System

Abstract

An object of a system according to an embodiment is to provide a travel plan customized in accordance with preferences and needs of a user.SOLUTION: A system includes a proposal unit, a translation unit, an association unit, a planning unit, and a support unit. The proposal unit analyzes a user's preference and a past travel history and proposes an individually customized travel plan. The translation unit automatically translates the language of the travel destination. The coping unit flexibly copes with an unexpected situation. The planner dynamically adjusts the trip plan according to the user's real-time needs. The support unit provides a plurality of supports to the user during the travel.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] Existing technologies do not adequately customize travel plans or handle unexpected situations, leaving room for improvement.

[0005] The system according to the embodiment aims to provide a travel plan customized to the user's preferences and needs. [Means for solving the problem]

[0006] The system according to the embodiment includes a suggestion unit, a translation unit, a response unit, a planning unit, and a support unit. The suggestion unit analyzes the user's preferences and past travel history to propose individually customized travel plans. The translation unit automatically translates the language of the travel destination. The response unit flexibly responds to unexpected situations. The planning unit dynamically adjusts the travel plan according to the user's real-time needs. The support unit provides multiple types of support to the user during the trip. [Effects of the Invention]

[0007] The system according to the embodiment can provide a customized travel plan according to the preferences and needs of the user. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 is a system that uses generative AI to personalize travel experiences and provide multifaceted support to travelers. As a result, the travel support system can provide multifaceted support to travelers and realize a comfortable and safe travel experience.

[0029] A travel support system according to an embodiment includes a suggestion unit, a translation unit, a response unit, a planning unit, and a support unit. The suggestion unit analyzes a user's preferences and past travel history and proposes an individually customized travel plan. For example, the suggestion unit uses a generation AI to analyze the user's preferences and past travel history and create a travel plan that includes natural parks and hiking trails. The suggestion unit can also use the generation AI to suggest restaurants and tourist spots based on the user's preferences. The suggestion unit can also use the generation AI to suggest accommodations based on the user's preferences. The translation unit automatically translates the language of the travel destination. For example, the translation unit uses the generation AI to translate local restaurant menus into the user's native language. The translation unit can also use the generation AI to translate tourist information into the user's native language. The translation unit can also use the generation AI to translate local transportation information into the user's native language. The response unit flexibly responds to unexpected situations. For example, the response unit uses the generation AI to suggest alternative plans for sudden changes in weather. The response unit can also use the generation AI to propose alternative plans for transportation delays. Furthermore, the response unit can also use the generation AI to propose emergency response measures. The planning unit dynamically adjusts the travel plan according to the user's real-time needs. For example, the planning unit can use the generation AI to instantly create a plan including tourist attractions based on the user's new needs. The planning unit can also use the generation AI to instantly create a plan including activities based on the user's new needs. Furthermore, the planning unit can also use the generation AI to instantly create a plan including restaurants based on the user's new needs. The support unit provides various support to the user during the trip. For example, the support unit can use the generation AI to provide emergency contact information. The support unit can also use the generation AI to provide information on local medical institutions. Furthermore, the support unit can also use the generation AI to provide local tourist information. As a result, the travel support system according to the embodiment can provide multifaceted support to travelers, enabling them to have a comfortable and safe travel experience.

[0030] The suggestion unit can analyze the user's health data and propose travel plans that suit their physical condition. For example, the suggestion unit uses a generation AI to analyze the user's health data and propose travel plans that suit their physical condition. For example, it can propose a reasonable sightseeing route based on the user's number of steps and heart rate. Health data is obtained from a fitness tracker. This makes it possible to provide travel plans that suit the user's physical condition.

[0031] The suggestion unit can analyze the user's social media posts and update the travel plan based on the user's latest interests and concerns. For example, the suggestion unit uses a generation AI to analyze the user's social media posts and update the travel plan based on the user's latest interests and concerns. For example, the suggestion unit suggests tourist spots of interest based on photos and comments recently posted by the user. This allows the travel plan to be updated based on the user's latest interests and concerns.

[0032] The suggestion unit can take into account the travel history of the user's friends or family and propose the optimal travel plan for the entire group. For example, the suggestion unit uses a generation AI to analyze the travel history of the user's friends and family and propose the optimal travel plan for the entire group. For example, it creates a plan that includes activities that everyone can enjoy. This makes it possible to provide the optimal travel plan for the entire group.

[0033] The suggestion unit can analyze reviews or ratings of the user's past travel destinations and suggest travel plans that will provide more satisfaction. For example, the suggestion unit uses a generation AI to analyze reviews and ratings of the user's past travel destinations and suggest travel plans that will provide more satisfaction. For example, it creates a plan that includes highly rated tourist spots and restaurants. This makes it possible to provide travel plans that will provide more satisfaction based on the user's past reviews and ratings.

[0034] The translation unit understands local slang or dialects and can provide more natural-sounding translations. For example, the generative AI can accurately translate local youth slang and regional expressions. This allows the translation unit to understand local slang and dialects and provide more natural-sounding translations.

[0035] The translation unit can learn the user's past translation history and provide individually optimized translations. For example, the generation AI can learn the user's past translation history and provide individually optimized translations. For example, it can prioritize translations of phrases and words that the user uses frequently. This makes it possible to provide individually optimized translations based on the user's past translation history.

[0036] The translation unit can translate information about local culture and customs, allowing users to gain a deeper understanding of the local area. For example, the generative AI can translate information about local culture and customs, allowing users to gain a deeper understanding of the local area. For example, it can translate explanations of local festivals and traditional events. This translates information about local culture and customs, allowing users to gain a deeper understanding of the local area.

[0037] The translation unit translates the user's voice input in real time, making it possible to have smooth conversations with local people. For example, the translation unit uses a generative AI to translate the user's voice input in real time, making it possible to have smooth conversations with local people. For example, it can instantly translate what the user says and respond in the local language. This allows the user's voice input to be translated in real time, making it possible to have smooth conversations with local people.

[0038] The response unit can analyze real-time traffic information and weather forecasts and instantly generate the optimal alternative plan. For example, the generation AI in the response unit analyzes real-time traffic information and weather forecasts and instantly generates the optimal alternative plan. For example, if a traffic jam occurs, it will suggest the optimal detour route. This makes it possible to instantly generate the optimal alternative plan based on real-time traffic information and weather forecasts.

[0039] The response unit can analyze the experiences of other travelers and propose the most successful alternative plan. For example, the response unit uses a generation AI to analyze the experiences of other travelers and propose the most successful alternative plan. For example, the response unit proposes an alternative plan based on alternative plans that other travelers have given high ratings. This makes it possible to provide the most successful alternative plan based on the experiences of other travelers.

[0040] The response unit can analyze the user's current location and surrounding resources and suggest the closest alternative option. For example, the generation AI can analyze the user's current location and surrounding resources and suggest the closest alternative option. For example, it can suggest nearby restaurants or cafes. This allows the closest alternative option to be provided based on the user's current location and surrounding resources.

[0041] The planning unit can analyze the user's health data and propose travel plans tailored to their physical condition. For example, the planning unit uses a generation AI to analyze the user's health data and propose travel plans tailored to their physical condition. For example, it can propose a reasonable sightseeing route based on the user's number of steps and heart rate. Health data is obtained from a fitness tracker. This makes it possible to provide travel plans tailored to the user's physical condition based on their health data.

[0042] The planning unit can analyze the user's social media posts and update the travel plan based on their latest interests and concerns. For example, the planning unit uses a generative AI to analyze the user's social media posts and update the travel plan based on their latest interests and concerns. For example, the planning unit suggests tourist spots of interest based on photos and comments recently posted by the user. This makes it possible to provide a travel plan that matches the user's latest interests and concerns based on their social media posts.

[0043] The planning unit takes into account the travel history of the user's friends and family and can propose the optimal travel plan for the entire group. For example, the planning unit uses a generation AI to analyze the travel history of the user's friends and family and propose the optimal travel plan for the entire group. For example, it creates a plan that includes activities that everyone can enjoy. This allows the system to take into account the travel history of the user's friends and family and propose the optimal travel plan for the entire group.

[0044] The planning unit can analyze reviews and ratings of the user's past travel destinations and propose travel plans that will provide greater satisfaction. For example, the planning unit uses a generation AI to analyze reviews and ratings of the user's past travel destinations and propose travel plans that will provide greater satisfaction. For example, it can create plans that include highly rated tourist spots and restaurants. This makes it possible to provide travel plans that will provide greater satisfaction based on the user's reviews and ratings of their past travel destinations.

[0045] The support unit can analyze the user's health data and suggest the most suitable medical institution in the event of an emergency. For example, the generative AI analyzes the user's health data and suggests the most suitable medical institution in the event of an emergency. For example, if the user's heart rate or blood pressure shows abnormalities, a nearby hospital will be suggested. This makes it possible to provide the most suitable medical institution in the event of an emergency based on the user's health data.

[0046] The support department can learn the user's past support history and provide the most effective support. For example, the generative AI can learn the user's past support history and provide the most effective support. For example, it can suggest new support based on the support services used in the past. This makes it possible to provide the most effective support based on the user's past support history.

[0047] The support department can analyze the experiences of other travelers and suggest the most successful support method. For example, the generative AI analyzes the experiences of other travelers and suggests the most successful support method. For example, it makes suggestions based on support methods that other travelers have given high ratings. This makes it possible to provide the most successful support method based on the experiences of other travelers.

[0048] The support unit can analyze the user's current location and surrounding resources and suggest the nearest support option. For example, the generative AI can analyze the user's current location and surrounding resources and suggest the nearest support option. For example, it can suggest nearby medical institutions or pharmacies. This allows the nearest support option to be provided based on the user's current location and surrounding resources.

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

[0050] The suggestion unit analyzes the user's preferences and past travel history to propose individually customized travel plans. For example, the suggestion unit uses the generation AI to analyze the user's preferences and past travel history and create a travel plan that includes natural parks and hiking trails. The suggestion unit can also use the generation AI to suggest restaurants and tourist attractions based on the user's preferences. Furthermore, the suggestion unit can also use the generation AI to suggest accommodations based on the user's preferences. The translation unit automatically translates the language of the travel destination. For example, the translation unit uses the generation AI to translate local restaurant menus into the user's native language. The translation unit can also use the generation AI to translate tourist information into the user's native language. Furthermore, the translation unit can also use the generation AI to translate local transportation information into the user's native language. The response unit responds flexibly to unexpected situations. For example, the response unit uses the generation AI to suggest alternative plans for sudden changes in weather. The response unit can also use the generation AI to suggest alternative plans for transportation delays. Furthermore, the response unit can use the generation AI to suggest emergency response measures. The planning unit dynamically adjusts the travel plan according to the user's real-time needs. For example, the planning unit uses the generation AI to instantly create a plan that includes tourist attractions based on the user's new needs. The planning unit can also use the generation AI to instantly create a plan that includes activities based on the user's new needs. The planning unit can also use the generation AI to instantly create a plan that includes restaurants based on the user's new needs. The support unit provides various support to the user during the trip. For example, the support unit uses the generation AI to provide emergency contact information. The support unit can also use the generation AI to provide information on local medical institutions. The support unit can also use the generation AI to provide local tourist information. As a result, the travel support system according to the embodiment can provide multifaceted support to travelers and realize a comfortable and safe travel experience.

[0051] The suggestion unit analyzes the user's health data and can propose travel plans tailored to the user's physical condition. For example, it can propose a reasonable sightseeing route based on the user's number of steps and heart rate. The health data is obtained from a fitness tracker. This allows it to provide travel plans tailored to the user's physical condition.

[0052] The suggestion unit analyzes the user's social media posts and can update the travel plan based on the user's latest interests. For example, it can suggest tourist spots that interest the user based on the user's recently posted photos and comments. This allows the travel plan to be updated based on the user's latest interests.

[0053] The suggestion unit can take into account the travel history of the user's friends or family and propose the best travel plan for the entire group. For example, it can create a plan that includes activities that everyone can enjoy. This allows it to provide the best travel plan for the entire group.

[0054] The suggestion unit analyzes reviews and ratings of the user's past travel destinations and can suggest more satisfying travel plans. For example, it can create plans that include highly rated tourist spots and restaurants. This allows the user to be provided with highly satisfying travel plans based on the user's past reviews and ratings.

[0055] The translation department understands local slang or dialects and can provide more natural translations. For example, it accurately translates local youth slang and regional expressions. This allows it to understand local slang and dialects and provide more natural translations.

[0056] The translation unit can learn the user's past translation history and provide individually optimized translations. For example, it prioritizes translations of phrases and words that the user uses frequently. This allows it to provide individually optimized translations based on the user's past translation history.

[0057] The translation unit can translate information about local culture and customs to help users gain a deeper understanding of the local area. For example, it can translate explanations of local festivals and traditional events. This allows users to translate information about local culture and customs to help users gain a deeper understanding of the local area.

[0058] The translation unit translates the user's voice input in real time, making it possible to have a smooth conversation with local people. For example, it can instantly translate what the user says and respond in the local language. This allows the user's voice input to be translated in real time, making it possible to have a smooth conversation with local people.

[0059] The response unit analyzes real-time traffic information and weather forecasts and can instantly generate the optimal alternative plan. For example, if a traffic jam occurs, it will suggest the optimal detour route. This allows the optimal alternative plan to be instantly generated based on real-time traffic information and weather forecasts.

[0060] The response unit can analyze the experiences of other travelers and suggest the most successful alternative plan, for example, based on the alternative plans that other travelers have given high ratings. This allows the most successful alternative plan to be provided based on the experiences of other travelers.

[0061] The response unit can analyze the user's current location and surrounding resources and suggest the closest alternative options, for example, suggesting nearby restaurants or cafes, thereby providing the closest alternative options based on the user's current location and surrounding resources.

[0062] The planning unit analyzes the user's health data and can propose travel plans tailored to their physical condition. For example, it can propose a reasonable sightseeing route based on the user's number of steps and heart rate. The health data is obtained from a fitness tracker. This allows it to provide travel plans tailored to the user's physical condition based on the user's health data.

[0063] The planning unit can analyze users' social media posts and update travel plans based on their latest interests. For example, it can suggest tourist spots of interest based on the user's recent photos and comments. This allows it to provide travel plans that match the user's latest interests based on their social media posts.

[0064] The planning unit can take into account the travel history of the user's friends and family and propose the best travel plan for the entire group. For example, it can create a plan that includes activities that everyone can enjoy. This allows the planning unit to take into account the travel history of the user's friends and family and propose the best travel plan for the entire group.

[0065] The planning unit analyzes reviews and ratings of the user's past travel destinations and can propose travel plans that will provide a higher level of satisfaction. For example, it can create plans that include highly rated tourist spots and restaurants. This allows the system to provide a travel plan that will provide a higher level of satisfaction based on the user's reviews and ratings of the user's past travel destinations.

[0066] The support department can analyze the user's health data and suggest the most suitable medical institution in the event of an emergency. For example, the generative AI analyzes the user's health data and suggests the most suitable medical institution in the event of an emergency. For example, if the user's heart rate or blood pressure shows abnormalities, it will suggest a nearby hospital. This allows the system to provide the most suitable medical institution in the event of an emergency based on the user's health data.

[0067] The support department can learn from the user's past support history and provide the most effective support. For example, the generative AI can learn from the user's past support history and provide the most effective support. For example, it can suggest new support based on the support services used in the past. This allows the most effective support to be provided based on the user's past support history.

[0068] The support department can analyze the experiences of other travelers and suggest the most successful support methods. For example, the generative AI can analyze the experiences of other travelers and suggest the most successful support methods. For example, it can make suggestions based on support methods that other travelers have given high ratings. This allows it to provide the most successful support methods based on the experiences of other travelers.

[0069] The support department can analyze the user's current location and surrounding resources and suggest the nearest support option. For example, the generative AI can analyze the user's current location and surrounding resources and suggest the nearest support option. For example, it can suggest nearby medical institutions or pharmacies. This allows the nearest support option to be provided based on the user's current location and surrounding resources.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The proposal unit analyzes the user's preferences and past travel history and proposes individually customized travel plans. For example, it uses generative AI to propose travel plans that include natural parks and hiking trails, as well as restaurants, tourist spots, and accommodations based on the user's preferences. Step 2: The translation unit automatically translates the language of the destination. For example, it uses generative AI to translate local restaurant menus, tourist guides, and transportation information into the user's native language. Step 3: The response team responds flexibly to unexpected situations, for example, using generative AI to propose alternative plans for sudden changes in weather or transportation delays, as well as emergency response measures. Step 4: The planning department dynamically adjusts the travel plan based on the user's real-time needs, for example, using generative AI to instantly create a plan including new tourist attractions, activities, and restaurants based on the user's new needs. Step 5: The support department provides various support to users during their trip, such as emergency contact information, information on local medical facilities, and tourist information using generative AI.

[0072] (Example 2) A travel support system according to an embodiment of the present invention is a system that uses generative AI to personalize travel experiences and provide multifaceted support to travelers. As a result, the travel support system can provide multifaceted support to travelers and realize a comfortable and safe travel experience.

[0073] A travel support system according to an embodiment includes a suggestion unit, a translation unit, a response unit, a planning unit, and a support unit. The suggestion unit analyzes a user's preferences and past travel history and proposes an individually customized travel plan. For example, the suggestion unit uses a generation AI to analyze the user's preferences and past travel history and create a travel plan that includes natural parks and hiking trails. The suggestion unit can also use the generation AI to suggest restaurants and tourist spots based on the user's preferences. The suggestion unit can also use the generation AI to suggest accommodations based on the user's preferences. The translation unit automatically translates the language of the travel destination. For example, the translation unit uses the generation AI to translate local restaurant menus into the user's native language. The translation unit can also use the generation AI to translate tourist information into the user's native language. The translation unit can also use the generation AI to translate local transportation information into the user's native language. The response unit flexibly responds to unexpected situations. For example, the response unit uses the generation AI to suggest alternative plans for sudden changes in weather. The response unit can also use the generation AI to propose alternative plans for transportation delays. Furthermore, the response unit can also use the generation AI to propose emergency response measures. The planning unit dynamically adjusts the travel plan according to the user's real-time needs. For example, the planning unit can use the generation AI to instantly create a plan including tourist attractions based on the user's new needs. The planning unit can also use the generation AI to instantly create a plan including activities based on the user's new needs. Furthermore, the planning unit can also use the generation AI to instantly create a plan including restaurants based on the user's new needs. The support unit provides various support to the user during the trip. For example, the support unit can use the generation AI to provide emergency contact information. The support unit can also use the generation AI to provide information on local medical institutions. Furthermore, the support unit can also use the generation AI to provide local tourist information. As a result, the travel support system according to the embodiment can provide multifaceted support to travelers, enabling them to have a comfortable and safe travel experience.

[0074] The suggestion unit analyzes the user's real-time emotional data and can adjust the travel plan on the spot. For example, if the user is feeling stressed, the suggestion unit can suggest relaxing activities. Emotional data is obtained from sensors in the smartwatch or smartphone. This allows the travel plan to be dynamically adjusted according to the user's emotions.

[0075] The suggestion unit can analyze the user's health data and propose travel plans that suit their physical condition. For example, the suggestion unit uses a generation AI to analyze the user's health data and propose travel plans that suit their physical condition. For example, it can propose a reasonable sightseeing route based on the user's number of steps and heart rate. Health data is obtained from a fitness tracker. This makes it possible to provide travel plans that suit the user's physical condition.

[0076] The suggestion unit can analyze the user's social media posts and update the travel plan based on the user's latest interests and concerns. For example, the suggestion unit uses a generation AI to analyze the user's social media posts and update the travel plan based on the user's latest interests and concerns. For example, the suggestion unit suggests tourist spots of interest based on photos and comments recently posted by the user. This allows the travel plan to be updated based on the user's latest interests and concerns.

[0077] The suggestion unit can take into account the travel history of the user's friends or family and propose the optimal travel plan for the entire group. For example, the suggestion unit uses a generation AI to analyze the travel history of the user's friends and family and propose the optimal travel plan for the entire group. For example, it creates a plan that includes activities that everyone can enjoy. This makes it possible to provide the optimal travel plan for the entire group.

[0078] The suggestion unit can analyze reviews or ratings of the user's past travel destinations and suggest travel plans that will provide more satisfaction. For example, the suggestion unit uses a generation AI to analyze reviews and ratings of the user's past travel destinations and suggest travel plans that will provide more satisfaction. For example, it creates a plan that includes highly rated tourist spots and restaurants. This makes it possible to provide travel plans that will provide more satisfaction based on the user's past reviews and ratings.

[0079] The suggestion unit can use the emotion estimation function to analyze the user's emotions in real time when checking a travel plan and suggest plans that elicit a positive response. For example, the suggestion unit can use the emotion estimation function to analyze the user's emotions in real time when checking a travel plan and suggest plans that elicit a positive response. For example, if the user smiles, the suggestion unit can preferentially suggest that plan. This makes it possible to provide travel plans that elicit a positive response according to the user's emotions.

[0080] The translation unit analyzes the user's emotions and can translate by emphasizing information that is particularly important when the user is feeling stressed. For example, the translation unit uses a generation AI to analyze the user's emotions and translate by emphasizing information that is particularly important when the user is feeling stressed. For example, emergency contact information and information about medical institutions are displayed prominently. This allows important information to be translated by emphasizing it when the user is feeling stressed.

[0081] The translation unit understands local slang or dialects and can provide more natural-sounding translations. For example, the generative AI can accurately translate local youth slang and regional expressions. This allows the translation unit to understand local slang and dialects and provide more natural-sounding translations.

[0082] The translation unit can learn the user's past translation history and provide individually optimized translations. For example, the generation AI can learn the user's past translation history and provide individually optimized translations. For example, it can prioritize translations of phrases and words that the user uses frequently. This makes it possible to provide individually optimized translations based on the user's past translation history.

[0083] The translation unit can translate information about local culture and customs, allowing users to gain a deeper understanding of the local area. For example, the generative AI can translate information about local culture and customs, allowing users to gain a deeper understanding of the local area. For example, it can translate explanations of local festivals and traditional events. This translates information about local culture and customs, allowing users to gain a deeper understanding of the local area.

[0084] The translation unit translates the user's voice input in real time, making it possible to have smooth conversations with local people. For example, the translation unit uses a generative AI to translate the user's voice input in real time, making it possible to have smooth conversations with local people. For example, it can instantly translate what the user says and respond in the local language. This allows the user's voice input to be translated in real time, making it possible to have smooth conversations with local people.

[0085] The translation unit can use the emotion estimation function to analyze the emotion of the user when checking the translated information and provide the information in an easy-to-understand format. For example, the translation unit can use the emotion estimation function to analyze the emotion of the user when checking the translated information and provide the information in an easy-to-understand format. For example, if the user is confused, the translation unit retranslates the information in simpler terms. This makes it possible to provide information in an easy-to-understand format according to the user's emotion.

[0086] The response unit can analyze the user's emotions and suggest alternative plans that are particularly relaxing when the user is feeling stressed. For example, the response unit can have the generation AI analyze the user's emotions and suggest alternative plans that are particularly relaxing when the user is feeling stressed. For example, if plans are changed due to a sudden change in weather, the response unit can suggest spa or relaxation facilities. This makes it possible to provide an alternative plan that allows the user to relax when they are feeling stressed.

[0087] The response unit can analyze real-time traffic information and weather forecasts and instantly generate the optimal alternative plan. For example, the generation AI in the response unit analyzes real-time traffic information and weather forecasts and instantly generates the optimal alternative plan. For example, if a traffic jam occurs, it will suggest the optimal detour route. This makes it possible to instantly generate the optimal alternative plan based on real-time traffic information and weather forecasts.

[0088] The response unit can analyze the experiences of other travelers and propose the most successful alternative plan. For example, the response unit uses a generation AI to analyze the experiences of other travelers and propose the most successful alternative plan. For example, the response unit proposes an alternative plan based on alternative plans that other travelers have given high ratings. This makes it possible to provide the most successful alternative plan based on the experiences of other travelers.

[0089] The response unit can analyze the user's current location and surrounding resources and suggest the closest alternative option. For example, the generation AI can analyze the user's current location and surrounding resources and suggest the closest alternative option. For example, it can suggest nearby restaurants or cafes. This allows the closest alternative option to be provided based on the user's current location and surrounding resources.

[0090] The response unit can use the emotion estimation function to analyze the emotion of the user when confirming the alternative plan and propose a plan that will elicit a positive response. For example, the response unit can use the emotion estimation function to analyze the emotion of the user when confirming the alternative plan and propose a plan that will elicit a positive response. For example, if the user smiles, the response unit preferentially proposes that plan. This makes it possible to provide an alternative plan that will elicit a positive response according to the user's emotion.

[0091] The planning unit analyzes the user's real-time emotional data and can adjust the travel plan on the spot. For example, the planning unit uses a generative AI to analyze the user's real-time emotional data and adjust the travel plan on the spot. For example, if the user is tired, it will suggest a plan that includes rest. Emotional data is obtained from sensors in a smartwatch or smartphone. This allows the travel plan to be adjusted on the spot based on the user's real-time emotional data.

[0092] The planning unit can analyze the user's health data and propose travel plans tailored to their physical condition. For example, the planning unit uses a generation AI to analyze the user's health data and propose travel plans tailored to their physical condition. For example, it can propose a reasonable sightseeing route based on the user's number of steps and heart rate. Health data is obtained from a fitness tracker. This makes it possible to provide travel plans tailored to the user's physical condition based on their health data.

[0093] The planning unit can analyze the user's social media posts and update the travel plan based on their latest interests and concerns. For example, the planning unit uses a generative AI to analyze the user's social media posts and update the travel plan based on their latest interests and concerns. For example, the planning unit suggests tourist spots of interest based on photos and comments recently posted by the user. This makes it possible to provide a travel plan that matches the user's latest interests and concerns based on their social media posts.

[0094] The planning unit takes into account the travel history of the user's friends and family and can propose the optimal travel plan for the entire group. For example, the planning unit uses a generation AI to analyze the travel history of the user's friends and family and propose the optimal travel plan for the entire group. For example, it creates a plan that includes activities that everyone can enjoy. This allows the system to take into account the travel history of the user's friends and family and propose the optimal travel plan for the entire group.

[0095] The planning unit can analyze reviews and ratings of the user's past travel destinations and propose travel plans that will provide greater satisfaction. For example, the planning unit uses a generation AI to analyze reviews and ratings of the user's past travel destinations and propose travel plans that will provide greater satisfaction. For example, it can create plans that include highly rated tourist spots and restaurants. This makes it possible to provide travel plans that will provide greater satisfaction based on the user's reviews and ratings of their past travel destinations.

[0096] The planning unit can use the emotion estimation function to analyze the user's emotions in real time when checking a travel plan and propose plans that elicit a positive response. For example, the planning unit can use the emotion estimation function to analyze the user's emotions in real time when checking a travel plan and propose plans that elicit a positive response. For example, if the user smiles, the planning unit can preferentially propose that plan. This makes it possible to provide travel plans that elicit a positive response according to the user's emotions.

[0097] The support unit can analyze the user's emotions and provide information that is particularly reassuring when the user is feeling stressed. For example, the support unit uses a generation AI to analyze the user's emotions and provide information that is particularly reassuring when the user is feeling stressed. For example, emergency contact information and information about medical institutions are prominently displayed. This makes it possible to provide information that is particularly reassuring when the user is feeling stressed.

[0098] The support unit can analyze the user's health data and suggest the most suitable medical institution in the event of an emergency. For example, the generative AI analyzes the user's health data and suggests the most suitable medical institution in the event of an emergency. For example, if the user's heart rate or blood pressure shows abnormalities, a nearby hospital will be suggested. This makes it possible to provide the most suitable medical institution in the event of an emergency based on the user's health data.

[0099] The support department can learn the user's past support history and provide the most effective support. For example, the generative AI can learn the user's past support history and provide the most effective support. For example, it can suggest new support based on the support services used in the past. This makes it possible to provide the most effective support based on the user's past support history.

[0100] The support department can analyze the experiences of other travelers and suggest the most successful support method. For example, the generative AI analyzes the experiences of other travelers and suggests the most successful support method. For example, it makes suggestions based on support methods that other travelers have given high ratings. This makes it possible to provide the most successful support method based on the experiences of other travelers.

[0101] The support unit can analyze the user's current location and surrounding resources and suggest the nearest support option. For example, the generative AI can analyze the user's current location and surrounding resources and suggest the nearest support option. For example, it can suggest nearby medical institutions or pharmacies. This allows the nearest support option to be provided based on the user's current location and surrounding resources.

[0102] The support unit can use the emotion estimation function to analyze the emotion of the user when checking support information and provide information that gives a sense of security. For example, the support unit can use the emotion estimation function to analyze the emotion of the user when checking support information and provide information that gives a sense of security. For example, if the user is feeling anxious, the support unit can change the expression to one that gives a sense of security. This makes it possible to provide support information that gives a sense of security according to the user's emotion.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] The suggestion unit analyzes the user's preferences and past travel history to propose individually customized travel plans. For example, the suggestion unit uses the generation AI to analyze the user's preferences and past travel history and create a travel plan that includes natural parks and hiking trails. The suggestion unit can also use the generation AI to suggest restaurants and tourist attractions based on the user's preferences. Furthermore, the suggestion unit can also use the generation AI to suggest accommodations based on the user's preferences. The translation unit automatically translates the language of the travel destination. For example, the translation unit uses the generation AI to translate local restaurant menus into the user's native language. The translation unit can also use the generation AI to translate tourist information into the user's native language. Furthermore, the translation unit can also use the generation AI to translate local transportation information into the user's native language. The response unit responds flexibly to unexpected situations. For example, the response unit uses the generation AI to suggest alternative plans for sudden changes in weather. The response unit can also use the generation AI to suggest alternative plans for transportation delays. Furthermore, the response unit can use the generation AI to suggest emergency response measures. The planning unit dynamically adjusts the travel plan according to the user's real-time needs. For example, the planning unit uses the generation AI to instantly create a plan that includes tourist attractions based on the user's new needs. The planning unit can also use the generation AI to instantly create a plan that includes activities based on the user's new needs. The planning unit can also use the generation AI to instantly create a plan that includes restaurants based on the user's new needs. The support unit provides various support to the user during the trip. For example, the support unit uses the generation AI to provide emergency contact information. The support unit can also use the generation AI to provide information on local medical institutions. The support unit can also use the generation AI to provide local tourist information. As a result, the travel support system according to the embodiment can provide multifaceted support to travelers and realize a comfortable and safe travel experience.

[0105] The suggestion module analyzes the user's real-time emotional data and can adjust the travel plan on the fly. For example, if the user is feeling stressed, it can suggest relaxing activities. Emotional data is acquired from sensors in the smartwatch or smartphone. This allows the travel plan to be dynamically adjusted according to the user's emotions.

[0106] The suggestion unit analyzes the user's health data and can propose travel plans tailored to the user's physical condition. For example, it can propose a reasonable sightseeing route based on the user's number of steps and heart rate. The health data is obtained from a fitness tracker. This allows it to provide travel plans tailored to the user's physical condition.

[0107] The suggestion unit analyzes the user's social media posts and can update the travel plan based on the user's latest interests. For example, it can suggest tourist spots that interest the user based on the user's recently posted photos and comments. This allows the travel plan to be updated based on the user's latest interests.

[0108] The suggestion unit can take into account the travel history of the user's friends or family and propose the best travel plan for the entire group. For example, it can create a plan that includes activities that everyone can enjoy. This allows it to provide the best travel plan for the entire group.

[0109] The suggestion unit analyzes reviews and ratings of the user's past travel destinations and can suggest more satisfying travel plans. For example, it can create plans that include highly rated tourist spots and restaurants. This allows the user to be provided with highly satisfying travel plans based on the user's past reviews and ratings.

[0110] The suggestion unit uses the emotion estimation function to analyze the user's emotions in real time when checking travel plans, and can suggest plans that will elicit a positive response. For example, if the user smiles, the suggestion unit will prioritize the suggestion of that plan. This makes it possible to provide travel plans that elicit a positive response according to the user's emotions.

[0111] The translation unit analyzes the user's emotions and can translate information that is particularly important when the user is feeling stressed. For example, emergency contact information and medical institution information can be prominently displayed. This allows important information to be translated with emphasis when the user is feeling stressed.

[0112] The translation department understands local slang or dialects and can provide more natural translations. For example, it accurately translates local youth slang and regional expressions. This allows it to understand local slang and dialects and provide more natural translations.

[0113] The translation unit can learn the user's past translation history and provide individually optimized translations. For example, it prioritizes translations of phrases and words that the user uses frequently. This allows it to provide individually optimized translations based on the user's past translation history.

[0114] The translation unit can translate information about local culture and customs to help users gain a deeper understanding of the local area. For example, it can translate explanations of local festivals and traditional events. This allows users to translate information about local culture and customs to help users gain a deeper understanding of the local area.

[0115] The translation unit translates the user's voice input in real time, making it possible to have a smooth conversation with local people. For example, it can instantly translate what the user says and respond in the local language. This allows the user's voice input to be translated in real time, making it possible to have a smooth conversation with local people.

[0116] The translation unit uses its emotion estimation function to analyze the user's emotions when reviewing translated information and provide the information in an easy-to-understand format. For example, if the user is confused, the translation unit retranslates the information in simpler terms. This allows the information to be provided in an easy-to-understand format according to the user's emotions.

[0117] The response unit can analyze the user's emotions and suggest alternative plans that are particularly relaxing when the user is feeling stressed. For example, if plans are changed due to a sudden change in weather, the response unit can suggest spa or relaxation facilities. This allows the user to be provided with alternative plans that allow them to relax when they are feeling stressed.

[0118] The response unit analyzes real-time traffic information and weather forecasts and can instantly generate the optimal alternative plan. For example, if a traffic jam occurs, it will suggest the optimal detour route. This allows the optimal alternative plan to be instantly generated based on real-time traffic information and weather forecasts.

[0119] The response unit can analyze the experiences of other travelers and suggest the most successful alternative plan, for example, based on the alternative plans that other travelers have given high ratings. This allows the most successful alternative plan to be provided based on the experiences of other travelers.

[0120] The response unit can analyze the user's current location and surrounding resources and suggest the closest alternative options, for example, suggesting nearby restaurants or cafes, thereby providing the closest alternative options based on the user's current location and surrounding resources.

[0121] The response unit uses the emotion estimation function to analyze the user's emotions when confirming alternative plans and can propose plans that elicit a positive response. For example, if the user smiles, that plan will be proposed preferentially. This makes it possible to provide alternative plans that elicit a positive response according to the user's emotions.

[0122] The planning unit analyzes the user's real-time emotional data and can adjust the travel plan on the spot. For example, if the user is tired, it will suggest a plan that includes rest. Emotional data is acquired from sensors in the smartwatch or smartphone. This allows the travel plan to be adjusted on the spot based on the user's real-time emotional data.

[0123] The planning unit analyzes the user's health data and can propose travel plans tailored to their physical condition. For example, it can propose a reasonable sightseeing route based on the user's number of steps and heart rate. The health data is obtained from a fitness tracker. This allows it to provide travel plans tailored to the user's physical condition based on the user's health data.

[0124] The planning unit can analyze users' social media posts and update travel plans based on their latest interests. For example, it can suggest tourist spots of interest based on the user's recent photos and comments. This allows it to provide travel plans that match the user's latest interests based on their social media posts.

[0125] The planning unit can take into account the travel history of the user's friends and family and propose the best travel plan for the entire group. For example, it can create a plan that includes activities that everyone can enjoy. This allows the planning unit to take into account the travel history of the user's friends and family and propose the best travel plan for the entire group.

[0126] The planning unit analyzes reviews and ratings of the user's past travel destinations and can propose travel plans that will provide a higher level of satisfaction. For example, it can create plans that include highly rated tourist spots and restaurants. This allows the system to provide a travel plan that will provide a higher level of satisfaction based on the user's reviews and ratings of the user's past travel destinations.

[0127] The planning unit uses the emotion estimation function to analyze the user's emotions in real time when checking travel plans, and can suggest plans that will elicit a positive response. For example, if the user smiles, that plan will be suggested preferentially. This makes it possible to provide travel plans that elicit a positive response according to the user's emotions.

[0128] The support section can analyze the user's emotions and provide information that is particularly reassuring when the user is feeling stressed. For example, the generation AI can analyze the user's emotions and provide information that is particularly reassuring when the user is feeling stressed. For example, emergency contact information and medical institution information can be prominently displayed. This allows the user to receive information that is particularly reassuring when the user is feeling stressed.

[0129] The support department can analyze the user's health data and suggest the most suitable medical institution in the event of an emergency. For example, the generative AI analyzes the user's health data and suggests the most suitable medical institution in the event of an emergency. For example, if the user's heart rate or blood pressure shows abnormalities, it will suggest a nearby hospital. This allows the system to provide the most suitable medical institution in the event of an emergency based on the user's health data.

[0130] The support department can learn from the user's past support history and provide the most effective support. For example, the generative AI can learn from the user's past support history and provide the most effective support. For example, it can suggest new support based on the support services used in the past. This allows the most effective support to be provided based on the user's past support history.

[0131] The support department can analyze the experiences of other travelers and suggest the most successful support methods. For example, the generative AI can analyze the experiences of other travelers and suggest the most successful support methods. For example, it can make suggestions based on support methods that other travelers have given high ratings. This allows it to provide the most successful support methods based on the experiences of other travelers.

[0132] The support department can analyze the user's current location and surrounding resources and suggest the nearest support option. For example, the generative AI can analyze the user's current location and surrounding resources and suggest the nearest support option. For example, it can suggest nearby medical institutions or pharmacies. This allows the nearest support option to be provided based on the user's current location and surrounding resources.

[0133] The support unit can use the emotion estimation function to analyze the emotion of the user when checking support information and provide information that gives a sense of security. For example, the emotion estimation function can be used to analyze the emotion of the user when checking support information and provide information that gives a sense of security. For example, if the user is feeling anxious, the expression can be changed to one that gives a sense of security. In this way, support information that gives a sense of security can be provided according to the user's emotion.

[0134] The processing flow of the second embodiment will be briefly explained below.

[0135] Step 1: The proposal unit analyzes the user's preferences and past travel history and proposes individually customized travel plans. For example, it uses generative AI to propose travel plans that include natural parks and hiking trails, as well as restaurants, tourist spots, and accommodations based on the user's preferences. Step 2: The translation unit automatically translates the language of the destination. For example, it uses generative AI to translate local restaurant menus, tourist guides, and transportation information into the user's native language. Step 3: The response team responds flexibly to unexpected situations, for example, using generative AI to propose alternative plans for sudden changes in weather or transportation delays, as well as emergency response measures. Step 4: The planning department dynamically adjusts the travel plan based on the user's real-time needs, for example, using generative AI to instantly create a plan including new tourist attractions, activities, and restaurants based on the user's new needs. Step 5: The support department provides various support to users during their trip, such as emergency contact information, information on local medical facilities, and tourist information using generative AI.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0149] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0164] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 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.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0180] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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).

[0189] 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.

[0190] 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."

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0203] 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 proposal unit that analyzes the user's preferences and past travel history and proposes individually customized travel plans; A translation section that automatically translates the language of the destination, A response department that responds flexibly to unexpected situations, a planning unit that dynamically adjusts the travel plan according to the user's real-time needs; a support unit that provides multiple supports to the user during travel. A system characterized by:

2. The proposal unit Analyzing the user's real-time emotional data and adjusting the travel plan on the fly 2. The system of claim 1.

3. The translation unit Analyzing the user's emotions and performing the translation while emphasizing information that is particularly important when the user is feeling stressed 2. The system of claim 1.

4. The corresponding part is Analyzing the user's emotions and suggesting alternative plans that are particularly relaxing if the user is feeling stressed 2. The system of claim 1.

5. The planning unit Analyzing the real-time emotional data of the user and adjusting the travel plan on the fly.

2. The system of claim 1.

6. The support portion is Analyzing the user's emotions and providing information that is particularly reassuring when the user is feeling stressed 2. The system of claim 1.

Images