System

Abstract

The system according to the embodiment aims to propose a travel activity based on a user's desire and provide safety measures and support.SOLUTION: A system according to an embodiment includes a reception unit, a proposal unit, a safety unit, and a support unit. The reception unit receives an input of a destination of a trip of a user and an activity desired by the user. The proposal unit analyzes the information received by the reception unit and presents an example of a specific activity based on the user's desire. The safety unit provides appropriate safety measures and responsible support based on the activity suggested by the suggestion unit. The support unit smoothly makes reservations and planning based on the support provided by the safety unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately suggest travel activities based on the user's preferences or provide safety measures and support, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest travel activities based on the user's wishes and provide safety measures and support. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a suggestion unit, a safety unit, and a support unit. The reception unit accepts input of a user's travel destination and desired activities. The suggestion unit analyzes the information accepted by the reception unit and presents specific examples of activities based on the user's preferences. The safety unit provides appropriate safety measures and responsible support based on the activities suggested by the suggestion unit. The support unit smoothly completes reservations and planning based on the support provided by the safety unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest travel activities based on the user's wishes and provide safety measures and support. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A travel support system according to an embodiment of the present invention accepts input from a user about their travel destination and desired activities. It uses AI to suggest special activities that allow users to enjoy hidden spots and local culture based on the user's preferences, provides top-notch safety measures, and ensures responsible support, facilitating smooth booking and planning. For example, a user may input their travel destination and desired activities, such as "I want to fully enjoy nature" or "I want to visit historical sites." This information is then entered into the system. The travel support system then uses AI to suggest special activities that allow users to enjoy hidden spots and local culture based on the user's preferences. For example, for a user who wants to fully enjoy nature, the system suggests a tour of remote areas led by a local guide or a workshop where users can experience local culture. This allows users to enjoy special experiences tailored to their interests. Furthermore, the travel support system provides top-notch safety measures and responsible support. For example, by providing a support system for emergencies during travel and local safety information, users can enjoy their trip with peace of mind. The travel support system also provides solutions for travel troubles, allowing users to enjoy their trip with peace of mind. The travel support system also provides smooth support for booking and planning. For example, it provides a function that allows users to make reservations for accommodations and transportation all at once, and a function that automatically creates a travel schedule. This allows users to prepare for their trip without any hassle. In this way, the travel support system can support all the processes of a trip so that users can have an inspiring experience. In this way, the travel support system can support all the processes of a trip so that users can have an inspiring experience. For example, users can enjoy special experiences that match their interests and wishes, and can enjoy their trip with peace of mind.

[0029] A travel support system according to an embodiment includes a reception unit, a suggestion unit, a safety unit, and a support unit. The reception unit accepts input of a user's travel destination and desired activities. Examples of the user's travel destination include, but are not limited to, domestic travel, international travel, urban areas, and natural areas. Examples of desired activities include, but are not limited to, sightseeing, sports, cultural experiences, and relaxation. The reception unit accepts input of the user's preferences, such as "I want to enjoy nature" or "I want to visit historical sites." The suggestion unit uses AI to analyze the information accepted by the reception unit and suggests specific examples of activities based on the user's preferences. The suggestion unit performs analysis using, for example, text analysis, data mining, and machine learning algorithms. The suggestion unit suggests, for example, a remote tour led by a local guide or a workshop where the user can experience local culture based on the user's preferences. The suggestion unit suggests specific examples of activities, such as hiking, visiting museums, and local cooking classes. The safety unit provides appropriate safety measures and responsible support based on the activities suggested by the suggestion unit. The safety department provides appropriate safety measures, such as providing insurance, presenting safety guidelines, and providing emergency contact information. The safety department provides responsible support, such as a 24-hour support center and deploying local staff. The support department smoothly performs reservations and planning based on the support provided by the safety department. The support department provides, for example, a function for making reservations for accommodations and transportation in one go. The support department provides reservation and planning methods, such as an online reservation system and automatic generation of travel plans. As a result, the travel support system according to the embodiment can efficiently support the user's travel experience and provide an exciting experience.

[0030] The suggestion unit can suggest a remote tour guided by a local guide or a workshop where the user can experience local culture based on the user's preference. The suggestion unit, for example, suggests a remote tour guided by a local guide based on the user's preference. For example, the suggestion unit suggests a tour that takes the user to unexplored natural areas or places that are not tourist destinations. The suggestion unit can also suggest a workshop where the user can experience local culture based on the user's preference. For example, the suggestion unit suggests a traditional craft experience or a cooking class for local cuisine. This makes it possible to provide a special experience that matches the user's interests. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can cause AI to select the activities to suggest based on the user's preference.

[0031] The safety department can provide a support system for emergencies during travel or local safety information. The safety department, for example, provides a support system for emergencies during travel. For example, the safety department provides emergency contact information and a 24-hour support center. The safety department can also provide local safety information. For example, the safety department provides public safety information, information on medical facilities, evacuation routes, etc. This allows users to enjoy their trip with peace of mind. Some or all of the above-mentioned processing in the safety department may be performed using AI, or may be performed without using AI. For example, the safety department can have AI collect and provide local safety information.

[0032] The support unit can provide a function for making reservations for accommodations or transportation in one go. The support unit, for example, provides a function for making reservations for accommodations in one go. For example, the support unit makes reservations for accommodations such as hotels, guesthouses, and resorts in one go. The support unit can also provide a function for making reservations for transportation in one go. For example, the support unit makes reservations for transportation such as airplanes, trains, buses, and rental cars in one go. This allows the user to prepare for a trip without hassle. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without using AI. For example, the support unit can have AI make reservations for accommodations and transportation.

[0033] The support unit can provide a function to automatically create a travel schedule. The support unit provides, for example, a function to automatically create a travel schedule. For example, the support unit automatically generates itineraries and customizes them based on the user's wishes. This allows the user to efficiently manage their travel schedule. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can cause AI to automatically generate a travel schedule.

[0034] The reception unit can analyze the user's past travel history and select an appropriate input method. The reception unit, for example, analyzes the user's past travel history and selects the optimal input method. For example, the reception unit automatically displays places the user has frequently visited in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also provide input options related to specific seasons or events from the user's past travel history. This makes it possible to provide the optimal input method based on the user's past travel history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can cause AI to analyze the user's past travel history.

[0035] The reception unit can filter based on the user's current interests when inputting a travel destination or a desired activity. For example, the reception unit can filter based on the user's current interests when inputting a travel destination or a desired activity. For example, the reception unit can preferentially display related activities based on keywords recently searched by the user. The reception unit can also suggest related destinations based on posts that the user has "liked" on social media. The reception unit can also filter related activities based on events the user has recently participated in. This makes it possible to provide optimal suggestions based on the user's current interests. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can cause AI to filter based on the user's current interests.

[0036] The reception unit can select an appropriate input means according to the user's input method when inputting a travel destination or a desired activity. For example, when inputting a travel destination or a desired activity, the reception unit selects the optimal input means according to the user's input method (voice, text, image, etc.). For example, if the user selects voice input, the reception unit supports the input using voice recognition technology. Furthermore, if the user selects image input, the reception unit can also identify the destination or activity using image analysis technology. Furthermore, if the user selects text input, the reception unit can also analyze the input content using natural language processing technology. This makes it possible to provide the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can cause AI to select the input means according to the user's input method.

[0037] The reception unit can prioritize input of highly relevant information based on the user's geographical location information when inputting a travel destination or a desired activity. For example, when inputting a travel destination or a desired activity, the reception unit prioritizes input of highly relevant information taking into account the user's geographical location information. For example, the reception unit prioritizes suggesting locations close to the user's current location. The reception unit can also suggest activities near places the user has previously visited. The reception unit can also suggest optimal transportation methods based on the user's current location. This makes it possible to provide optimal information based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can cause AI to analyze the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity and input appropriate information when the user inputs a travel destination or a desired activity. For example, the reception unit can analyze the user's social media activity and input relevant information when the user inputs a travel destination or a desired activity. For example, the reception unit can suggest locations where the user has checked in on social media as candidate locations. The reception unit can also analyze the content of the user's social media posts and suggest related activities. The reception unit can also suggest related locations based on the activity of the user's friends on social media. This makes it possible to provide optimal information based on the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can cause AI to analyze the user's social media activity.

[0039] The reception unit can adjust the input method by reflecting the user's past feedback when inputting a travel destination or a desired activity. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting a travel destination or a desired activity. For example, the reception unit preferentially suggests input methods that the user has previously preferred. The reception unit can also customize the input interface based on the user's past feedback. The reception unit can also adjust the input interface to avoid input methods that the user has previously dissatisfied with. This makes it possible to provide an optimal input method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can cause AI to analyze the user's past feedback.

[0040] The suggestion unit can change the level of detail of the suggestion based on the importance of the activity when making the suggestion. For example, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the activity when making the suggestion. For example, the suggestion unit can provide a detailed explanation for an activity with high importance. The suggestion unit can also provide a concise explanation for an activity with low importance. The suggestion unit can also adjust the level of detail of the activity based on the user's interest level. This makes it possible to provide an optimal level of detail of the suggestion according to the importance of the activity. Some or all of the above-described processing in the suggestion unit can be performed using, or without, AI. For example, the suggestion unit can cause AI to evaluate the importance of the activity and adjust the level of detail of the suggestion.

[0041] The suggestion unit can apply an appropriate suggestion algorithm depending on the activity category when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the activity category when making a suggestion. For example, the suggestion unit can make suggestions that emphasize environmental information for nature-related activities. The suggestion unit can also make suggestions that emphasize history and background information for cultural activities. The suggestion unit can also make suggestions that emphasize thrills and challenges for adventure-related activities. This makes it possible to provide an optimal suggestion algorithm depending on the activity category. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can cause AI to apply the suggestion algorithm depending on the activity category.

[0042] The suggestion unit can improve the accuracy of suggestions based on the user's past suggestion results when making suggestions. For example, the suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results when making suggestions. For example, the suggestion unit can suggest similar activities based on suggestions that the user has liked in the past. The suggestion unit can also adjust to avoid suggestions that the user has rejected in the past. The suggestion unit can also improve the suggestion algorithm based on the user's past feedback. This makes it possible to provide optimal suggestions based on the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can cause AI to analyze the user's past suggestion results and improve the suggestion algorithm.

[0043] The suggestion unit, when making suggestions, can determine the order of suggestions based on the time when the activities will be performed. For example, the suggestion unit, when making suggestions, can determine the priority of suggestions based on the time when the activities will be performed. For example, the suggestion unit can prioritize seasonal activities. The suggestion unit can also prioritize activities during specific event periods. The suggestion unit can also suggest optimal activities based on the user's travel period. This makes it possible to provide optimal suggestion priorities according to the time when the activities will be performed. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can cause AI to evaluate the time when the activities should be performed and determine the order of suggestions.

[0044] The suggestion unit can change the order of suggestions based on the relevance of the activities when suggesting activities. For example, the suggestion unit can adjust the order of suggestions based on the relevance of the activities when suggesting activities. For example, the suggestion unit can first suggest an activity that is most relevant to the user's interests. The suggestion unit can also preferentially suggest highly relevant activities based on the user's past selection history. The suggestion unit can also suggest highly relevant activities based on the user's current interests. This makes it possible to provide an optimal order of suggestions based on the relevance of the activities. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can cause AI to evaluate the relevance of activities and adjust the order of suggestions.

[0045] The suggestion unit may change the use of technical terminology in the proposal depending on the user's level of expertise when making a proposal. For example, the suggestion unit may adjust the use of technical terminology in the proposal depending on the user's level of expertise when making a proposal. For example, if the user is a beginner, the suggestion unit may avoid technical terminology and make an easy-to-understand proposal. If the user is an intermediate user, the suggestion unit may use appropriate technical terminology to make a proposal. If the user is an advanced user, the suggestion unit may use a lot of technical terminology to make a detailed proposal. This makes it possible to provide an optimal proposal depending on the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may have AI evaluate the user's level of expertise and adjust the technical terminology in the proposal.

[0046] The safety unit can analyze the user's past travel history to select appropriate safety measures when implementing safety measures. For example, the safety unit can analyze the user's past travel history to select optimal safety measures when implementing safety measures. For example, the safety unit provides safety information for places the user has visited in the past. The safety unit can also suggest measures against specific risks based on the user's past travel history. The safety unit can also customize safety measures based on the user's past feedback. This makes it possible to provide optimal safety measures based on the user's past travel history. Some or all of the above-described processing in the safety unit may be performed using, for example, AI, or may be performed without using AI. For example, the safety unit can cause AI to analyze the user's past travel history.

[0047] The safety unit can adjust the safety measures based on the user's current living situation when implementing safety measures. For example, the safety unit customizes the safety measures based on the user's current living situation when implementing safety measures. For example, the safety unit can provide special safety measures if the user is elderly. The safety unit can also provide family-oriented safety measures if the user is accompanied by children. The safety unit can also provide special medical support if the user has health problems. This makes it possible to provide optimal safety measures according to the user's current living situation. Some or all of the above-described processing in the safety unit may be performed using AI, for example, or may be performed without using AI. For example, the safety unit can cause AI to perform an analysis of the user's current living situation.

[0048] The safety unit can change the method of safety measures by reflecting user feedback when implementing safety measures. For example, the safety unit can improve the method of safety measures by reflecting user feedback when implementing safety measures. For example, the safety unit can improve the procedures of safety measures based on feedback previously provided by the user. The safety unit can also collect user feedback in real time and immediately reflect it in safety measures. The safety unit can also analyze user feedback, identify common problems, and take improvement measures. This makes it possible to provide optimal safety measures based on user feedback. Some or all of the above-mentioned processing in the safety unit can be performed using AI, for example, or can be performed without using AI. For example, the safety unit can cause AI to analyze user feedback.

[0049] The safety unit can select appropriate safety measures based on the user's geographical location information when implementing safety measures. For example, the safety unit selects optimal safety measures taking the user's geographical location information into consideration when implementing safety measures. For example, the safety unit provides safety measures based on public safety information for the user's current location. The safety unit can also provide safety information for places the user plans to visit. The safety unit can also suggest optimal evacuation routes based on the user's current location. This makes it possible to provide optimal safety measures based on the user's geographical location information. Some or all of the above-described processing in the safety unit may be performed using, for example, AI, or may be performed without using AI. For example, the safety unit can cause AI to analyze the user's geographical location information.

[0050] The safety unit can analyze the user's social media activity and suggest safety measures when implementing safety measures. For example, the safety unit can analyze the user's social media activity and suggest safety measures when implementing safety measures. For example, the safety unit can provide safety information for locations where the user has checked in on social media. The safety unit can also analyze the content of the user's social media posts and suggest related safety measures. The safety unit can also suggest safety measures based on the user's social media activities. This makes it possible to provide optimal safety measures based on the user's social media activities. Some or all of the above-described processing in the safety unit can be performed using, for example, AI, or can be performed without using AI. For example, the safety unit can have AI perform the analysis of the user's social media activities.

[0051] The safety unit can adjust the method of safety measures by reflecting the user's past feedback when implementing safety measures. For example, the safety unit can customize the method of safety measures by reflecting the user's past feedback when implementing safety measures. For example, the safety unit can customize the safety measure procedure based on feedback provided by the user in the past. The safety unit can also collect user feedback in real time and immediately reflect it in the safety measures. The safety unit can also analyze user feedback, identify common problems, and take improvement measures. This makes it possible to provide optimal safety measures based on the user's past feedback. Some or all of the above-mentioned processing in the safety unit may be performed using AI, for example, or may be performed without using AI. For example, the safety unit can cause AI to analyze the user's feedback.

[0052] The support unit can analyze the user's past travel history and select an appropriate reservation method when making a reservation or planning. For example, the support unit analyzes the user's past travel history and selects the optimal reservation method when making a reservation or planning. For example, the support unit prioritizes suggesting accommodations that the user has used in the past. The support unit can also suggest the optimal means of transportation based on the user's past travel history. The support unit can also customize the reservation method based on the user's past feedback. This makes it possible to provide the optimal reservation method based on the user's past travel history. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can cause AI to analyze the user's past travel history.

[0053] The support unit can adjust the reservation or planning method based on the user's current living situation when making a reservation or planning. For example, the support unit customizes the reservation or planning method based on the user's current living situation when making a reservation or planning. For example, the support unit can provide special support if the user is elderly. The support unit can also suggest a family plan if the user is traveling with children. The support unit can also provide special medical support if the user has health problems. This makes it possible to provide the optimal reservation or planning method according to the user's current living situation. Some or all of the above-mentioned processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can cause AI to analyze the user's current living situation.

[0054] The support unit can change the reservation and planning methods by reflecting user feedback during reservation and planning. For example, the support unit can improve the reservation and planning methods by reflecting user feedback during reservation and planning. For example, the support unit can improve the reservation method based on feedback previously provided by the user. The support unit can also collect user feedback in real time and immediately reflect it in the reservation method. The support unit can also analyze user feedback, identify common problems, and take measures to improve them. This makes it possible to provide an optimal reservation and planning method based on user feedback. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can have AI analyze user feedback.

[0055] The support unit can select an appropriate reservation method based on the user's geographical location information when making a reservation or planning. For example, the support unit selects the optimal reservation method by taking the user's geographical location information into consideration when making a reservation or planning. For example, the support unit preferentially suggests accommodations close to the user's current location. The support unit can also suggest transportation options near places the user plans to visit. The support unit can also suggest optimal tourist spots based on the user's current location. This makes it possible to provide the optimal reservation method based on the user's geographical location information. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can cause AI to analyze the user's geographical location information.

[0056] The support unit can analyze the user's social media activity and suggest reservation and planning methods when making reservations or planning. For example, the support unit can analyze the user's social media activity and suggest reservation and planning methods when making reservations or planning. For example, the support unit can suggest accommodations near the location where the user checked in on social media. The support unit can also analyze the user's social media posts and suggest related transportation methods. The support unit can also suggest related tourist spots based on the activity of the user's friends on social media. This makes it possible to provide optimal reservation and planning methods based on the user's social media activity. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can cause AI to analyze the user's social media activity.

[0057] The support unit can adjust the reservation and planning methods by reflecting the user's past feedback when making a reservation or planning. For example, the support unit customizes the reservation and planning methods by reflecting the user's past feedback when making a reservation or planning. For example, the support unit customizes the reservation method based on feedback provided by the user in the past. The support unit can also collect user feedback in real time and immediately reflect it in the reservation method. The support unit can also analyze user feedback, identify common problems, and take measures to improve them. This makes it possible to provide an optimal reservation and planning method based on the user's past feedback. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can have AI analyze the user's feedback.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The suggestion unit can analyze the user's past travel history and make new suggestions based on the places the user has visited and the activities the user has participated in. For example, it can suggest new tourist spots near cities the user has visited in the past. It can also suggest new experiences related to activities the user has participated in in the past. It can also suggest similar travel plans based on the user's preferred travel styles in the past. This makes it possible to provide optimal suggestions based on the user's past travel history.

[0060] The support unit can provide health management support during travel, taking into account the user's current health condition. For example, if the user has a specific health problem, the support unit can provide information on medical facilities that can address that problem. Also, if the user needs a specific medication, the support unit can guide the user to a place where that medication can be obtained. Furthermore, if the user requests a travel plan that takes into account their health condition, the support unit can suggest a plan based on that request. This makes it possible to provide optimal support according to the user's health condition.

[0061] The reception unit can suggest travel destinations and activities taking into account the user's current weather information. For example, if the user is planning a trip on a rainy day, the reception unit can suggest indoor activities that can be enjoyed. Also, if the user is planning a trip on a sunny day, the reception unit can suggest outdoor activities that can be enjoyed. Furthermore, if the user desires specific weather conditions, the reception unit can suggest destinations that match those conditions. This makes it possible to provide optimal suggestions based on the user's current weather information.

[0062] The support unit can analyze the user's past travel history and suggest new reservation methods based on the accommodations and transportation methods the user has used in the past. For example, it can prioritize suggestions of hotel chains that the user has used in the past. It can also prioritize suggestions of airlines that the user has used in the past. It can also suggest similar reservation methods based on the travel styles that the user has preferred in the past. This makes it possible to provide the optimal reservation method based on the user's past travel history.

[0063] The support unit can propose an optimal travel plan taking into account the user's current geographical location information. For example, the support unit can propose tourist spots close to the user's current location. It can also propose transportation methods that are easily accessible from the user's current location. It can also propose day trip plans that the user can visit from their current location. This makes it possible to provide an optimal travel plan based on the user's current geographical location information.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit accepts input of the user's travel destination and desired activities. The user's travel destinations include, but are not limited to, domestic travel, international travel, urban areas, natural areas, etc. The desired activities include, but are not limited to, sightseeing, sports, cultural experiences, relaxation, etc. The reception unit accepts, for example, input of the user's wishes such as "I want to enjoy nature" or "I want to visit historical places." Step 2: The suggestion unit uses AI to analyze the information received by the reception unit and suggests specific examples of activities based on the user's preferences. The suggestion unit performs analysis using, for example, text analysis, data mining, or machine learning algorithms. Based on the user's preferences, the suggestion unit suggests, for example, a tour of remote areas led by a local guide or a workshop where the user can experience local culture. The suggestion unit suggests specific examples of activities such as hiking, visiting museums, or local cooking classes. Step 3: The Safety Department provides appropriate safety measures and responsible support based on the activities proposed by the Proposal Department. The Safety Department provides appropriate safety measures, such as providing insurance, presenting safety guidelines, and providing emergency contact information. The Safety Department provides responsible support, such as a 24-hour support center and on-site staffing. Step 4: The Support Department smoothly performs reservations and planning based on the support provided by the Safety Department. For example, the Support Department provides a function that allows reservations for accommodations and transportation to be made in bulk. The Support Department provides reservation and planning methods, such as an online reservation system and automatic generation of travel plans.

[0066] (Example 2) A travel support system according to an embodiment of the present invention accepts inputs of a user's travel destination and desired activities. It uses AI to suggest special activities that allow users to enjoy hidden spots and local culture based on the user's preferences, provides top-notch safety measures, and ensures responsible support, facilitating smooth booking and planning. For example, a user inputs their travel destination and desired activities. For example, they may input preferences such as "I want to enjoy nature" or "I want to visit historical sites." This information is then entered into the system. The travel support system then uses AI to suggest special activities that allow users to enjoy hidden spots and local culture based on the user's preferences. For example, for a user who wants to enjoy nature, the system suggests a tour of remote areas led by a local guide or a workshop where users can experience local culture. This allows users to enjoy special experiences tailored to their interests. Furthermore, the travel support system provides top-notch safety measures and ensures responsible support. For example, by providing a support system for emergencies during travel and local safety information, users can enjoy their trip with peace of mind. The travel support system also provides solutions for travel troubles, allowing users to enjoy their trip with peace of mind. The travel support system also provides smooth support for booking and planning. For example, it provides a function that allows users to make reservations for accommodations and transportation all at once, and a function that automatically creates a travel schedule. This allows users to prepare for their trip without any hassle. In this way, the travel support system can support all the processes of a trip so that users can have an inspiring experience. In this way, the travel support system can support all the processes of a trip so that users can have an inspiring experience. For example, users can enjoy special experiences that match their interests and wishes, and can enjoy their trip with peace of mind.

[0067] A travel support system according to an embodiment includes a reception unit, a suggestion unit, a safety unit, and a support unit. The reception unit accepts input of a user's travel destination and desired activities. Examples of the user's travel destination include, but are not limited to, domestic travel, international travel, urban areas, and natural areas. Examples of desired activities include, but are not limited to, sightseeing, sports, cultural experiences, and relaxation. The reception unit accepts input of the user's preferences, such as "I want to enjoy nature" or "I want to visit historical sites." The suggestion unit uses AI to analyze the information accepted by the reception unit and suggests specific examples of activities based on the user's preferences. The suggestion unit performs analysis using, for example, text analysis, data mining, and machine learning algorithms. The suggestion unit suggests, for example, a remote tour led by a local guide or a workshop where the user can experience local culture based on the user's preferences. The suggestion unit suggests specific examples of activities, such as hiking, visiting museums, and local cooking classes. The safety unit provides appropriate safety measures and responsible support based on the activities suggested by the suggestion unit. The safety department provides appropriate safety measures, such as providing insurance, presenting safety guidelines, and providing emergency contact information. The safety department provides responsible support, such as a 24-hour support center and deploying local staff. The support department smoothly performs reservations and planning based on the support provided by the safety department. The support department provides, for example, a function for making reservations for accommodations and transportation in one go. The support department provides reservation and planning methods, such as an online reservation system and automatic generation of travel plans. As a result, the travel support system according to the embodiment can efficiently support the user's travel experience and provide an exciting experience.

[0068] The suggestion unit can suggest a remote tour guided by a local guide or a workshop where the user can experience local culture based on the user's preference. The suggestion unit, for example, suggests a remote tour guided by a local guide based on the user's preference. For example, the suggestion unit suggests a tour that takes the user to unexplored natural areas or places that are not tourist destinations. The suggestion unit can also suggest a workshop where the user can experience local culture based on the user's preference. For example, the suggestion unit suggests a traditional craft experience or a cooking class for local cuisine. This makes it possible to provide a special experience that matches the user's interests. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can cause AI to select the activities to suggest based on the user's preference.

[0069] The safety department can provide a support system for emergencies during travel or local safety information. The safety department, for example, provides a support system for emergencies during travel. For example, the safety department provides emergency contact information and a 24-hour support center. The safety department can also provide local safety information. For example, the safety department provides public safety information, information on medical facilities, evacuation routes, etc. This allows users to enjoy their trip with peace of mind. Some or all of the above-mentioned processing in the safety department may be performed using AI, or may be performed without using AI. For example, the safety department can have AI collect and provide local safety information.

[0070] The support unit can provide a function for making reservations for accommodations or transportation in one go. The support unit, for example, provides a function for making reservations for accommodations in one go. For example, the support unit makes reservations for accommodations such as hotels, guesthouses, and resorts in one go. The support unit can also provide a function for making reservations for transportation in one go. For example, the support unit makes reservations for transportation such as airplanes, trains, buses, and rental cars in one go. This allows the user to prepare for a trip without hassle. Some or all of the above-mentioned processing in the support unit may be performed using AI, or may be performed without using AI. For example, the support unit can have AI make reservations for accommodations and transportation.

[0071] The support unit can provide a function to automatically create a travel schedule. The support unit provides, for example, a function to automatically create a travel schedule. For example, the support unit automatically generates itineraries and customizes them based on the user's wishes. This allows the user to efficiently manage their travel schedule. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can cause AI to automatically generate a travel schedule.

[0072] The reception unit can estimate the user's emotions and adjust the input method for the travel destination or desired activity based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and adjust the input method for the travel destination or desired activity based on the estimated user emotions. For example, if the user is excited, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is stressed, the reception unit can provide a simple interface and minimize the input steps. Furthermore, if the user is relaxed, the reception unit can provide various input methods, such as voice input or image input. This makes it possible to provide the optimal input method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can cause an AI to estimate the user's emotions.

[0073] The reception unit can analyze the user's past travel history and select an appropriate input method. The reception unit, for example, analyzes the user's past travel history and selects the optimal input method. For example, the reception unit automatically displays places the user has frequently visited in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also provide input options related to specific seasons or events from the user's past travel history. This makes it possible to provide the optimal input method based on the user's past travel history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can cause AI to analyze the user's past travel history.

[0074] The reception unit can filter based on the user's current interests when inputting a travel destination or a desired activity. For example, the reception unit can filter based on the user's current interests when inputting a travel destination or a desired activity. For example, the reception unit can preferentially display related activities based on keywords recently searched by the user. The reception unit can also suggest related destinations based on posts that the user has "liked" on social media. The reception unit can also filter related activities based on events the user has recently participated in. This makes it possible to provide optimal suggestions based on the user's current interests. Some or all of the above-described processing in the reception unit can be performed using, or without, AI. For example, the reception unit can cause AI to filter based on the user's current interests.

[0075] The reception unit can select an appropriate input means according to the user's input method when inputting a travel destination or a desired activity. For example, when inputting a travel destination or a desired activity, the reception unit selects the optimal input means according to the user's input method (voice, text, image, etc.). For example, if the user selects voice input, the reception unit supports the input using voice recognition technology. Furthermore, if the user selects image input, the reception unit can also identify the destination or activity using image analysis technology. Furthermore, if the user selects text input, the reception unit can also analyze the input content using natural language processing technology. This makes it possible to provide the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can cause AI to select the input means according to the user's input method.

[0076] The reception unit can estimate the user's emotions and determine the order of information to be input based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and determine the priority of information to be input based on the estimated user emotions. For example, the reception unit can prioritize detailed information input when the user is excited. Furthermore, the reception unit can also prompt the user to input only the minimum amount of information necessary when the user is stressed. Furthermore, the reception unit can provide customizable input options when the user is relaxed. This allows optimal information prioritization according to the user's emotions to be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using AI, or without AI. For example, the reception unit can cause AI to estimate the user's emotions.

[0077] The reception unit can prioritize input of highly relevant information based on the user's geographical location information when inputting a travel destination or a desired activity. For example, when inputting a travel destination or a desired activity, the reception unit prioritizes input of highly relevant information taking into account the user's geographical location information. For example, the reception unit prioritizes suggesting locations close to the user's current location. The reception unit can also suggest activities near places the user has previously visited. The reception unit can also suggest optimal transportation methods based on the user's current location. This makes it possible to provide optimal information based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can cause AI to analyze the user's geographical location information.

[0078] The reception unit can analyze the user's social media activity and input appropriate information when the user inputs a travel destination or a desired activity. For example, the reception unit can analyze the user's social media activity and input relevant information when the user inputs a travel destination or a desired activity. For example, the reception unit can suggest locations where the user has checked in on social media as candidate locations. The reception unit can also analyze the content of the user's social media posts and suggest related activities. The reception unit can also suggest related locations based on the activity of the user's friends on social media. This makes it possible to provide optimal information based on the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can cause AI to analyze the user's social media activity.

[0079] The reception unit can adjust the input method by reflecting the user's past feedback when inputting a travel destination or a desired activity. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting a travel destination or a desired activity. For example, the reception unit preferentially suggests input methods that the user has previously preferred. The reception unit can also customize the input interface based on the user's past feedback. The reception unit can also adjust the input interface to avoid input methods that the user has previously dissatisfied with. This makes it possible to provide an optimal input method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can cause AI to analyze the user's past feedback.

[0080] The suggestion unit can estimate the user's emotion and change the way the suggestion is expressed based on the estimated user's emotion. For example, the suggestion unit estimates the user's emotion and adjusts the way the suggestion is expressed based on the estimated user's emotion. For example, if the user is excited, the suggestion unit can make a visually stimulating suggestion. If the user is relaxed, the suggestion unit can make a suggestion in a calm tone. If the user is stressed, the suggestion unit can make a simple and easy-to-understand suggestion. This makes it possible to provide an optimal way to express the suggestion according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using an AI, or may be performed without using an AI. For example, the suggestion unit can cause an AI to estimate the user's emotion.

[0081] The suggestion unit can change the level of detail of the suggestion based on the importance of the activity when making the suggestion. For example, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the activity when making the suggestion. For example, the suggestion unit can provide a detailed explanation for an activity with high importance. The suggestion unit can also provide a concise explanation for an activity with low importance. The suggestion unit can also adjust the level of detail of the activity based on the user's interest level. This makes it possible to provide an optimal level of detail of the suggestion according to the importance of the activity. Some or all of the above-described processing in the suggestion unit can be performed using, or without, AI. For example, the suggestion unit can cause AI to evaluate the importance of the activity and adjust the level of detail of the suggestion.

[0082] The suggestion unit can apply an appropriate suggestion algorithm depending on the activity category when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the activity category when making a suggestion. For example, the suggestion unit can make suggestions that emphasize environmental information for nature-related activities. The suggestion unit can also make suggestions that emphasize history and background information for cultural activities. The suggestion unit can also make suggestions that emphasize thrills and challenges for adventure-related activities. This makes it possible to provide an optimal suggestion algorithm depending on the activity category. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can cause AI to apply the suggestion algorithm depending on the activity category.

[0083] The suggestion unit can improve the accuracy of suggestions based on the user's past suggestion results when making suggestions. For example, the suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results when making suggestions. For example, the suggestion unit can suggest similar activities based on suggestions that the user has liked in the past. The suggestion unit can also adjust to avoid suggestions that the user has rejected in the past. The suggestion unit can also improve the suggestion algorithm based on the user's past feedback. This makes it possible to provide optimal suggestions based on the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can cause AI to analyze the user's past suggestion results and improve the suggestion algorithm.

[0084] The suggestion unit can estimate the user's emotion and change the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit estimates the user's emotion and adjusts the length of the suggestion based on the estimated user's emotion. For example, if the user is in a hurry, the suggestion unit can provide a short, to-the-point suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. If the user is excited, the suggestion unit can provide a suggestion with visually stimulating effects. This makes it possible to provide an optimal suggestion length according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit may be performed using an AI, or may be performed without using an AI. For example, the suggestion unit can cause an AI to estimate the user's emotion.

[0085] The suggestion unit, when making suggestions, can determine the order of suggestions based on the time when the activities will be performed. For example, the suggestion unit, when making suggestions, can determine the priority of suggestions based on the time when the activities will be performed. For example, the suggestion unit can prioritize seasonal activities. The suggestion unit can also prioritize activities during specific event periods. The suggestion unit can also suggest optimal activities based on the user's travel period. This makes it possible to provide optimal suggestion priorities according to the time when the activities will be performed. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can cause AI to evaluate the time when the activities should be performed and determine the order of suggestions.

[0086] The suggestion unit can change the order of suggestions based on the relevance of the activities when suggesting activities. For example, the suggestion unit can adjust the order of suggestions based on the relevance of the activities when suggesting activities. For example, the suggestion unit can first suggest an activity that is most relevant to the user's interests. The suggestion unit can also preferentially suggest highly relevant activities based on the user's past selection history. The suggestion unit can also suggest highly relevant activities based on the user's current interests. This makes it possible to provide an optimal order of suggestions based on the relevance of the activities. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can cause AI to evaluate the relevance of activities and adjust the order of suggestions.

[0087] The suggestion unit may change the use of technical terminology in the proposal depending on the user's level of expertise when making a proposal. For example, the suggestion unit may adjust the use of technical terminology in the proposal depending on the user's level of expertise when making a proposal. For example, if the user is a beginner, the suggestion unit may avoid technical terminology and make an easy-to-understand proposal. If the user is an intermediate user, the suggestion unit may use appropriate technical terminology to make a proposal. If the user is an advanced user, the suggestion unit may use a lot of technical terminology to make a detailed proposal. This makes it possible to provide an optimal proposal depending on the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may have AI evaluate the user's level of expertise and adjust the technical terminology in the proposal.

[0088] The safety unit can estimate the user's emotions and change the method of safety measures based on the estimated user emotions. The safety unit, for example, estimates the user's emotions and adjusts the method of safety measures based on the estimated user emotions. For example, the safety unit can provide detailed safety measure information when the user is nervous. The safety unit can also provide concise safety measure information when the user is relaxed. The safety unit can also provide visually easy-to-understand safety measure information when the user is excited. This makes it possible to provide optimal safety measures according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the safety unit may be performed using an AI, for example, or without using an AI. For example, the safety unit can cause an AI to estimate the user's emotions.

[0089] The safety unit can analyze the user's past travel history to select appropriate safety measures when implementing safety measures. For example, the safety unit can analyze the user's past travel history to select optimal safety measures when implementing safety measures. For example, the safety unit provides safety information for places the user has visited in the past. The safety unit can also suggest measures against specific risks based on the user's past travel history. The safety unit can also customize safety measures based on the user's past feedback. This makes it possible to provide optimal safety measures based on the user's past travel history. Some or all of the above-described processing in the safety unit may be performed using, for example, AI, or may be performed without using AI. For example, the safety unit can cause AI to analyze the user's past travel history.

[0090] The safety unit can adjust the safety measures based on the user's current living situation when implementing safety measures. For example, the safety unit customizes the safety measures based on the user's current living situation when implementing safety measures. For example, the safety unit can provide special safety measures if the user is elderly. The safety unit can also provide family-oriented safety measures if the user is accompanied by children. The safety unit can also provide special medical support if the user has health problems. This makes it possible to provide optimal safety measures according to the user's current living situation. Some or all of the above-described processing in the safety unit may be performed using AI, for example, or may be performed without using AI. For example, the safety unit can cause AI to perform an analysis of the user's current living situation.

[0091] The safety unit can change the method of safety measures by reflecting user feedback when implementing safety measures. For example, the safety unit can improve the method of safety measures by reflecting user feedback when implementing safety measures. For example, the safety unit can improve the procedures of safety measures based on feedback previously provided by the user. The safety unit can also collect user feedback in real time and immediately reflect it in safety measures. The safety unit can also analyze user feedback, identify common problems, and take improvement measures. This makes it possible to provide optimal safety measures based on user feedback. Some or all of the above-mentioned processing in the safety unit can be performed using AI, for example, or can be performed without using AI. For example, the safety unit can cause AI to analyze user feedback.

[0092] The safety unit can estimate the user's emotions and determine the order of safety measures based on the estimated user emotions. The safety unit, for example, estimates the user's emotions and determines the priority of safety measures based on the estimated user emotions. For example, if the user is nervous, the safety unit can prioritize the most important safety measures. If the user is relaxed, the safety unit can also prioritize general safety measures. If the user is excited, the safety unit can also prioritize visually easy-to-understand safety measures. This makes it possible to provide optimal safety measure priorities according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the safety unit may be performed using an AI, for example, or without using an AI. For example, the safety unit can cause an AI to estimate the user's emotions.

[0093] The safety unit can select appropriate safety measures based on the user's geographical location information when implementing safety measures. For example, the safety unit selects optimal safety measures taking the user's geographical location information into consideration when implementing safety measures. For example, the safety unit provides safety measures based on public safety information for the user's current location. The safety unit can also provide safety information for places the user plans to visit. The safety unit can also suggest optimal evacuation routes based on the user's current location. This makes it possible to provide optimal safety measures based on the user's geographical location information. Some or all of the above-described processing in the safety unit may be performed using, for example, AI, or may be performed without using AI. For example, the safety unit can cause AI to analyze the user's geographical location information.

[0094] The safety unit can analyze the user's social media activity and suggest safety measures when implementing safety measures. For example, the safety unit can analyze the user's social media activity and suggest safety measures when implementing safety measures. For example, the safety unit can provide safety information for locations where the user has checked in on social media. The safety unit can also analyze the content of the user's social media posts and suggest related safety measures. The safety unit can also suggest safety measures based on the user's social media activities. This makes it possible to provide optimal safety measures based on the user's social media activities. Some or all of the above-described processing in the safety unit can be performed using, for example, AI, or can be performed without using AI. For example, the safety unit can have AI perform the analysis of the user's social media activities.

[0095] The safety unit can adjust the method of safety measures by reflecting the user's past feedback when implementing safety measures. For example, the safety unit can customize the method of safety measures by reflecting the user's past feedback when implementing safety measures. For example, the safety unit can customize the safety measure procedure based on feedback provided by the user in the past. The safety unit can also collect user feedback in real time and immediately reflect it in the safety measures. The safety unit can also analyze user feedback, identify common problems, and take improvement measures. This makes it possible to provide optimal safety measures based on the user's past feedback. Some or all of the above-mentioned processing in the safety unit may be performed using AI, for example, or may be performed without using AI. For example, the safety unit can cause AI to analyze the user's feedback.

[0096] The support unit can estimate a user's emotions and adjust a reservation or planning method based on the estimated user emotions. For example, the support unit can estimate a user's emotions and adjust a reservation or planning method based on the estimated user emotions. For example, if the user is nervous, the support unit can provide a simple and easy-to-understand reservation method. If the user is relaxed, the support unit can provide a reservation method with detailed options. If the user is excited, the support unit can provide a visually appealing reservation method. This makes it possible to provide an optimal reservation or planning method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can cause AI to estimate the user's emotions.

[0097] The support unit can analyze the user's past travel history and select an appropriate reservation method when making a reservation or planning. For example, the support unit analyzes the user's past travel history and selects the optimal reservation method when making a reservation or planning. For example, the support unit prioritizes suggesting accommodations that the user has used in the past. The support unit can also suggest the optimal means of transportation based on the user's past travel history. The support unit can also customize the reservation method based on the user's past feedback. This makes it possible to provide the optimal reservation method based on the user's past travel history. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can cause AI to analyze the user's past travel history.

[0098] The support unit can adjust the reservation or planning method based on the user's current living situation when making a reservation or planning. For example, the support unit customizes the reservation or planning method based on the user's current living situation when making a reservation or planning. For example, the support unit can provide special support if the user is elderly. The support unit can also suggest a family plan if the user is traveling with children. The support unit can also provide special medical support if the user has health problems. This makes it possible to provide the optimal reservation or planning method according to the user's current living situation. Some or all of the above-mentioned processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can cause AI to analyze the user's current living situation.

[0099] The support unit can change the reservation and planning methods by reflecting user feedback during reservation and planning. For example, the support unit can improve the reservation and planning methods by reflecting user feedback during reservation and planning. For example, the support unit can improve the reservation method based on feedback previously provided by the user. The support unit can also collect user feedback in real time and immediately reflect it in the reservation method. The support unit can also analyze user feedback, identify common problems, and take measures to improve them. This makes it possible to provide an optimal reservation and planning method based on user feedback. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can have AI analyze user feedback.

[0100] The support unit can estimate a user's emotions and determine the priority of reservations or planning based on the estimated user emotions. For example, the support unit can estimate a user's emotions and determine the priority of reservations or planning based on the estimated user emotions. For example, if a user is nervous, the support unit can prioritize the most important reservations. Furthermore, if a user is relaxed, the support unit can also prioritize general reservations. Furthermore, if a user is excited, the support unit can also prioritize visually appealing reservations. This makes it possible to provide optimal reservation and planning priorities according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the support unit can cause an AI to estimate the user's emotions.

[0101] The support unit can select an appropriate reservation method based on the user's geographical location information when making a reservation or planning. For example, the support unit selects the optimal reservation method by taking the user's geographical location information into consideration when making a reservation or planning. For example, the support unit preferentially suggests accommodations close to the user's current location. The support unit can also suggest transportation options near places the user plans to visit. The support unit can also suggest optimal tourist spots based on the user's current location. This makes it possible to provide the optimal reservation method based on the user's geographical location information. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can cause AI to analyze the user's geographical location information.

[0102] The support unit can analyze the user's social media activity and suggest reservation and planning methods when making reservations or planning. For example, the support unit can analyze the user's social media activity and suggest reservation and planning methods when making reservations or planning. For example, the support unit can suggest accommodations near the location where the user checked in on social media. The support unit can also analyze the user's social media posts and suggest related transportation methods. The support unit can also suggest related tourist spots based on the activity of the user's friends on social media. This makes it possible to provide optimal reservation and planning methods based on the user's social media activity. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can cause AI to analyze the user's social media activity.

[0103] The support unit can adjust the reservation and planning methods by reflecting the user's past feedback when making a reservation or planning. For example, the support unit customizes the reservation and planning methods by reflecting the user's past feedback when making a reservation or planning. For example, the support unit customizes the reservation method based on feedback provided by the user in the past. The support unit can also collect user feedback in real time and immediately reflect it in the reservation method. The support unit can also analyze user feedback, identify common problems, and take measures to improve them. This makes it possible to provide an optimal reservation and planning method based on the user's past feedback. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can have AI analyze the user's feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, suggestion unit, safety 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 reception device 38 of the smart device 14 and accepts input of the user's travel destination and desired activities. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses AI to suggest activities based on the user's preferences. The safety unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate safety measures and responsible support. The support unit is realized, for example, by the control unit 46A of the smart device 14 and provides functions for smooth reservation and planning. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, suggestion unit, safety unit, and support unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and accepts input of the user's travel destination and desired activities. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses AI to suggest activities based on the user's preferences. The safety unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate safety measures and responsible support. The support unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides functions for smooth reservation and planning. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, suggestion unit, safety unit, and support unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and accepts input of the user's travel destination and desired activities. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses AI to suggest activities based on the user's preferences. The safety unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate safety measures and responsible support. The support unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides functions for smooth reservation and planning. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, suggestion unit, safety unit, and support unit described above 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 microphone 238 of the robot 414 and accepts input of the user's travel destination and desired activities. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and uses AI to suggest activities based on the user's preferences. The safety unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides appropriate safety measures and responsible support. The support unit is realized, for example, by the control unit 46A of the robot 414 and provides functions for smooth reservation and planning.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can select the timing to make detailed suggestions. If the user is stressed, the suggestion unit can also select the timing to make concise suggestions. Furthermore, if the user is excited, the suggestion unit can also select the timing to make visually appealing suggestions. This makes it possible to provide optimal suggestion timing according to the user's emotions.

[0106] The suggestion unit can analyze the user's past travel history and make new suggestions based on the places the user has visited and the activities the user has participated in. For example, it can suggest new tourist spots near cities the user has visited in the past. It can also suggest new experiences related to activities the user has participated in in the past. It can also suggest similar travel plans based on the user's preferred travel styles in the past. This makes it possible to provide optimal suggestions based on the user's past travel history.

[0107] The safety unit can estimate the user's emotions and adjust the emergency response method based on the estimated user's emotions. For example, if the user is nervous, the safety unit can provide detailed emergency response procedures. If the user is relaxed, the safety unit can provide concise emergency response procedures. Furthermore, if the user is excited, the safety unit can provide visually easy-to-understand emergency response procedures. In this way, it is possible to provide the optimal emergency response method according to the user's emotions.

[0108] The support unit can provide health management support during travel, taking into account the user's current health condition. For example, if the user has a specific health problem, the support unit can provide information on medical facilities that can address that problem. Also, if the user needs a specific medication, the support unit can guide the user to a place where that medication can be obtained. Furthermore, if the user requests a travel plan that takes into account their health condition, the support unit can suggest a plan based on that request. This makes it possible to provide optimal support according to the user's health condition.

[0109] The suggestion unit can estimate the user's emotions and customize the content of suggestions based on the estimated user emotions. For example, if the user is excited, the suggestion unit can suggest adventurous activities. If the user is relaxed, the suggestion unit can also suggest activities that emphasize relaxation. Furthermore, if the user is stressed, the suggestion unit can also suggest activities that help relieve stress. In this way, it is possible to provide optimal suggestions according to the user's emotions.

[0110] The reception unit can suggest travel destinations and activities taking into account the user's current weather information. For example, if the user is planning a trip on a rainy day, the reception unit can suggest indoor activities that can be enjoyed. Also, if the user is planning a trip on a sunny day, the reception unit can suggest outdoor activities that can be enjoyed. Furthermore, if the user desires specific weather conditions, the reception unit can suggest destinations that match those conditions. This makes it possible to provide optimal suggestions based on the user's current weather information.

[0111] The suggestion unit can estimate the user's emotions and adjust the frequency of suggestions based on the estimated user's emotions. For example, if the user is excited, the suggestion unit can make new suggestions frequently. If the user is relaxed, the suggestion unit can make suggestions at an appropriate frequency. Furthermore, if the user is stressed, the suggestion unit can reduce the frequency of suggestions. This makes it possible to provide an optimal frequency of suggestions according to the user's emotions.

[0112] The support unit can analyze the user's past travel history and suggest new reservation methods based on the accommodations and transportation methods the user has used in the past. For example, it can prioritize suggestions of hotel chains that the user has used in the past. It can also prioritize suggestions of airlines that the user has used in the past. It can also suggest similar reservation methods based on the travel styles that the user has preferred in the past. This makes it possible to provide the optimal reservation method based on the user's past travel history.

[0113] The safety unit can estimate the user's emotions and customize the content of safety measures based on the estimated user's emotions. For example, if the user is nervous, the safety unit can provide detailed safety measure information. If the user is relaxed, the safety unit can provide concise safety measure information. Furthermore, if the user is excited, the safety unit can provide visually easy-to-understand safety measure information. This makes it possible to provide optimal safety measures according to the user's emotions.

[0114] The support unit can propose an optimal travel plan taking into account the user's current geographical location information. For example, the support unit can propose tourist spots close to the user's current location. It can also propose transportation methods that are easily accessible from the user's current location. It can also propose day trip plans that the user can visit from their current location. This makes it possible to provide an optimal travel plan based on the user's current geographical location information.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The reception unit accepts input of the user's travel destination and desired activities. The user's travel destinations include, but are not limited to, domestic travel, international travel, urban areas, natural areas, etc. The desired activities include, but are not limited to, sightseeing, sports, cultural experiences, relaxation, etc. The reception unit accepts, for example, input of the user's wishes such as "I want to enjoy nature" or "I want to visit historical places." Step 2: The suggestion unit uses AI to analyze the information received by the reception unit and suggests specific examples of activities based on the user's preferences. The suggestion unit performs analysis using, for example, text analysis, data mining, or machine learning algorithms. Based on the user's preferences, the suggestion unit suggests, for example, a tour of remote areas led by a local guide or a workshop where the user can experience local culture. The suggestion unit suggests specific examples of activities such as hiking, visiting museums, or local cooking classes. Step 3: The Safety Department provides appropriate safety measures and responsible support based on the activities proposed by the Proposal Department. The Safety Department provides appropriate safety measures, such as providing insurance, presenting safety guidelines, and providing emergency contact information. The Safety Department provides responsible support, such as a 24-hour support center and on-site staffing. Step 4: The Support Department smoothly performs reservations and planning based on the support provided by the Safety Department. For example, the Support Department provides a function that allows reservations for accommodations and transportation to be made in bulk. The Support Department provides reservation and planning methods, such as an online reservation system and automatic generation of travel plans.

[0117] 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.

[0118] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

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

[0124] The 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.

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

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

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

[0128] Fig. 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.

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

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

[0131] In the 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.

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

[0133] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0135] The data processing system 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

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

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

[0147] In the 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.

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

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0151] The data processing system 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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 input of a user's travel destination and desired activities; a suggestion unit that analyzes the information received by the reception unit and suggests specific examples of activities based on the user's wishes; a safety department that provides appropriate safety measures and responsible support based on the activities proposed by the proposal department; a support unit that smoothly performs reservations and planning based on the support provided by the safety unit. A system characterized by:

2. The proposal unit Based on the user's preferences, we suggest a tour of remote areas led by a local guide or a workshop where they can experience the local culture.

2. The system of claim 1.

3. The safety part is Providing emergency support or local safety information for travellers 2. The system of claim 1.

4. The support portion is Providing the ability to book accommodation or transportation in bulk 2. The system of claim 1.

5. The support portion is Providing the ability to automatically create travel schedules 2. The system of claim 1.

6. The reception unit Inferring a user's emotions and adjusting the input method of a travel destination or a desired activity based on the estimated user's emotions 2. The system of claim 1.

7. The reception unit Analyze the user's past travel history and select the appropriate input method 2. The system of claim 1.

8. The reception unit Filtering based on your current interests or preferences when entering travel destinations or desired activities 2. The system of claim 1.

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