system

Abstract

The system according to the embodiment aims to appropriately suggest services and precautions according to the travel destination and period. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives input of a travel destination and a travel period. The analysis unit analyzes the information received by the reception unit and suggests services or precautions related to the travel destination. The provision unit provides the services or precautions suggested by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately suggest services and precautions appropriate for travel destinations and duration, and there is room for improvement.

[0005] The system according to the embodiment aims to appropriately suggest services and precautions according to the travel destination and period. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives input of a travel destination and a travel period. The analysis unit analyzes the information received by the reception unit and proposes services or precautions related to the travel destination. The provision unit provides the services or precautions proposed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately suggest services and precautions according to the travel destination and period. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A travel planning support system according to an embodiment of the present invention automatically analyzes information input by a user about the destination and duration of a trip and suggests services and precautions to be offered. This travel planning support system analyzes the information and provides services and precautions related to the destination. For example, if it is typhoon season, various insurance options are suggested. This mechanism allows users to efficiently plan their trips. First, the user inputs the destination and duration of the trip. To do this, the user only needs to input the departure point, destination, and travel duration. For example, the user might input "I want to travel from Tokyo to Okinawa for five days." This information is entered into the system. Next, the system analyzes the input information. The system considers the climate, season, local event information, and other factors at the destination and suggests optimal services and precautions. For example, if it is typhoon season, it suggests travel insurance and cancellation insurance. It also provides information on local events and tourist spots. Furthermore, the system suggests services available locally based on the user's travel plans. For example, it suggests car rental services, hotel reservations, and restaurant reservations. This allows users to efficiently plan their trips. This system allows users to obtain information about their travel destinations in a centralized manner and efficiently plan their trips. The system also provides important information to help users mitigate risks during their trips. For example, during typhoon season, the system can suggest travel insurance and cancellation insurance, allowing users to enjoy their trips with peace of mind. This allows the travel planning support system to efficiently support users' travel plans and mitigate risks during their trips.

[0029] A travel planning support system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives a user's input of a travel destination and duration. When receiving the input of the travel destination and duration, the user may input, for example, a departure point, a destination, and travel duration. For example, the user may input, "I want to travel from Tokyo to Okinawa for five days." This information is input into the system. The analysis unit analyzes the information received by the reception unit and suggests services and precautions related to the travel destination. The analysis unit suggests services and precautions taking into account, for example, the climate, season, and local event information of the travel destination. For example, the analysis unit suggests travel insurance and cancellation insurance if it is typhoon season. The analysis unit can also provide information on local events and tourist spots. For example, the analysis unit provides information on local festivals, concerts, sporting events, etc. The provision unit provides the services and precautions suggested by the analysis unit. The provision unit suggests, for example, rental car services, hotel reservations, and restaurant reservations. For example, the provision unit suggests services available locally based on the user's travel plans. For example, the providing unit can suggest local car rental services, hotel reservations, and restaurant reservations. As a result, the travel planning support system according to the embodiment can efficiently support the user in planning their travel and reduce risks during the trip.

[0030] The analysis unit can suggest services or precautions by taking into account the climate or season of the travel destination and local event information. The analysis unit can suggest services or precautions by taking into account, for example, the climate of the travel destination. For example, the analysis unit can suggest appropriate clothing and items to bring based on climate information such as the temperature, precipitation, and humidity of the travel destination. The analysis unit can also suggest services and precautions by taking into account the season of the travel destination. For example, the analysis unit can suggest bringing sunscreen and a hat if the travel destination is in summer. The analysis unit can also suggest bringing warm clothing if the travel destination is in winter. The analysis unit can also suggest services and precautions by taking into account local event information. For example, the analysis unit can provide information on festivals, concerts, sporting events, etc. held at the travel destination. In this way, more appropriate services and precautions can be suggested by taking into account the climate, season, and local event information of the travel destination.

[0031] The provision unit can propose travel insurance or cancellation insurance if it is typhoon season. For example, the provision unit can propose travel insurance if it is typhoon season. For example, the provision unit can propose travel insurance that covers medical expenses during the trip and cancellation costs. The provision unit can also propose cancellation insurance if it is typhoon season. For example, the provision unit can propose cancellation insurance that covers trip cancellations and changes due to typhoons. In this way, by proposing insurance suitable for typhoon season, the user's travel risks can be reduced.

[0032] The providing unit can provide local event information or tourist spot information. The providing unit provides, for example, local event information. For example, the providing unit can provide information on festivals, concerts, sporting events, etc. held at the travel destination. The providing unit can also provide information on local tourist spots. For example, the providing unit can provide information on historical buildings, natural landscapes, theme parks, etc. at the travel destination. In this way, by providing local event information and tourist spot information, the user's travel plans can be enriched.

[0033] The providing unit can suggest a rental car service, a hotel reservation, or a restaurant reservation. The providing unit, for example, suggests a rental car service. For example, the providing unit can suggest a rental car service that can be used at the travel destination. The providing unit can also suggest a hotel reservation. For example, the providing unit can suggest a hotel reservation that can be used at the travel destination. The providing unit can also suggest a restaurant reservation. For example, the providing unit can suggest a restaurant reservation that can be used at the travel destination. This makes it possible to efficiently support the user's travel plans by suggesting a rental car service, a hotel reservation, or a restaurant reservation.

[0034] The providing unit can suggest services that can be used locally based on the user's travel plans. The providing unit can suggest services that can be used locally based on the user's travel plans, for example. For example, the providing unit can suggest rental car services, hotel reservations, and restaurant reservations that can be used at the travel destination. The providing unit can also suggest sightseeing tours and guide services that can be used locally. For example, the providing unit can suggest sightseeing tours and guide services that can be used at the travel destination. This can improve the convenience of travel by suggesting services that can be used locally based on the user's travel plans.

[0035] The reception unit can analyze the user's past travel history and suggest an input method. The reception unit, for example, automatically displays travel destinations that the user has frequently visited in the past as candidates. For example, the reception unit can automatically display travel destinations that the user has frequently visited in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest travel destinations related to a particular season or event from the user's past travel history. For example, the reception unit can predict and suggest travel destinations related to a particular season or event from the user's past travel history. This makes it possible to suggest a more appropriate input method by taking the user's past travel history into consideration.

[0036] When the user inputs a travel destination and duration, the reception unit can present input candidates based on the user's current living situation or areas of interest. The reception unit can, for example, suggest travel destinations based on themes the user has recently been interested in (e.g., beach resorts or historical tourist destinations). For example, the reception unit can suggest travel destinations based on themes the user has recently been interested in (e.g., beach resorts or historical tourist destinations). The reception unit can also suggest travel destinations based on the user's current living situation (e.g., a place where the user can relax if the user is under a lot of stress at work). For example, the reception unit can suggest travel destinations based on the user's current living situation (e.g., a place where the user can relax if the user is under a lot of stress at work). The reception unit can also analyze the user's recent social media activity and suggest travel destinations of interest. For example, the reception unit can analyze the user's recent social media activity and suggest travel destinations of interest. This allows the system to suggest more appropriate travel destinations by presenting input candidates based on the user's current living situation and areas of interest.

[0037] When a travel destination and a travel period are input, the reception unit can prioritize displaying highly relevant candidates based on the user's geographical location information. The reception unit, for example, can prioritize displaying travel destinations close to the user's current location. For example, the reception unit can prioritize displaying travel destinations close to the user's current location. The reception unit can also suggest optimal travel destinations based on the climate and season of the user's current location. For example, the reception unit can suggest optimal travel destinations based on the climate and season of the user's current location. The reception unit can also suggest easily accessible travel destinations by taking into account the mode of transportation (plane, train, etc.) from the user's current location. For example, the reception unit can suggest easily accessible travel destinations by taking into account the mode of transportation (plane, train, etc.) from the user's current location. In this way, more relevant travel destinations can be suggested by taking into account the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity when the user inputs the travel destination and period and present related candidates. The reception unit, for example, can suggest places related to travel destinations that the user has recently "liked" or commented on. For example, the reception unit can suggest places related to travel destinations that the user has recently "liked" or commented on. The reception unit can also suggest places visited by influencers the user follows. For example, the reception unit can suggest places visited by influencers the user follows. The reception unit can also analyze and suggest travel destinations of interest from the content of the user's social media posts. For example, the reception unit can analyze and suggest travel destinations of interest from the content of the user's social media posts. In this way, by analyzing the user's social media activity, it is possible to suggest travel destinations that are more interesting to the user.

[0039] During analysis, the analysis unit can improve prediction accuracy based on past weather data for the travel destination. The analysis unit, for example, predicts the weather during the travel period based on weather data from the past few years. For example, the analysis unit can predict the weather during the travel period based on weather data from the past few years. The analysis unit can also analyze past weather data to identify weather patterns for a specific season. For example, the analysis unit can analyze past weather data to identify weather patterns for a specific season. The analysis unit can also predict climatic risks (typhoons, heavy rain, etc.) at the travel destination based on past weather data. For example, the analysis unit can predict climatic risks (typhoons, heavy rain, etc.) at the travel destination based on past weather data. In this way, by referring to past weather data, the accuracy of weather predictions during the travel period can be improved.

[0040] During analysis, the analysis unit can suggest services or precautions taking into account the culture or customs of the travel destination. The analysis unit, for example, suggests appropriate clothing and manners based on the culture of the travel destination. For example, the analysis unit can suggest appropriate clothing and manners based on the culture of the travel destination. The analysis unit can also provide information on specific events and festivals based on the customs of the travel destination. For example, the analysis unit can provide information on specific events and festivals based on the customs of the travel destination. The analysis unit can also suggest behaviors and language that should be taken into account, taking into account the cultural background of the travel destination. For example, the analysis unit can suggest behaviors and language that should be taken into account, taking into account the cultural background of the travel destination. In this way, more appropriate services and precautions can be suggested by taking into account the culture and customs of the travel destination.

[0041] During the analysis, the analysis unit can suggest services or precautions taking into account the geographical features of the travel destination. For example, if the travel destination is a mountainous area, the analysis unit can suggest mountain climbing equipment and precautions. For example, if the travel destination is a mountainous area, the analysis unit can suggest mountain climbing equipment and precautions. Furthermore, if the travel destination is a coastal area, the analysis unit can suggest bringing a swimsuit and sunscreen. For example, if the travel destination is a coastal area, the analysis unit can suggest bringing a swimsuit and sunscreen. Furthermore, if the travel destination is an urban area, the analysis unit can provide information on transportation and congestion. For example, if the travel destination is an urban area, the analysis unit can provide information on transportation and congestion. In this way, more appropriate services and precautions can be suggested by taking into account the geographical features of the travel destination.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis based on related literature for the travel destination. The analysis unit, for example, refers to the latest guidebooks and tourist information for the travel destination. For example, the analysis unit can refer to the latest guidebooks and tourist information for the travel destination. The analysis unit can also provide detailed information based on academic papers and research materials for the travel destination. For example, the analysis unit can provide detailed information based on academic papers and research materials for the travel destination. The analysis unit can also analyze user reviews and ratings for the travel destination to provide highly reliable information. For example, the analysis unit can analyze user reviews and ratings for the travel destination to provide highly reliable information. In this way, by referring to related literature, the accuracy of the analysis can be improved.

[0043] The providing unit can, at the time of providing, suggest services or precautions based on the user's past travel experience. The providing unit, for example, suggests an optimal service based on services (car rental, hotel, etc.) that the user has used in the past. For example, the providing unit can suggest an optimal service based on services (car rental, hotel, etc.) that the user has used in the past. The providing unit can also suggest precautions for specific risks (for example, typhoon season) based on the user's past travel experience. For example, the providing unit can suggest precautions for specific risks (for example, typhoon season) based on the user's past travel experience. The providing unit can also analyze the user's past travel experience and suggest the most efficient service. For example, the providing unit can analyze the user's past travel experience and suggest the most efficient service. In this way, more appropriate services and precautions can be suggested by taking the user's past travel experience into consideration.

[0044] The providing unit can customize services or precautions based on the user's current living situation when providing the services or precautions. For example, if the user is feeling stressed at work, the providing unit can suggest a relaxing service (spa, massage, etc.). For example, if the user is feeling stressed at work, the providing unit can suggest a relaxing service (spa, massage, etc.). Furthermore, if the user is planning a family trip, the providing unit can suggest family-oriented services (kids club, family room, etc.). For example, if the user is planning a family trip, the providing unit can suggest family-oriented services (kids club, family room, etc.). Furthermore, the providing unit can suggest activities (hiking, diving, etc.) if the user is planning an active trip. For example, if the user is planning an active trip, the providing unit can suggest activities (hiking, diving, etc.). This makes it possible to improve the convenience of travel by providing services and precautions based on the user's current living situation.

[0045] The providing unit can suggest services or precautions based on the user's geographical location information at the time of suggestion. The providing unit, for example, prioritizes suggesting services (car rentals, hotels, etc.) that are close to the user's current location. For example, the providing unit can prioritize suggesting services (car rentals, hotels, etc.) that are close to the user's current location. The providing unit can also suggest optimal services based on the climate and season of the user's current location. For example, the providing unit can suggest optimal services based on the climate and season of the user's current location. The providing unit can also suggest services that are easy to access by taking into account the mode of transportation (plane, train, etc.) from the user's current location. For example, the providing unit can suggest services that are easy to access by taking into account the mode of transportation (plane, train, etc.) from the user's current location. In this way, more relevant services and precautions can be suggested by taking into account the user's geographical location information.

[0046] At the time of providing, the providing unit may analyze the user's social media activity and suggest related services or precautions. For example, the providing unit may suggest information related to services that the user has recently "liked" or commented on. For example, the providing unit may suggest information related to services that the user has recently "liked" or commented on. The providing unit may also suggest services used by influencers the user follows. For example, the providing unit may suggest services used by influencers the user follows. The providing unit may also analyze and suggest services of interest from the content of the user's social media posts. For example, the providing unit may analyze and suggest services of interest from the content of the user's social media posts. In this way, by analyzing the user's social media activity, it is possible to suggest services and precautions that are of greater interest to the user.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The analysis unit can also analyze the user's past travel history and provide information useful for selecting a travel destination. For example, the analysis unit can suggest similar travel destinations based on ratings and reviews of travel destinations the user has visited in the past. The analysis unit can also suggest new related activities and services by taking into account the user's activity and service usage history at travel destinations visited in the past. Furthermore, the analysis unit can predict and suggest travel destinations related to specific seasons or events based on the user's past travel history. This makes it possible to utilize the user's past travel history to support more personalized travel planning.

[0049] The providing unit can also suggest health management services at the travel destination, taking into account the user's current health condition. For example, if the user has a specific allergy, the providing unit can suggest restaurants and accommodations that cater to allergies. In addition, if the user has a chronic illness, the providing unit can provide information on local medical institutions and pharmacies. Furthermore, if the user needs to exercise to maintain their health, the providing unit can provide information on local fitness facilities and jogging courses. This allows the user to reduce health risks during travel by providing services tailored to their health condition.

[0050] The providing unit can also provide information about transportation means at the user's travel destination. For example, the providing unit can provide information about how to use local public transportation and timetables. The providing unit can also suggest how to use local taxis and ride-sharing services. Furthermore, the providing unit can also suggest how to use local bicycle rentals and electric scooters. This allows the user to efficiently select a means of transportation at the travel destination, improving the convenience of the trip.

[0051] The providing unit can also provide information about meals at the user's travel destination. For example, the providing unit can provide information about popular local restaurants and cafes. The providing unit can also suggest restaurants that match the user's food preferences. Furthermore, the providing unit can also provide information about local food culture and local specialties. This allows the user to enjoy meals at the travel destination and improves the user's satisfaction with the trip.

[0052] The providing unit can also provide information about shopping at the user's travel destination. For example, the providing unit can provide information about popular local shopping areas and markets. The providing unit can also suggest shopping spots that match the user's interests. Furthermore, the providing unit can also provide information about local specialties and souvenirs. This allows the user to enjoy shopping at the travel destination and improves the user's satisfaction with the trip.

[0053] The providing unit can also provide safety information for the user's travel destination. For example, the providing unit can provide local security information and emergency contact information. The providing unit can also provide advice for the user to take safety measures at their travel destination. Furthermore, the providing unit can also provide information on local medical institutions and police stations. This allows the user to ensure safety at their travel destination and enjoy their trip with peace of mind.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The reception unit receives the user's input of the travel destination and duration. The user can input the departure point, destination, and travel duration. For example, the user may input "I would like to travel from Tokyo to Okinawa for 5 days." This information is entered into the system. Step 2: The analysis unit analyzes the information received by the reception unit and suggests services and precautions related to the travel destination. The analysis unit suggests services and precautions taking into account the climate, season, and local event information of the travel destination. For example, if it is typhoon season, it will suggest travel insurance and cancellation insurance, and also provide information on local festivals, concerts, sporting events, etc. Step 3: The provider provides the services and precautions suggested by the analyzer. Based on the user's travel plans, the provider suggests services that can be used locally. For example, it suggests car rental services, hotel reservations, and restaurant reservations.

[0056] (Example 2) A travel planning support system according to an embodiment of the present invention automatically analyzes information input by a user about the destination and duration of a trip and suggests services and precautions to be offered. This travel planning support system analyzes the information and provides services and precautions related to the destination. For example, if it is typhoon season, various insurance options are suggested. This mechanism allows users to efficiently plan their trips. First, the user inputs the destination and duration of the trip. To do this, the user only needs to input the departure point, destination, and travel duration. For example, the user might input "I want to travel from Tokyo to Okinawa for five days." This information is entered into the system. Next, the system analyzes the input information. The system considers the climate, season, local event information, and other factors at the destination and suggests optimal services and precautions. For example, if it is typhoon season, it suggests travel insurance and cancellation insurance. It also provides information on local events and tourist spots. Furthermore, the system suggests services available locally based on the user's travel plans. For example, it suggests car rental services, hotel reservations, and restaurant reservations. This allows users to efficiently plan their trips. This system allows users to obtain information about their travel destinations in a centralized manner and efficiently plan their trips. The system also provides important information to help users mitigate risks during their trips. For example, during typhoon season, the system can suggest travel insurance and cancellation insurance, allowing users to enjoy their trips with peace of mind. This allows the travel planning support system to efficiently support users' travel plans and mitigate risks during their trips.

[0057] A travel planning support system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives a user's input of a travel destination and duration. When receiving the input of the travel destination and duration, the user may input, for example, a departure point, a destination, and travel duration. For example, the user may input, "I want to travel from Tokyo to Okinawa for five days." This information is input into the system. The analysis unit analyzes the information received by the reception unit and suggests services and precautions related to the travel destination. The analysis unit suggests services and precautions taking into account, for example, the climate, season, and local event information of the travel destination. For example, the analysis unit suggests travel insurance and cancellation insurance if it is typhoon season. The analysis unit can also provide information on local events and tourist spots. For example, the analysis unit provides information on local festivals, concerts, sporting events, etc. The provision unit provides the services and precautions suggested by the analysis unit. The provision unit suggests, for example, rental car services, hotel reservations, and restaurant reservations. For example, the provision unit suggests services available locally based on the user's travel plans. For example, the providing unit can suggest local car rental services, hotel reservations, and restaurant reservations. As a result, the travel planning support system according to the embodiment can efficiently support the user in planning their travel and reduce risks during the trip.

[0058] The analysis unit can suggest services or precautions by taking into account the climate or season of the travel destination and local event information. The analysis unit can suggest services or precautions by taking into account, for example, the climate of the travel destination. For example, the analysis unit can suggest appropriate clothing and items to bring based on climate information such as the temperature, precipitation, and humidity of the travel destination. The analysis unit can also suggest services and precautions by taking into account the season of the travel destination. For example, the analysis unit can suggest bringing sunscreen and a hat if the travel destination is in summer. The analysis unit can also suggest bringing warm clothing if the travel destination is in winter. The analysis unit can also suggest services and precautions by taking into account local event information. For example, the analysis unit can provide information on festivals, concerts, sporting events, etc. held at the travel destination. In this way, more appropriate services and precautions can be suggested by taking into account the climate, season, and local event information of the travel destination.

[0059] The provision unit can propose travel insurance or cancellation insurance if it is typhoon season. For example, the provision unit can propose travel insurance if it is typhoon season. For example, the provision unit can propose travel insurance that covers medical expenses during the trip and cancellation costs. The provision unit can also propose cancellation insurance if it is typhoon season. For example, the provision unit can propose cancellation insurance that covers trip cancellations and changes due to typhoons. In this way, by proposing insurance suitable for typhoon season, the user's travel risks can be reduced.

[0060] The providing unit can provide local event information or tourist spot information. The providing unit provides, for example, local event information. For example, the providing unit can provide information on festivals, concerts, sporting events, etc. held at the travel destination. The providing unit can also provide information on local tourist spots. For example, the providing unit can provide information on historical buildings, natural landscapes, theme parks, etc. at the travel destination. In this way, by providing local event information and tourist spot information, the user's travel plans can be enriched.

[0061] The providing unit can suggest a rental car service, a hotel reservation, or a restaurant reservation. The providing unit, for example, suggests a rental car service. For example, the providing unit can suggest a rental car service that can be used at the travel destination. The providing unit can also suggest a hotel reservation. For example, the providing unit can suggest a hotel reservation that can be used at the travel destination. The providing unit can also suggest a restaurant reservation. For example, the providing unit can suggest a restaurant reservation that can be used at the travel destination. This makes it possible to efficiently support the user's travel plans by suggesting a rental car service, a hotel reservation, or a restaurant reservation.

[0062] The providing unit can suggest services that can be used locally based on the user's travel plans. The providing unit can suggest services that can be used locally based on the user's travel plans, for example. For example, the providing unit can suggest rental car services, hotel reservations, and restaurant reservations that can be used at the travel destination. The providing unit can also suggest sightseeing tours and guide services that can be used locally. For example, the providing unit can suggest sightseeing tours and guide services that can be used at the travel destination. This can improve the convenience of travel by suggesting services that can be used locally based on the user's travel plans.

[0063] The reception unit can estimate the user's emotions and customize the input interface for the travel destination and duration based on the estimated user's emotions. For example, when the user is feeling stressed, the reception unit can provide a simple and intuitive interface to minimize input steps. For example, when the user is feeling stressed, the reception unit can provide a simple and intuitive interface to minimize input steps. Furthermore, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. For example, when the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input the travel destination and duration. For example, when the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input the travel destination and duration. In this way, by providing an interface according to the user's emotions, input convenience can be improved.

[0064] The reception unit can analyze the user's past travel history and suggest an input method. The reception unit, for example, automatically displays travel destinations that the user has frequently visited in the past as candidates. For example, the reception unit can automatically display travel destinations that the user has frequently visited in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit can preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest travel destinations related to a particular season or event from the user's past travel history. For example, the reception unit can predict and suggest travel destinations related to a particular season or event from the user's past travel history. This makes it possible to suggest a more appropriate input method by taking the user's past travel history into consideration.

[0065] When the user inputs a travel destination and duration, the reception unit can present input candidates based on the user's current living situation or areas of interest. The reception unit can, for example, suggest travel destinations based on themes the user has recently been interested in (e.g., beach resorts or historical tourist destinations). For example, the reception unit can suggest travel destinations based on themes the user has recently been interested in (e.g., beach resorts or historical tourist destinations). The reception unit can also suggest travel destinations based on the user's current living situation (e.g., a place where the user can relax if the user is under a lot of stress at work). For example, the reception unit can suggest travel destinations based on the user's current living situation (e.g., a place where the user can relax if the user is under a lot of stress at work). The reception unit can also analyze the user's recent social media activity and suggest travel destinations of interest. For example, the reception unit can analyze the user's recent social media activity and suggest travel destinations of interest. This allows the system to suggest more appropriate travel destinations by presenting input candidates based on the user's current living situation and areas of interest.

[0066] The reception unit can estimate the user's emotions and determine the priority of input based on the estimated user's emotions. For example, if the user is nervous, the reception unit can cause the user to input the most important information (travel destination and duration) first. For example, if the user is nervous, the reception unit can cause the user to input the most important information (travel destination and duration) first. Furthermore, if the user is relaxed, the reception unit can cause the user to input more detailed information (accommodation and activities) later. For example, if the user is relaxed, the reception unit can cause the user to input more detailed information (accommodation and activities) later. Furthermore, if the user is in a hurry, the reception unit can cause the user to input only the minimum necessary information and allow the user to add more details later. For example, if the user is in a hurry, the reception unit can cause the user to input only the minimum necessary information and allow the user to add more details later. In this way, by determining the priority of input according to the user's emotions, it is possible to improve input efficiency.

[0067] When a travel destination and a travel period are input, the reception unit can prioritize displaying highly relevant candidates based on the user's geographical location information. The reception unit, for example, can prioritize displaying travel destinations close to the user's current location. For example, the reception unit can prioritize displaying travel destinations close to the user's current location. The reception unit can also suggest optimal travel destinations based on the climate and season of the user's current location. For example, the reception unit can suggest optimal travel destinations based on the climate and season of the user's current location. The reception unit can also suggest easily accessible travel destinations by taking into account the mode of transportation (plane, train, etc.) from the user's current location. For example, the reception unit can suggest easily accessible travel destinations by taking into account the mode of transportation (plane, train, etc.) from the user's current location. In this way, more relevant travel destinations can be suggested by taking into account the user's geographical location information.

[0068] The reception unit can analyze the user's social media activity when the user inputs the travel destination and period and present related candidates. The reception unit, for example, can suggest places related to travel destinations that the user has recently "liked" or commented on. For example, the reception unit can suggest places related to travel destinations that the user has recently "liked" or commented on. The reception unit can also suggest places visited by influencers the user follows. For example, the reception unit can suggest places visited by influencers the user follows. The reception unit can also analyze and suggest travel destinations of interest from the content of the user's social media posts. For example, the reception unit can analyze and suggest travel destinations of interest from the content of the user's social media posts. In this way, by analyzing the user's social media activity, it is possible to suggest travel destinations that are more interesting to the user.

[0069] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, when the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, when the user is nervous, the analysis unit can provide a simple and highly visible display method. Furthermore, when the user is relaxed, the analysis unit can also provide a display method including detailed information. For example, when the user is relaxed, the analysis unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. For example, when the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. In this way, by providing a display method according to the user's emotions, the visibility of the analysis results can be improved.

[0070] During analysis, the analysis unit can improve prediction accuracy based on past weather data for the travel destination. The analysis unit, for example, predicts the weather during the travel period based on weather data from the past few years. For example, the analysis unit can predict the weather during the travel period based on weather data from the past few years. The analysis unit can also analyze past weather data to identify weather patterns for a specific season. For example, the analysis unit can analyze past weather data to identify weather patterns for a specific season. The analysis unit can also predict climatic risks (typhoons, heavy rain, etc.) at the travel destination based on past weather data. For example, the analysis unit can predict climatic risks (typhoons, heavy rain, etc.) at the travel destination based on past weather data. In this way, by referring to past weather data, the accuracy of weather predictions during the travel period can be improved.

[0071] During analysis, the analysis unit can suggest services or precautions taking into account the culture or customs of the travel destination. The analysis unit, for example, suggests appropriate clothing and manners based on the culture of the travel destination. For example, the analysis unit can suggest appropriate clothing and manners based on the culture of the travel destination. The analysis unit can also provide information on specific events and festivals based on the customs of the travel destination. For example, the analysis unit can provide information on specific events and festivals based on the customs of the travel destination. The analysis unit can also suggest behaviors and language that should be taken into account, taking into account the cultural background of the travel destination. For example, the analysis unit can suggest behaviors and language that should be taken into account, taking into account the cultural background of the travel destination. In this way, more appropriate services and precautions can be suggested by taking into account the culture and customs of the travel destination.

[0072] The analysis unit can estimate the user's emotions and determine the priority of the analysis results based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can display the most important information (weather risks and safety information) first. For example, if the user is nervous, the analysis unit can display the most important information (weather risks and safety information) first. Furthermore, if the user is relaxed, the analysis unit can display detailed information (tourist spot and event information) later. For example, if the user is relaxed, the analysis unit can display detailed information (tourist spot and event information) later. Furthermore, if the user is in a hurry, the analysis unit can display only the minimum necessary information and allow the user to add more details later. For example, if the user is in a hurry, the analysis unit can display only the minimum necessary information and allow the user to add more details later. In this way, by determining the priority according to the user's emotions, important information can be provided preferentially.

[0073] During the analysis, the analysis unit can suggest services or precautions taking into account the geographical features of the travel destination. For example, if the travel destination is a mountainous area, the analysis unit can suggest mountain climbing equipment and precautions. For example, if the travel destination is a mountainous area, the analysis unit can suggest mountain climbing equipment and precautions. Furthermore, if the travel destination is a coastal area, the analysis unit can suggest bringing a swimsuit and sunscreen. For example, if the travel destination is a coastal area, the analysis unit can suggest bringing a swimsuit and sunscreen. Furthermore, if the travel destination is an urban area, the analysis unit can provide information on transportation and congestion. For example, if the travel destination is an urban area, the analysis unit can provide information on transportation and congestion. In this way, more appropriate services and precautions can be suggested by taking into account the geographical features of the travel destination.

[0074] During analysis, the analysis unit can improve the accuracy of the analysis based on related literature for the travel destination. The analysis unit, for example, refers to the latest guidebooks and tourist information for the travel destination. For example, the analysis unit can refer to the latest guidebooks and tourist information for the travel destination. The analysis unit can also provide detailed information based on academic papers and research materials for the travel destination. For example, the analysis unit can provide detailed information based on academic papers and research materials for the travel destination. The analysis unit can also analyze user reviews and ratings for the travel destination to provide highly reliable information. For example, the analysis unit can analyze user reviews and ratings for the travel destination to provide highly reliable information. In this way, by referring to related literature, the accuracy of the analysis can be improved.

[0075] The providing unit can estimate the user's emotions and adjust the display method of the services and warnings to be provided based on the estimated user's emotions. For example, when the user is nervous, the providing unit can provide a simple and highly visible display method. For example, when the user is nervous, the providing unit can provide a simple and highly visible display method. Furthermore, when the user is relaxed, the providing unit can also provide a display method including detailed information. For example, when the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. For example, when the user is in a hurry, the providing unit can provide a display method that focuses on the main points. In this way, by providing a display method according to the user's emotions, it is possible to improve the visibility of services and warnings.

[0076] The providing unit can, at the time of providing, suggest services or precautions based on the user's past travel experience. The providing unit, for example, suggests an optimal service based on services (car rental, hotel, etc.) that the user has used in the past. For example, the providing unit can suggest an optimal service based on services (car rental, hotel, etc.) that the user has used in the past. The providing unit can also suggest precautions for specific risks (for example, typhoon season) based on the user's past travel experience. For example, the providing unit can suggest precautions for specific risks (for example, typhoon season) based on the user's past travel experience. The providing unit can also analyze the user's past travel experience and suggest the most efficient service. For example, the providing unit can analyze the user's past travel experience and suggest the most efficient service. In this way, more appropriate services and precautions can be suggested by taking the user's past travel experience into consideration.

[0077] The providing unit can customize services or precautions based on the user's current living situation when providing the services or precautions. For example, if the user is feeling stressed at work, the providing unit can suggest a relaxing service (spa, massage, etc.). For example, if the user is feeling stressed at work, the providing unit can suggest a relaxing service (spa, massage, etc.). Furthermore, if the user is planning a family trip, the providing unit can suggest family-oriented services (kids club, family room, etc.). For example, if the user is planning a family trip, the providing unit can suggest family-oriented services (kids club, family room, etc.). Furthermore, the providing unit can suggest activities (hiking, diving, etc.) if the user is planning an active trip. For example, if the user is planning an active trip, the providing unit can suggest activities (hiking, diving, etc.). This makes it possible to improve the convenience of travel by providing services and precautions based on the user's current living situation.

[0078] The providing unit can estimate the user's emotions and determine the priority of services and precautions to be provided based on the estimated user's emotions. For example, when the user is nervous, the providing unit can first display the most important services (travel insurance, safety information). For example, when the user is nervous, the providing unit can first display the most important services (travel insurance, safety information). Furthermore, when the user is relaxed, the providing unit can later display detailed information (tourist attraction and event information). For example, when the user is relaxed, the providing unit can later display detailed information (tourist attraction and event information). Furthermore, when the user is in a hurry, the providing unit can display only the minimum necessary information and allow the user to add details later. For example, when the user is in a hurry, the providing unit can display only the minimum necessary information and allow the user to add details later. In this way, by determining the priority according to the user's emotions, important information can be preferentially provided.

[0079] The providing unit can suggest services or precautions based on the user's geographical location information at the time of suggestion. The providing unit, for example, prioritizes suggesting services (car rentals, hotels, etc.) that are close to the user's current location. For example, the providing unit can prioritize suggesting services (car rentals, hotels, etc.) that are close to the user's current location. The providing unit can also suggest optimal services based on the climate and season of the user's current location. For example, the providing unit can suggest optimal services based on the climate and season of the user's current location. The providing unit can also suggest services that are easy to access by taking into account the mode of transportation (plane, train, etc.) from the user's current location. For example, the providing unit can suggest services that are easy to access by taking into account the mode of transportation (plane, train, etc.) from the user's current location. In this way, more relevant services and precautions can be suggested by taking into account the user's geographical location information.

[0080] At the time of providing, the providing unit may analyze the user's social media activity and suggest related services or precautions. For example, the providing unit may suggest information related to services that the user has recently "liked" or commented on. For example, the providing unit may suggest information related to services that the user has recently "liked" or commented on. The providing unit may also suggest services used by influencers the user follows. For example, the providing unit may suggest services used by influencers the user follows. The providing unit may also analyze and suggest services of interest from the content of the user's social media posts. For example, the providing unit may analyze and suggest services of interest from the content of the user's social media posts. In this way, by analyzing the user's social media activity, it is possible to suggest services and precautions that are of greater interest to the user. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and the user inputs the travel destination and duration. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information and suggests services and precautions related to the travel destination. The provision unit is realized, for example, by the output device 40 of the smart device 14, and provides the user with services and precautions based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, and the user inputs the travel destination and duration by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information and suggests services and precautions related to the travel destination. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214, and provides the user with services and precautions by voice based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314, and the user inputs the travel destination and duration by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information and suggests services and precautions related to the travel destination. The provision unit is realized, for example, by the display 343 of the headset terminal 314, and displays services and precautions based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and the user inputs the travel destination and duration by voice. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input information and suggests services and precautions related to the travel destination. The provision unit is realized, for example, by the speaker 240 of the robot 414, and provides the user with services and precautions by voice based on the analysis results.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The analysis unit can also analyze the user's past travel history and provide information useful for selecting a travel destination. For example, the analysis unit can suggest similar travel destinations based on ratings and reviews of travel destinations the user has visited in the past. The analysis unit can also suggest new related activities and services by taking into account the user's activity and service usage history at travel destinations visited in the past. Furthermore, the analysis unit can predict and suggest travel destinations related to specific seasons or events based on the user's past travel history. This makes it possible to utilize the user's past travel history to support more personalized travel planning.

[0083] The providing unit can also suggest health management services at the travel destination, taking into account the user's current health condition. For example, if the user has a specific allergy, the providing unit can suggest restaurants and accommodations that cater to allergies. In addition, if the user has a chronic illness, the providing unit can provide information on local medical institutions and pharmacies. Furthermore, if the user needs to exercise to maintain their health, the providing unit can provide information on local fitness facilities and jogging courses. This allows the user to reduce health risks during travel by providing services tailored to their health condition.

[0084] The analysis unit can also estimate the user's emotions and assist in selecting a travel destination based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can suggest a travel destination where the user can relax. If the user is excited, the analysis unit can also suggest a travel destination where the user can enjoy active activities. Furthermore, if the user is sad, the analysis unit can also suggest a travel destination that provides healing. In this way, travel destinations that correspond to the user's emotions can be suggested, thereby improving travel satisfaction.

[0085] The providing unit can also provide information about transportation means at the user's travel destination. For example, the providing unit can provide information about how to use local public transportation and timetables. The providing unit can also suggest how to use local taxis and ride-sharing services. Furthermore, the providing unit can also suggest how to use local bicycle rentals and electric scooters. This allows the user to efficiently select a means of transportation at the travel destination, improving the convenience of the trip.

[0086] The analysis unit can also estimate the user's emotions and suggest activities at the travel destination based on the estimated user emotions. For example, if the user wants to relax, the analysis unit can suggest relaxation activities such as spas and hot springs. If the user wants to stay active, the analysis unit can suggest outdoor activities such as hiking and cycling. Furthermore, if the user wants a cultural experience, the analysis unit can suggest visiting local museums and art galleries. This makes it possible to improve travel satisfaction by suggesting activities that correspond to the user's emotions.

[0087] The providing unit can also provide information about meals at the user's travel destination. For example, the providing unit can provide information about popular local restaurants and cafes. The providing unit can also suggest restaurants that match the user's food preferences. Furthermore, the providing unit can also provide information about local food culture and local specialties. This allows the user to enjoy meals at the travel destination and improves the user's satisfaction with the trip.

[0088] The analysis unit can also estimate the user's emotions and suggest accommodations at the travel destination based on the estimated user emotions. For example, if the user wants to relax, the analysis unit can suggest quiet and peaceful accommodations. If the user wants to be active, the analysis unit can suggest resort hotels with plenty of activities. Furthermore, if the user is looking for a cultural experience, the analysis unit can suggest accommodations in historical buildings. In this way, suggesting accommodations that match the user's emotions can improve travel satisfaction.

[0089] The providing unit can also provide information about shopping at the user's travel destination. For example, the providing unit can provide information about popular local shopping areas and markets. The providing unit can also suggest shopping spots that match the user's interests. Furthermore, the providing unit can also provide information about local specialties and souvenirs. This allows the user to enjoy shopping at the travel destination and improves the user's satisfaction with the trip.

[0090] The analysis unit can also estimate the user's emotions and suggest events at the travel destination based on the estimated user emotions. For example, if the user is excited, the analysis unit can suggest music festivals or sporting events. If the user wants to relax, the analysis unit can suggest art exhibitions or film screenings. Furthermore, if the user is looking for a cultural experience, the analysis unit can suggest local traditional festivals or performances. This makes it possible to improve travel satisfaction by suggesting events that match the user's emotions.

[0091] The providing unit can also provide safety information for the user's travel destination. For example, the providing unit can provide local security information and emergency contact information. The providing unit can also provide advice for the user to take safety measures at their travel destination. Furthermore, the providing unit can also provide information on local medical institutions and police stations. This allows the user to ensure safety at their travel destination and enjoy their trip with peace of mind.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The reception unit receives the user's input of the travel destination and duration. The user can input the departure point, destination, and travel duration. For example, the user may input "I would like to travel from Tokyo to Okinawa for 5 days." This information is entered into the system. Step 2: The analysis unit analyzes the information received by the reception unit and suggests services and precautions related to the travel destination. The analysis unit suggests services and precautions taking into account the climate, season, and local event information of the travel destination. For example, if it is typhoon season, it will suggest travel insurance and cancellation insurance, and also provide information on local festivals, concerts, sporting events, etc. Step 3: The provider provides the services and precautions suggested by the analyzer. Based on the user's travel plans, the provider suggests services that can be used locally. For example, it suggests car rental services, hotel reservations, and restaurant reservations.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] 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.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] 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.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0125] 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.

[0126] 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.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for receiving input of a travel destination and period; an analysis unit that analyzes the information received by the reception unit and proposes services or precautions related to the travel destination; a providing unit that provides the service or precautions suggested by the analysis unit. A system characterized by:

2. The analysis unit Suggest services or precautions taking into account the climate or season of the destination and local event information 2. The system of claim 1.

3. The providing unit If it's typhoon season, offer travel or cancellation insurance.

2. The system of claim 1.

4. The providing unit Providing information about local events or tourist attractions 2. The system of claim 1.

5. The providing unit Offer car rental services, hotel reservations, or restaurant reservations 2. The system of claim 1.

6. The providing unit Suggesting locally available services based on the user's travel plans 2. The system of claim 1.

7. The reception unit The system estimates the user's emotions and customizes the input interface for the travel destination and duration based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past travel history and suggests input methods 2. The system of claim 1.

Images