unit can acquire information about events and activities related to the user's current location and prioritize suggestions. This allows the suggestion unit to prioritize highly relevant suggestions in consideration of the user's geographical location information, thereby improving the efficiency of suggestions.

[0046] When making a suggestion, the suggestion unit may analyze the user's social media activities and make relevant suggestions. The suggestion unit may make relevant suggestions based on, for example, travel destinations or activities shared by the user on social media. For example, the suggestion unit may analyze the user's social media posts and make suggestions related to the shared travel destinations or activities. The suggestion unit may also customize the suggestion content based on the user's social media interests (e.g., specific events or places). For example, the suggestion unit may analyze the user's social media like history and followers and make suggestions related to the interests. Furthermore, the suggestion unit may also make suggestions of related travel destinations or activities based on the activities of the user's friends on social media. For example, the suggestion unit may make relevant suggestions based on travel destinations or activities shared by the user's friends. In this way, the suggestion unit may make relevant suggestions by analyzing the user's social media activities, thereby improving the efficiency of the suggestions.

[0047] The reservation department can refer to a user's past reservation history to perform the appropriate reservation process. For example, the reservation department can prioritize reservations for airlines and hotels that the user has used in the past. For example, the reservation department can retrieve a user's past reservation history from the database and prioritize reservations for airlines and hotels that the user has used in the past. The reservation department can also use the user's past reservation history to make reservations for preferred activities and restaurants. For example, the reservation department can analyze a user's past reservation history and make reservations for preferred activities and restaurants. Furthermore, the reservation department can analyze a user's past reservation history and suggest the most efficient reservation process. For example, the reservation department can suggest an efficient reservation process based on a user's past reservation history. In this way, the reservation department can perform the optimal procedure by referring to a user's past reservation history and improve the efficiency of reservations.

[0048] The reservation department can customize the reservation process based on the user's current lifestyle and areas of interest. For example, if a user is planning a family trip, the reservation department can make reservations for family-friendly activities and accommodations. For example, the reservation department can consider the user's current lifestyle (e.g., family structure and work situation) when making reservations for family-friendly activities and accommodations. The reservation department can also make reservations for business travelers, such as hotels and conference facilities. For example, the reservation department can consider the user's current lifestyle when making reservations for business travelers. Furthermore, the reservation department can customize relevant reservation procedures based on the user's areas of interest (e.g., history, nature, art). For example, the reservation department can consider the user's areas of interest when making reservations. In this way, the reservation department can provide more appropriate reservation procedures by customizing the process based on the user's current lifestyle and areas of interest.

[0049] During the reservation process, the reservation unit can prioritize highly relevant procedures based on the user's geographical location information. For example, the reservation unit can prioritize reservation procedures related to the departure point based on the user's current location. For example, the reservation unit can use the user's GPS data or IP address to identify the user's current location and prioritize reservation procedures related to the departure point. The reservation unit can also prioritize reservation procedures related to travel destinations close to the user's current location. For example, the reservation unit can obtain information about travel destinations close to the user's current location from a database and prioritize reservation procedures. Furthermore, the reservation unit can prioritize reservation procedures for events and activities related to the user's current location. For example, the reservation unit can obtain information about events and activities related to the user's current location and prioritize reservation procedures. This allows the reservation unit to prioritize highly relevant procedures in consideration of the user's geographical location information, thereby improving reservation efficiency.

[0050] The reservation unit can analyze the user's social media activity during the reservation process and perform a related procedure. The reservation unit can perform a related reservation process, for example, based on travel destinations or activities shared by the user on social media. For example, the reservation unit can analyze the user's social media posts and perform a reservation process related to the shared travel destinations or activities. The reservation unit can also customize the reservation process based on the user's social media interests (e.g., specific events or places). For example, the reservation unit can analyze the user's social media like history and followers and perform a reservation process related to the interests. Furthermore, the reservation unit can perform a reservation process for related travel destinations or activities based on the activities of the user's friends on social media. For example, the reservation unit can perform a related reservation process based on travel destinations and activities shared by the user's friends. In this way, the reservation unit can analyze the user's social media activity to perform a related procedure and improve reservation efficiency.

[0051] When providing support, the support unit can provide appropriate support by referring to the user's past travel history. The support unit can provide optimal support based on, for example, services and facilities that the user has used in the past. For example, the support unit can retrieve the user's past travel history from a database and provide support based on the services and facilities that the user has used in the past. The support unit can also provide information on favorite activities and restaurants based on the user's past travel history. For example, the support unit can analyze the user's past travel history and provide information on favorite activities and restaurants. Furthermore, the support unit can analyze the user's past travel history and provide support that will provide the highest level of satisfaction. For example, the support unit can provide support that will provide the highest level of satisfaction based on the user's past travel history. As a result, the support unit can provide optimal support by referring to the user's past travel history, thereby improving user satisfaction.

[0052] When providing support, the support unit can customize support content based on the user's current living situation and areas of interest. For example, if the user is planning a family trip, the support unit can provide information on family activities and accommodations. For example, the support unit can provide information on family activities and accommodations by taking into account the user's current living situation (e.g., family composition and work situation). Furthermore, if the user is planning a business trip, the support unit can provide information on business hotels and conference facilities by taking into account the user's current living situation. Furthermore, the support unit can customize related support content based on the user's areas of interest (e.g., history, nature, art). For example, the support unit can provide related support content by taking into account the user's areas of interest. In this way, the support unit can provide more appropriate support by customizing the support content based on the user's current living situation and areas of interest.

[0053] The support department can prioritize providing highly relevant support based on the user's geographical location. For example, it can prioritize support related to the user's origin based on their current location. For instance, it can use the user's GPS data or IP address to determine their current location and prioritize support related to their origin. It can also prioritize support related to travel destinations near the user's current location. For example, it can retrieve information on nearby travel destinations from its database and prioritize support for those destinations. Furthermore, it can prioritize support for events and activities related to the user's current location. For example, it can retrieve information on events and activities related to the user's current location and prioritize support for those events. This allows the support department to improve the efficiency of its support by prioritizing highly relevant support based on the user's geographical location.

[0054] During support, the support unit can analyze the user's social media activities and provide relevant support. The support unit can provide relevant support, for example, based on travel destinations or activities shared by the user on social media. For example, the support unit can analyze the user's social media posts and provide support related to the shared travel destinations or activities. The support unit can also customize support content based on the user's social media interests (e.g., specific events or places). For example, the support unit can analyze the user's social media like history and followers to provide support related to the interests. Furthermore, the support unit can provide support for related travel destinations or activities based on the activities of the user's friends on social media. For example, the support unit can provide relevant support based on travel destinations or activities shared by the user's friends. In this way, the support unit can provide relevant support and improve support efficiency by analyzing the user's social media activities.

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

[0056] The collection unit can analyze the user's hobbies and interests and adjust the method of information collection based on the analyzed hobbies and interests. For example, if the user likes outdoor activities, information about hiking and camping can be preferentially collected. If the user is interested in art and culture, information about museums and historical buildings can be collected. Furthermore, if the user is interested in gourmet food, information about local specialty dishes and restaurants can be collected. In this way, the collection unit can adjust the method of information collection based on the user's hobbies and interests, thereby providing more personalized travel plans.

[0057] The suggestion unit can analyze the user's travel purpose and adjust the suggestion content based on the analyzed travel purpose. For example, if the user is planning a business trip, conference facilities and business hotels can be preferentially suggested. If the user is planning a trip for relaxation, spas and resort facilities can be suggested. Furthermore, if the user is seeking adventure, suggestions regarding activities and extreme sports can be made. In this way, the suggestion unit can provide a more appropriate travel plan by adjusting the suggestion content based on the user's travel purpose.

[0058] The support unit can analyze the user's behavioral patterns during travel and adjust the support content based on the analyzed behavioral patterns. For example, if the user frequently visits tourist spots, the support unit can provide information on the tourist spot's congestion status and the optimal time to visit. If the user places importance on meals, the support unit can provide restaurant reservations and recommended meal plans. Furthermore, if the user enjoys shopping, the support unit can provide information on local shopping areas and sales. In this way, the support unit can provide a more fulfilling travel experience by adjusting the support content based on the user's behavioral patterns.

[0059] The suggestion unit can analyze real-time data during the user's trip and adjust the suggestion content based on the analyzed data. For example, it can suggest indoor activities based on weather information for the user's current location. It can also suggest alternative plans to avoid crowds based on the congestion status of tourist spots visited by the user. It can also suggest efficient travel routes based on the user's movement history. As a result, the suggestion unit can provide more flexible and appropriate travel plans by adjusting the suggestion content based on the user's real-time data.

[0060] The suggestion unit can analyze the user's health data during the trip and adjust the suggestion content based on the analyzed health data. For example, it can suggest a sightseeing route that allows for moderate exercise based on the user's step count data. It can also suggest relaxing activities based on the user's heart rate data. It can also suggest accommodations where the user can get sufficient rest based on the user's sleep data. In this way, the suggestion unit can provide a healthier and more comfortable travel plan by adjusting the suggestion content based on the user's health data.

[0061] The suggestion unit can analyze the user's social media activity during the trip and adjust the suggestions based on the analyzed activity. For example, the suggestion unit can suggest related tourist spots and activities based on photos and posts shared by the user on social media. The suggestion unit can also suggest popular travel destinations and events based on information shared by the user's followers and friends. Furthermore, the suggestion unit can make interesting suggestions based on the user's social media interests. This allows the suggestion unit to provide a more personalized travel plan by adjusting the suggestions based on the user's social media activity.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reception desk receives the user's travel preferences. These preferences include the departure point, destination, travel dates, budget, and desired activities. The reception desk saves the user's entered travel preferences to a database for use in subsequent processing. Step 2: The collection unit collects information using multiple services and platforms based on the information received by the reception unit. The collection unit collects information from airline ticket price comparison sites, hotel reservation sites, activity reservation sites, restaurant reservation sites, etc., and collects the most appropriate information taking into account the user's past travel history and preferences. Step 3: The suggestion unit proposes an optimal travel plan based on the information collected by the collection unit. The suggestion unit proposes a travel plan that combines the optimal airfare, hotel, activity, meal, etc. within the user's budget, and can also customize the travel plan based on the user's preferences. Step 4: The reservation unit performs the reservation procedure based on the plan proposed by the proposal unit. The reservation unit makes reservations for airline tickets, hotels, activities, restaurants, etc., and proceeds with the reservation procedure while interacting with the user. Step 5: The support department provides real-time support at the travel destination. The support department provides local weather, traffic, and tourist information, and suggests optimal responses in emergencies to ensure the user's safety.

[0064] (Example 2) An embodiment of the present invention provides a travel total assistant AI system that collects and makes reservations for flights, hotels, activities, meals, and other services on behalf of the user. The system allows the user to input their travel preferences, and the AI ​​utilizes multiple services and platforms to collect information tailored to the user's preferences and proposes an optimal travel plan. Furthermore, the AI ​​supports multiple languages ​​and proceeds with the reservation process while interacting with the user. At the travel destination, the AI ​​provides real-time support, ensuring a smooth, safe, and comfortable travel experience. For example, the user inputs their travel preferences, including departure point, destination, travel dates, budget, and desired activities. This information is then input into the AI. Next, the AI ​​utilizes multiple services and platforms to collect information tailored to the user's preferences. For example, it collects information from flight price comparison sites, hotel booking sites, activity booking sites, restaurant booking sites, etc. The AI ​​considers the user's past travel history and preferences to propose an optimal travel plan. Furthermore, the AI ​​supports multiple languages ​​and proceeds with the reservation process while interacting with the user. For example, if the user inputs in Japanese, the AI ​​translates it into other languages ​​such as English or Chinese and proceeds with the reservation process. Furthermore, when a user enters a question or request, the AI ​​responds in real time and provides appropriate information. At the travel destination, the AI ​​provides real-time support. For example, it provides local weather information, traffic information, and information on tourist attractions. In case of emergency, the AI ​​will suggest the best course of action to ensure the user's safety. In this way, the travel total assistant AI collects information on behalf of the user, handles booking procedures, and provides support at the travel destination, thereby realizing a smooth, safe, and comfortable travel experience. Thus, the travel total assistant AI system can accept the user's travel preferences, collect information, propose the best travel plan, handle booking procedures, and provide real-time support at the travel destination, thereby realizing a smooth, safe, and comfortable travel experience.

[0065] A travel total assistant AI system according to an embodiment includes a reception unit, a collection unit, a proposal unit, a reservation unit, and a support unit. The reception unit accepts a user's desired travel conditions. The user's desired travel conditions include, but are not limited to, a departure point, a destination, a travel itinerary, a budget, and desired activities. The reception unit, for example, stores the user's desired travel conditions in a database and can use them for subsequent processing. The collection unit collects information using multiple services and platforms based on the information accepted by the reception unit. The collection unit collects information from, for example, airline ticket price comparison sites, hotel reservation sites, activity reservation sites, restaurant reservation sites, etc. The collection unit can collect optimal information taking into account the user's past travel history and preferences. The proposal unit proposes an optimal travel plan based on the information collected by the collection unit. The proposal unit proposes, for example, a travel plan that combines optimal airline tickets, hotels, activities, meals, etc. within the user's budget. The proposal unit can also customize the travel plan based on the user's preferences. The reservation unit performs a reservation procedure based on the plan proposed by the proposal unit. The reservation unit, for example, makes airline ticket reservations, hotel reservations, activity reservations, and restaurant reservations. The reservation unit can proceed with the reservation procedure while interacting with the user. The support unit provides real-time support at the travel destination. The support unit provides, for example, local weather information, traffic information, and information on tourist attractions. In the event of an emergency, the support unit can propose optimal countermeasures and ensure the user's safety. As a result, the travel total assistant AI system according to the embodiment can accept the user's desired travel conditions, collect information, propose optimal travel plans, complete the reservation procedure, and provide real-time support at the travel destination.

[0066] The data collection unit can collect information on flights, hotels, activities, and meals by utilizing multiple services or platforms. For example, the data collection unit can use flight price comparison sites to collect information on the best flights. For instance, it can compare prices from multiple airlines and select the cheapest flight. The data collection unit can also use hotel booking sites to collect information on the best hotels. For example, it can select the best hotel based on the user's budget and preferences. Furthermore, the data collection unit can use activity booking sites to collect information on the best activities. For example, it can select the best activities based on the user's interests. The data collection unit can also use restaurant booking sites to collect information on the best meals. For example, it can select the best restaurant based on the user's preferences and budget. In this way, the data collection unit can provide users with the best travel plans by utilizing multiple services and platforms to collect information on flights, hotels, activities, meals, and more.

[0067] The suggestion department can propose appropriate travel plans based on the user's preferences. For example, the suggestion department can identify user preferences by analyzing the user's past travel history. For instance, it can identify user preferences based on destinations the user has visited and services the user has used in the past. The suggestion department can also identify user preferences based on travel preferences entered by the user. For example, it can identify user preferences based on information such as departure point, destination, travel itinerary, budget, and desired activities entered by the user. Furthermore, the suggestion department can identify user preferences by analyzing the user's social media activity. For example, it can identify user preferences based on destinations and activities the user has shared on social media. As a result, the suggestion department can improve user satisfaction by proposing the optimal travel plan based on the user's preferences.

[0068] The support department can provide local weather information, traffic information, and tourist information. For example, the support department can provide local weather information. For instance, it can obtain local weather information using data from the Japan Meteorological Agency or weather forecast APIs and provide it to users. The support department can also provide local traffic information. For example, it can obtain public transport operation information and traffic congestion information and provide it to users. Furthermore, the support department can also provide information on local tourist attractions. For example, it can obtain tourist attraction information using data from tourist guidebooks or local tourist information centers and provide it to users. In this way, the support department can improve the user's travel experience by providing local weather information, traffic information, and tourist attraction information.

[0069] The support department can propose appropriate countermeasures in emergencies. For example, in the event of a natural disaster, the support department can provide guidance on evacuation sites. For instance, the support department can obtain information on local evacuation sites and provide it to users. The support department can also provide emergency contact information in the event of an accident. For example, the support department can obtain information on local emergency contacts and provide it to users. Furthermore, the support department can refer users to medical facilities in the event of illness. For example, the support department can obtain information on local medical facilities and provide it to users. In this way, the support department can ensure the safety of users by proposing the most appropriate countermeasures in emergencies.

[0070] The proposal department supports multiple languages ​​and can facilitate the reservation process while interacting with the user. For example, if a user enters information in Japanese, the proposal department will translate it into other languages ​​such as English or Chinese and proceed with the reservation process. For example, the proposal department can use a translation API to translate information entered by the user into other languages. Furthermore, the proposal department can respond in real time to questions and requests entered by the user and provide appropriate information. For example, the proposal department can use a chatbot to facilitate the reservation process while interacting with the user. In this way, the proposal department can improve user convenience by supporting multiple languages ​​and facilitating the reservation process while interacting with the user.

[0071] The reception unit can analyze the user's emotions and adjust the input method for desired travel conditions based on the analyzed user emotions. For example, when the user is feeling stressed, the reception unit can provide a simple interface and minimize input procedures. For example, the reception unit can reduce the number of items the user needs to input and allow the user to enter only the minimum amount of information necessary. Furthermore, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. For example, the reception unit can increase the number of items the user needs to input and allow the user to enter more detailed information. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input desired travel conditions. For example, the reception unit can use voice recognition technology to convert information input by voice into text and expedite the input procedure. In this way, the reception unit can adjust the input method based on the user's emotions, thereby reducing the user's stress and providing a more comfortable input experience.

[0072] The input system can analyze a user's past travel history and provide appropriate input formats. For example, it can automatically display frequently visited travel destinations as suggestions. For instance, it can retrieve a user's past travel history from a database and prioritize displaying frequently visited destinations. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, if a user has used voice input in the past, it can prioritize suggesting voice input. Furthermore, based on the user's past travel history, the input system can suggest input fields related to specific seasons or events. For example, if a user has traveled during a specific season or event in the past, it can prioritize displaying input fields related to that season or event. In this way, by analyzing a user's past travel history, the input system can provide the optimal input format and improve input efficiency.

[0073] The reception unit can customize input items based on the user's current living situation and areas of interest when inputting desired travel conditions. The reception unit, for example, can prioritize displaying related input items based on activities in which the user has recently taken an interest. For example, the reception unit can analyze the user's social media activity and search history and prioritize displaying input items related to activities in which the user has recently taken an interest. The reception unit can also provide input items for proposing an appropriate travel plan based on the user's current living situation (e.g., family composition and work situation). For example, if the user is planning a family trip, the reception unit can prioritize displaying input items related to family activities and accommodations. Furthermore, the reception unit can customize input items for related travel destinations and activities based on the user's areas of interest (e.g., history, nature, art). For example, if the user is interested in history, the reception unit can prioritize displaying input items related to historical tourist spots and activities. In this way, the reception unit can propose a more appropriate travel plan by customizing input items based on the user's current living situation and areas of interest.

[0074] The reception unit can analyze the user's emotions and determine the priority of input items based on the analyzed user's emotions. For example, if the user is nervous, the reception unit can prioritize displaying the most important input items and postpone displaying other items. For example, the reception unit can detect that the user is nervous from the user's facial expressions and voice and prioritize displaying important input items. Furthermore, if the user is relaxed, the reception unit can sequentially display detailed input items to allow the user to freely select an item. For example, the reception unit can detect that the user is relaxed from the user's facial expressions and voice and sequentially display detailed input items. Furthermore, if the user is in a hurry, the reception unit can display only the most important input items to allow the user to quickly complete input. For example, the reception unit can detect that the user is in a hurry from the user's facial expressions and voice and display only the important input items. In this way, the reception unit can prioritize input items based on the user's emotions, thereby reducing stress for the user and providing a more comfortable input experience.

[0075] The reception system can prioritize displaying input fields that are highly relevant to the user's geographical location when they enter their travel preferences. For example, the reception system can automatically set the departure point based on the user's current location. For instance, it can use the user's GPS data or IP address to identify the current location and automatically set the departure point. The reception system can also prioritize displaying travel destinations close to the user's current location, narrowing down the options. For example, it can retrieve travel destinations close to the user's current location from its database and display them preferentially. Furthermore, the reception system can prioritize displaying events and activities related to the user's current location. For example, it can retrieve information on events and activities related to the user's current location and display it preferentially. In this way, the reception system can improve the efficiency of input by prioritizing the display of highly relevant input fields while considering the user's geographical location.

[0076] The reception desk can analyze the user's social media activity when they enter their travel preferences and suggest relevant input fields. For example, the reception desk can suggest relevant input fields based on travel destinations and activities the user has shared on social media. For instance, it can analyze the content of the user's social media posts and suggest input fields related to the shared travel destinations and activities. The reception desk can also customize input fields based on the user's interests on social media (for example, specific events or places). For instance, it can analyze the user's social media likes and followers and suggest input fields related to those interests. Furthermore, the reception desk can suggest relevant travel destinations and activities based on the activity of the user's friends on social media. For example, it can suggest relevant input fields based on travel destinations and activities shared by the user's friends. In this way, the reception desk can analyze the user's social media activity, suggest relevant input fields, and improve the efficiency of data entry.

[0077] The collection unit can analyze the user's emotions and adjust the method of collecting information based on the analyzed user's emotions. For example, when the user is relaxed, the collection unit collects detailed information and provides a wide range of options. For example, the collection unit can detect that the user is relaxed from the user's facial expressions and voice and collect detailed information. Furthermore, when the user is in a hurry, the collection unit can quickly collect only the most important information. For example, the collection unit can detect that the user is in a hurry from the user's facial expressions and voice and prioritize collecting important information. Furthermore, when the user is feeling stressed, the collection unit can prioritize collecting simple and easy-to-understand information. For example, the collection unit can detect that the user is feeling stressed from the user's facial expressions and voice and collect simple and easy-to-understand information. In this way, the collection unit can adjust the method of collecting information based on the user's emotions, thereby reducing the user's stress and providing more appropriate information.

[0078] When collecting information, the collection unit can select an appropriate information source by referring to the user's past travel history. The collection unit, for example, prioritizes collecting information about airlines and hotels used by the user in the past. For example, the collection unit can obtain the user's past travel history from a database and prioritize collecting information about airlines and hotels used in the past. The collection unit can also collect information about preferred activities and restaurants from the user's past travel history. For example, the collection unit can analyze the user's past travel history and prioritize collecting information about preferred activities and restaurants. Furthermore, the collection unit can analyze the user's past travel history and select the most reliable information source. For example, the collection unit can collect information from reliable websites and official databases based on the user's past travel history. In this way, the collection unit can select the optimal information source by referring to the user's past travel history, thereby improving the efficiency of information collection.

[0079] When collecting information, the collection unit can filter information based on the user's current living situation and areas of interest. For example, if the user is planning a family trip, the collection unit can prioritize collecting information on family activities and accommodations. For example, the collection unit can prioritize collecting information on family activities and accommodations by taking into account the user's current living situation (e.g., family composition and work situation). Furthermore, if the user is planning a business trip, the collection unit can also collect information on business hotels and conference facilities. For example, the collection unit can prioritize collecting information on business hotels and conference facilities by taking into account the user's current living situation. Furthermore, the collection unit can filter related information based on the user's areas of interest (e.g., history, nature, art). For example, the collection unit can prioritize collecting related information by taking into account the user's areas of interest. In this way, the collection unit can provide more appropriate information by filtering information based on the user's current living situation and areas of interest.

[0080] The collection unit can analyze the user's emotions and determine the priority of information to be collected based on the analyzed user's emotions. For example, if the user is nervous, the collection unit can prioritize collecting the most important information and postpone collecting other information. For example, the collection unit can detect that the user is nervous from the user's facial expressions or voice and prioritize collecting important information. Furthermore, if the user is relaxed, the collection unit can sequentially collect detailed information and allow the user to freely select it. For example, the collection unit can detect that the user is relaxed from the user's facial expressions or voice and sequentially collect detailed information. Furthermore, if the user is in a hurry, the collection unit can quickly collect only the most important information. For example, the collection unit can detect that the user is in a hurry from the user's facial expressions or voice and prioritize collecting important information. In this way, the collection unit can determine the priority of information to be collected based on the user's emotions, thereby reducing the user's stress and providing more appropriate information.

[0081] When collecting information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. The collection unit, for example, prioritizes collecting information related to a departure point based on the user's current location. For example, the collection unit can identify the user's current location using the user's GPS data or IP address and prioritize collecting information related to the departure point. The collection unit can also prioritize collecting information related to travel destinations close to the user's current location. For example, the collection unit can acquire information about travel destinations close to the user's current location from a database and prioritize collecting it. Furthermore, the collection unit can also prioritize collecting information about events and activities related to the user's current location. For example, the collection unit can acquire information about events and activities related to the user's current location and prioritize collecting it. This allows the collection unit to prioritize collecting highly relevant information in consideration of the user's geographical location information, thereby improving the efficiency of information collection.

[0082] When collecting information, the collection unit can analyze the user's social media activities and collect related information. The collection unit can collect related information, for example, based on travel destinations and activities shared by the user on social media. For example, the collection unit can analyze the content of the user's social media posts and collect information related to the shared travel destinations and activities. The collection unit can also filter information based on the user's social media interests (e.g., specific events or places). For example, the collection unit can analyze the user's social media like history and followers to collect information related to the interests. Furthermore, the collection unit can also collect information on related travel destinations and activities based on the activities of the user's friends on social media. For example, the collection unit can collect related information based on travel destinations and activities shared by the user's friends. In this way, the collection unit can collect related information by analyzing the user's social media activities, thereby improving the efficiency of information collection.

[0083] The suggestion unit can analyze the user's emotions and adjust the way in which suggestions are expressed based on the analyzed user's emotions. For example, if the user is nervous, the suggestion unit can make a simple and highly visible suggestion. For example, the suggestion unit can detect that the user is nervous from the user's facial expressions and voice and make a simple and highly visible suggestion. Furthermore, if the user is relaxed, the suggestion unit can make a suggestion including detailed information. For example, the suggestion unit can detect that the user is relaxed from the user's facial expressions and voice and make a suggestion including detailed information. Furthermore, if the user is in a hurry, the suggestion unit can make a suggestion that focuses on the main points. For example, the suggestion unit can detect that the user is in a hurry from the user's facial expressions and voice and make a suggestion that focuses on the main points. In this way, the suggestion unit can adjust the way in which suggestions are expressed based on the user's emotions, thereby reducing the user's stress and providing more appropriate suggestions.

[0084] The suggestion function can make appropriate suggestions by referring to the user's past travel history. For example, the suggestion function can make optimal suggestions based on the airlines and hotels the user has used in the past. For example, the suggestion function can retrieve the user's past travel history from a database and make suggestions based on the airlines and hotels the user has used in the past. The suggestion function can also suggest preferred activities and restaurants based on the user's past travel history. For example, the suggestion function can analyze the user's past travel history and suggest preferred activities and restaurants. Furthermore, the suggestion function can analyze the user's past travel history and make suggestions that will provide the highest level of satisfaction. For example, the suggestion function can make suggestions that will provide the highest level of satisfaction based on the user's past travel history. In this way, the suggestion function can make optimal suggestions by referring to the user's past travel history and improve user satisfaction.

[0085] The suggestion department can customize its suggestions based on the user's current lifestyle and areas of interest. For example, if a user is planning a family trip, the suggestion department can suggest family-friendly activities and accommodations. For example, the suggestion department can consider the user's current lifestyle (e.g., family structure and work situation) to suggest family-friendly activities and accommodations. Similarly, if a user is planning a business trip, the suggestion department can suggest business-oriented hotels and conference facilities. For example, the suggestion department can consider the user's current lifestyle to suggest business-oriented hotels and conference facilities. Furthermore, the suggestion department can suggest relevant travel destinations and activities based on the user's areas of interest (e.g., history, nature, art). For example, the suggestion department can consider the user's areas of interest to suggest relevant travel destinations and activities. In this way, the suggestion department can provide more appropriate suggestions by customizing its recommendations based on the user's current lifestyle and areas of interest.

[0086] The suggestion unit can analyze the user's emotions and prioritize suggestions based on those emotions. For example, if the user is tense, the suggestion unit will prioritize the most important suggestions and postpone others. For instance, it can detect tension from the user's facial expressions and voice and prioritize important suggestions. Furthermore, if the user is relaxed, the suggestion unit can sequentially offer detailed suggestions, allowing the user to choose freely. For example, it can detect relaxation from the user's facial expressions and voice and sequentially offer detailed suggestions. Additionally, if the user is in a hurry, the suggestion unit can quickly offer only the most important suggestions. For example, it can detect urgency from the user's facial expressions and voice and prioritize important suggestions. In this way, by prioritizing suggestions based on the user's emotions, the suggestion unit can reduce user stress and provide more appropriate suggestions.

[0087] When making a suggestion, the suggestion unit can prioritize highly relevant suggestions based on the user's geographical location information. The suggestion unit can prioritize suggestions related to the departure point, for example, based on the user's current location. For example, the suggestion unit can identify the user's current location using the user's GPS data or IP address and prioritize suggestions related to the departure point. The suggestion unit can also prioritize suggestions related to travel destinations close to the user's current location. For example, the suggestion unit can acquire information about travel destinations close to the user's current location from a database and prioritize suggestions. Furthermore, the suggestion unit can prioritize suggestions about events and activities related to the user's current location. For example, the suggestion unit can acquire information about events and activities related to the user's current location and prioritize suggestions. This allows the suggestion unit to prioritize highly relevant suggestions in consideration of the user's geographical location information, thereby improving the efficiency of suggestions.

[0088] When making a suggestion, the suggestion unit may analyze the user's social media activities and make relevant suggestions. The suggestion unit may make relevant suggestions based on, for example, travel destinations or activities shared by the user on social media. For example, the suggestion unit may analyze the user's social media posts and make suggestions related to the shared travel destinations or activities. The suggestion unit may also customize the suggestion content based on the user's social media interests (e.g., specific events or places). For example, the suggestion unit may analyze the user's social media like history and followers and make suggestions related to the interests. Furthermore, the suggestion unit may also make suggestions of related travel destinations or activities based on the activities of the user's friends on social media. For example, the suggestion unit may make relevant suggestions based on travel destinations or activities shared by the user's friends. In this way, the suggestion unit may make relevant suggestions by analyzing the user's social media activities, thereby improving the efficiency of the suggestions.

[0089] The reservation unit can analyze the user's emotions and adjust the reservation procedure method based on the analyzed user's emotions. For example, if the user is relaxed, the reservation unit can provide detailed reservation options and suggest a customizable reservation method. For example, the reservation unit can detect that the user is relaxed from the user's facial expressions and voice and provide detailed reservation options. Furthermore, if the user is in a hurry, the reservation unit can expedite the most important reservation procedure. For example, the reservation unit can detect that the user is in a hurry from the user's facial expressions and voice and prioritize important reservation procedures. Furthermore, if the user is feeling stressed, the reservation unit can prioritize a simple and easy-to-understand reservation procedure. For example, the reservation unit can detect that the user is feeling stressed from the user's facial expressions and voice and provide a simple and easy-to-understand reservation procedure. In this way, the reservation unit can adjust the reservation procedure method based on the user's emotions, thereby reducing the user's stress and providing a more comfortable reservation experience.

[0090] The reservation department can refer to a user's past reservation history to perform the appropriate reservation process. For example, the reservation department can prioritize reservations for airlines and hotels that the user has used in the past. For example, the reservation department can retrieve a user's past reservation history from the database and prioritize reservations for airlines and hotels that the user has used in the past. The reservation department can also use the user's past reservation history to make reservations for preferred activities and restaurants. For example, the reservation department can analyze a user's past reservation history and make reservations for preferred activities and restaurants. Furthermore, the reservation department can analyze a user's past reservation history and suggest the most efficient reservation process. For example, the reservation department can suggest an efficient reservation process based on a user's past reservation history. In this way, the reservation department can perform the optimal procedure by referring to a user's past reservation history and improve the efficiency of reservations.

[0091] The reservation department can customize the reservation process based on the user's current lifestyle and areas of interest. For example, if a user is planning a family trip, the reservation department can make reservations for family-friendly activities and accommodations. For example, the reservation department can consider the user's current lifestyle (e.g., family structure and work situation) when making reservations for family-friendly activities and accommodations. The reservation department can also make reservations for business travelers, such as hotels and conference facilities. For example, the reservation department can consider the user's current lifestyle when making reservations for business travelers. Furthermore, the reservation department can customize relevant reservation procedures based on the user's areas of interest (e.g., history, nature, art). For example, the reservation department can consider the user's areas of interest when making reservations. In this way, the reservation department can provide more appropriate reservation procedures by customizing the process based on the user's current lifestyle and areas of interest.

[0092] The reservation system can analyze the user's emotions and prioritize reservation procedures based on those emotions. For example, if the user is nervous, the system will prioritize the most important reservation procedures and postpone others. For instance, the system can detect nervousness from the user's facial expressions and voice and prioritize important reservation procedures. Furthermore, if the user is relaxed, the system can sequentially perform detailed reservation procedures, allowing the user to choose freely. For instance, the system can detect relaxation from the user's facial expressions and voice and sequentially perform detailed reservation procedures. Additionally, if the user is in a hurry, the system can quickly perform only the most important reservation procedures. For instance, the system can detect urgency from the user's facial expressions and voice and prioritize important reservation procedures. In this way, by prioritizing reservation procedures based on the user's emotions, the reservation system can reduce user stress and provide a more comfortable reservation experience.

[0093] During the reservation process, the reservation unit can prioritize highly relevant procedures based on the user's geographical location information. For example, the reservation unit can prioritize reservation procedures related to the departure point based on the user's current location. For example, the reservation unit can use the user's GPS data or IP address to identify the user's current location and prioritize reservation procedures related to the departure point. The reservation unit can also prioritize reservation procedures related to travel destinations close to the user's current location. For example, the reservation unit can obtain information about travel destinations close to the user's current location from a database and prioritize reservation procedures. Furthermore, the reservation unit can prioritize reservation procedures for events and activities related to the user's current location. For example, the reservation unit can obtain information about events and activities related to the user's current location and prioritize reservation procedures. This allows the reservation unit to prioritize highly relevant procedures in consideration of the user's geographical location information, thereby improving reservation efficiency.

[0094] The reservation unit can analyze the user's social media activity during the reservation process and perform a related procedure. The reservation unit can perform a related reservation process, for example, based on travel destinations or activities shared by the user on social media. For example, the reservation unit can analyze the user's social media posts and perform a reservation process related to the shared travel destinations or activities. The reservation unit can also customize the reservation process based on the user's social media interests (e.g., specific events or places). For example, the reservation unit can analyze the user's social media like history and followers and perform a reservation process related to the interests. Furthermore, the reservation unit can perform a reservation process for related travel destinations or activities based on the activities of the user's friends on social media. For example, the reservation unit can perform a related reservation process based on travel destinations and activities shared by the user's friends. In this way, the reservation unit can analyze the user's social media activity to perform a related procedure and improve reservation efficiency.

[0095] The support unit can analyze the user's emotions and adjust the support method based on the analyzed user's emotions. For example, if the user is nervous, the support unit can provide support in a calm voice. For example, the support unit can detect that the user is nervous from the user's facial expressions and voice and provide support in a calm voice. Furthermore, if the user is relaxed, the support unit can provide support including detailed information. For example, the support unit can detect that the user is relaxed from the user's facial expressions and voice and provide support including detailed information. Furthermore, if the user is in a hurry, the support unit can provide quick and concise support. For example, the support unit can detect that the user is in a hurry from the user's facial expressions and voice and provide quick and concise support. In this way, the support unit can adjust the support method based on the user's emotions, thereby reducing the user's stress and providing more appropriate support.

[0096] When providing support, the support unit can provide appropriate support by referring to the user's past travel history. The support unit can provide optimal support based on, for example, services and facilities that the user has used in the past. For example, the support unit can retrieve the user's past travel history from a database and provide support based on the services and facilities that the user has used in the past. The support unit can also provide information on favorite activities and restaurants based on the user's past travel history. For example, the support unit can analyze the user's past travel history and provide information on favorite activities and restaurants. Furthermore, the support unit can analyze the user's past travel history and provide support that will provide the highest level of satisfaction. For example, the support unit can provide support that will provide the highest level of satisfaction based on the user's past travel history. As a result, the support unit can provide optimal support by referring to the user's past travel history, thereby improving user satisfaction.

[0097] When providing support, the support unit can customize support content based on the user's current living situation and areas of interest. For example, if the user is planning a family trip, the support unit can provide information on family activities and accommodations. For example, the support unit can provide information on family activities and accommodations by taking into account the user's current living situation (e.g., family composition and work situation). Furthermore, if the user is planning a business trip, the support unit can provide information on business hotels and conference facilities by taking into account the user's current living situation. Furthermore, the support unit can customize related support content based on the user's areas of interest (e.g., history, nature, art). For example, the support unit can provide related support content by taking into account the user's areas of interest. In this way, the support unit can provide more appropriate support by customizing the support content based on the user's current living situation and areas of interest.

[0098] The support unit can analyze the user's emotions and determine the priority of support based on the analyzed user's emotions. For example, if the user is nervous, the support unit can prioritize providing the most important support and postpone other support. For example, the support unit can detect that the user is nervous from the user's facial expressions or voice and prioritize providing important support. Furthermore, if the user is relaxed, the support unit can sequentially provide detailed support and allow the user to freely select. For example, the support unit can detect that the user is relaxed from the user's facial expressions or voice and provide detailed support sequentially. Furthermore, if the user is in a hurry, the support unit can quickly provide only the most important support. For example, the support unit can detect that the user is in a hurry from the user's facial expressions or voice and prioritize providing important support. In this way, the support unit can determine the priority of support based on the user's emotions, thereby reducing the user's stress and providing more appropriate support.

[0099] The support department can prioritize providing highly relevant support based on the user's geographical location. For example, it can prioritize support related to the user's origin based on their current location. For instance, it can use the user's GPS data or IP address to determine their current location and prioritize support related to their origin. It can also prioritize support related to travel destinations near the user's current location. For example, it can retrieve information on nearby travel destinations from its database and prioritize support for those destinations. Furthermore, it can prioritize support for events and activities related to the user's current location. For example, it can retrieve information on events and activities related to the user's current location and prioritize support for those events. This allows the support department to improve the efficiency of its support by prioritizing highly relevant support based on the user's geographical location.

[0100] During support, the support unit can analyze the user's social media activities and provide relevant support. The support unit can provide relevant support, for example, based on travel destinations or activities shared by the user on social media. For example, the support unit can analyze the user's social media posts and provide support related to the shared travel destinations or activities. The support unit can also customize support content based on the user's social media interests (e.g., specific events or places). For example, the support unit can analyze the user's social media like history and followers to provide support related to the interests. Furthermore, the support unit can provide support for related travel destinations or activities based on the activities of the user's friends on social media. For example, the support unit can provide relevant support based on travel destinations or activities shared by the user's friends. In this way, the support unit can provide relevant support and improve support efficiency by analyzing the user's social media activities. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, collection unit, proposal unit, reservation unit, and support unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and accepts the user's desired travel conditions. The collection unit is realized by the specific processing unit 290 of the data processing device 12 and collects information using multiple services and platforms. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes an optimal travel plan based on the collected information. The reservation unit is realized by the control unit 46A of the smart device 14 and performs reservation procedures based on the proposed plan. The support unit is realized by the control unit 46A of the smart device 14 and provides real-time support at the travel destination. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, collection unit, proposal unit, reservation 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 accepts the user's travel preferences. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and collects information by utilizing multiple services and infrastructures. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the optimal travel plan based on the collected information. The reservation unit is implemented, for example, by the control unit 46A of the smart glasses 214 and performs the reservation procedure based on the proposed plan. The support unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides real-time support at the travel destination. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, collection unit, proposal unit, reservation unit, and support unit, is implemented by, for example, 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 accepts the user's travel preferences. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects information by utilizing multiple services and infrastructures. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal travel plan based on the collected information. The reservation unit is implemented by, for example, the control unit 46A of the headset terminal 314 and performs the reservation procedure based on the proposed plan. The support unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides real-time support at the travel destination. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, collection unit, proposal unit, reservation unit, and support unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and accepts the user's desired travel conditions. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects information by utilizing multiple services and platforms. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes an optimal travel plan based on the collected information. The reservation unit is realized, for example, by the control unit 46A of the robot 414 and performs reservation procedures based on the proposed plan. The support unit is realized, for example, by the control unit 46A of the robot 414 and provides real-time support at the travel destination.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The reception unit can analyze the user's health condition and adjust the travel plan based on the analyzed health condition. For example, if the user has an allergy, restaurants that provide meals that accommodate the allergy can be preferentially suggested. Also, if the user has a specific health problem, accommodations that are close to medical facilities that address that problem can be suggested. Furthermore, if the user is looking to relax, travel destinations with plenty of spa and relaxation facilities can be suggested. In this way, the reception unit can provide a safer and more comfortable travel experience by adjusting the travel plan based on the user's health condition.

[0103] The collection unit can analyze the user's hobbies and interests and adjust the method of information collection based on the analyzed hobbies and interests. For example, if the user likes outdoor activities, information about hiking and camping can be preferentially collected. If the user is interested in art and culture, information about museums and historical buildings can be collected. Furthermore, if the user is interested in gourmet food, information about local specialty dishes and restaurants can be collected. In this way, the collection unit can adjust the method of information collection based on the user's hobbies and interests, thereby providing more personalized travel plans.

[0104] The suggestion unit can analyze the user's travel purpose and adjust the suggestion content based on the analyzed travel purpose. For example, if the user is planning a business trip, conference facilities and business hotels can be preferentially suggested. If the user is planning a trip for relaxation, spas and resort facilities can be suggested. Furthermore, if the user is seeking adventure, suggestions regarding activities and extreme sports can be made. In this way, the suggestion unit can provide a more appropriate travel plan by adjusting the suggestion content based on the user's travel purpose.

[0105] The support unit can analyze the user's behavioral patterns during travel and adjust the support content based on the analyzed behavioral patterns. For example, if the user frequently visits tourist spots, the support unit can provide information on the tourist spot's congestion status and the optimal time to visit. If the user places importance on meals, the support unit can provide restaurant reservations and recommended meal plans. Furthermore, if the user enjoys shopping, the support unit can provide information on local shopping areas and sales. In this way, the support unit can provide a more fulfilling travel experience by adjusting the support content based on the user's behavioral patterns.

[0106] The support department can analyze the user's emotions and adjust emergency response measures based on those emotions. For example, if the user is panicking, it can give instructions in a calm voice and guide them to an evacuation site with simple steps. If the user is calm, it can provide detailed information and present multiple options. Furthermore, if the user is feeling anxious, it can provide reassuring messages and support. In this way, the support department can ensure the user's safety and provide a sense of security by adjusting emergency response measures based on the user's emotions.

[0107] The suggestion unit can analyze real-time data during the user's trip and adjust the suggestion content based on the analyzed data. For example, it can suggest indoor activities based on weather information for the user's current location. It can also suggest alternative plans to avoid crowds based on the congestion status of tourist spots visited by the user. It can also suggest efficient travel routes based on the user's movement history. As a result, the suggestion unit can provide more flexible and appropriate travel plans by adjusting the suggestion content based on the user's real-time data.

[0108] The data collection unit can analyze the user's emotions and determine the priority of information collection based on those emotions. For example, if the user is excited, it can prioritize collecting information about activities and events. If the user is relaxed, it can collect information about relaxation facilities and quiet tourist spots. Furthermore, if the user is stressed, it can prioritize collecting information that helps relieve stress. In this way, the data collection unit can provide more relevant information by prioritizing information collection based on the user's emotions.

[0109] The suggestion unit can analyze the user's health data during the trip and adjust the suggestion content based on the analyzed health data. For example, it can suggest a sightseeing route that allows for moderate exercise based on the user's step count data. It can also suggest relaxing activities based on the user's heart rate data. It can also suggest accommodations where the user can get sufficient rest based on the user's sleep data. In this way, the suggestion unit can provide a healthier and more comfortable travel plan by adjusting the suggestion content based on the user's health data.

[0110] The support department can analyze the user's emotions and adjust the support provided at the travel destination based on those emotions. For example, if the user is feeling anxious, it can provide messages and support to reassure them. If the user is excited, it can suggest activities and events to maintain that excitement. Furthermore, if the user is tired, it can suggest places and activities where they can relax. In this way, the support department can provide more appropriate support by adjusting the support based on the user's emotions.

[0111] The suggestion unit can analyze the user's social media activity during the trip and adjust the suggestions based on the analyzed activity. For example, the suggestion unit can suggest related tourist spots and activities based on photos and posts shared by the user on social media. The suggestion unit can also suggest popular travel destinations and events based on information shared by the user's followers and friends. Furthermore, the suggestion unit can make interesting suggestions based on the user's social media interests. This allows the suggestion unit to provide a more personalized travel plan by adjusting the suggestions based on the user's social media activity.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The reception desk receives the user's travel preferences. These preferences include the departure point, destination, travel dates, budget, and desired activities. The reception desk saves the user's entered travel preferences to a database for use in subsequent processing. Step 2: The collection unit collects information using multiple services and platforms based on the information received by the reception unit. The collection unit collects information from airline ticket price comparison sites, hotel reservation sites, activity reservation sites, restaurant reservation sites, etc., and collects the most appropriate information taking into account the user's past travel history and preferences. Step 3: The suggestion unit proposes an optimal travel plan based on the information collected by the collection unit. The suggestion unit proposes a travel plan that combines the optimal airfare, hotel, activity, meal, etc. within the user's budget, and can also customize the travel plan based on the user's preferences. Step 4: The reservation unit performs the reservation procedure based on the plan proposed by the proposal unit. The reservation unit makes reservations for airline tickets, hotels, activities, restaurants, etc., and proceeds with the reservation procedure while interacting with the user. Step 5: The support department provides real-time support at the travel destination. The support department provides local weather, traffic, and tourist information, and suggests optimal responses in emergencies to ensure the user's safety.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0157] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0158] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0169] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0170] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0171] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0175] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0176] 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.

[0177] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0178] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0179] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0180] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0182] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0183] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0184] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives the user's desired travel conditions; a collection unit that collects information by utilizing a plurality of services or platforms based on the information received by the reception unit; a suggestion unit that suggests an appropriate travel plan based on the information collected by the collection unit; a reservation unit that performs reservation procedures based on the plan proposed by the proposal unit; A support unit that provides real-time support at the travel destination. A system characterized by:

2. The collecting unit Leverage multiple services or platforms to collect flight, hotel, activity, and dining information The system of claim 1 .

3. The proposal unit Providing suitable travel plans based on user preferences The system of claim 1 .

4. The support portion is Providing local weather, traffic, and tourist information The system of claim 1 .

5. The support portion is Proposing appropriate countermeasures in emergencies The system of claim 1 .

6. The proposal unit Supports multiple languages ​​and allows users to interact with the booking process The system of claim 1 .

7. The reception unit Analyze user emotions and adjust the input method for desired travel conditions based on the analyzed user emotions. The system of claim 1 .

8. The reception unit Analyze the user's past travel history and provide an appropriate input format The system of claim 1 .

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