system

Abstract

The system according to the embodiment aims to enable travelers to centrally plan their travel plans, gather information on-site, and respond to emergencies. [Solution] The system according to the embodiment comprises a suggestion unit, a traffic information providing unit, an emergency response guidance unit, a rating linking unit, and a payment linking unit. The suggestion unit learns traveler preferences and past behavioral data to suggest appropriate travel plans. The traffic information providing unit provides real-time traffic information. The emergency response guidance unit provides guidance on how to respond in an emergency. The rating linking unit links with a restaurant rating app to find popular local restaurants. The payment linking unit links with an electronic payment app to make smooth payments.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult for travelers to centrally plan their trips, gather information on-site, and respond to emergencies.

[0005] The system according to the embodiment aims to enable travelers to centrally plan their travel plans, gather information on-site, and respond to emergencies. [Means for solving the problem]

[0006] The system according to the embodiment includes a suggestion unit, a traffic information providing unit, an emergency response guidance unit, a rating linking unit, and a payment linking unit. The suggestion unit learns traveler preferences and past behavioral data to suggest appropriate travel plans. The traffic information providing unit provides real-time traffic information. The emergency response guidance unit provides guidance on how to respond in an emergency. The rating linking unit links with a restaurant rating app to find popular local restaurants. The payment linking unit links with an electronic payment app to make smooth payments. [Effects of the Invention]

[0007] The system according to the embodiment allows travelers to centrally plan their trip, gather information on-site, and respond to emergencies. [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 smartphone app system for inbound travelers equipped with a Japan guide, according to an embodiment of the present invention, allows travelers to efficiently plan and navigate their trips to Japan, respond to emergencies, find popular local restaurants, and make payments smoothly. This system includes a proposal unit that learns travelers' preferences and past behavioral data to propose optimal travel plans, a transportation information provider that provides real-time transportation information, an emergency response guidance unit that provides emergency response instructions, a rating integration unit that connects with restaurant review apps to find popular local restaurants, and a payment integration unit that connects with electronic payment apps to make payments smoothly. For example, when a traveler plans a trip to Japan, AI learns the traveler's preferences and past behavioral data to propose optimal travel plans. Next, to support travel during the trip, the system provides real-time transportation information and emergency response instructions. Furthermore, to address local rules and issues, the system provides information on Japanese culture, etiquette, and emergency response methods, and includes a translation function to support communication across language barriers. Finally, the system connects with other domestic apps to make travelers' stays in Japan more convenient and enjoyable. For example, the system can connect with restaurant review apps to find popular local restaurants, and connect with electronic payment apps to make payments smoothly. This allows the Japan Guide smartphone app system for inbound tourists to efficiently plan their trip to Japan, navigate, respond to emergencies, find popular local shops, and make payments smoothly.

[0029] According to an embodiment, a smartphone app system for inbound tourists equipped with a Japan Guide app includes a suggestion unit, a transportation information provision unit, an emergency response guidance unit, a rating linkage unit, and a payment linkage unit. The suggestion unit learns a traveler's preferences and past behavioral data to suggest an optimal travel plan. The suggestion unit, for example, suggests optimal tourist spots and activities based on the traveler's past travel history and preferences. The suggestion unit can also filter based on the traveler's current interests and suggest highly relevant plans. For example, the suggestion unit can suggest tourist spots based on the traveler's current interests (e.g., history, nature, art). The suggestion unit can also estimate the traveler's emotions and adjust the proposed travel plan based on the estimated emotions. For example, if a traveler is feeling stressed, the suggestion unit can suggest tourist spots and activities that will help them relax. The transportation information provision unit provides real-time transportation information. For example, the transportation information provision unit can provide the closest transportation option to the traveler's current location. The transportation information provision unit can also provide optimal information by referring to the traveler's past travel history. For example, the transportation information providing unit can provide optimal transportation information based on the transportation means and routes the traveler has used in the past. The emergency response guidance unit provides guidance on how to respond in an emergency. For example, the emergency response guidance unit can provide the nearest emergency contact information to the traveler's current location. The emergency response guidance unit can also estimate the traveler's emotions and adjust emergency response methods based on the estimated emotions. For example, if the traveler is in a panic, the emergency response guidance unit can provide emergency response methods in a calm voice. The rating linking unit can link with a restaurant rating app to find popular local restaurants. For example, the rating linking unit can provide rating information for restaurants near the traveler's current location. The rating linking unit can also provide the optimal linking method by referring to the traveler's past rating history. For example, the rating linking unit can provide related rating information based on the traveler's past ratings of restaurants and tourist attractions. The payment linking unit can link with an electronic payment app to make smooth payments. For example, the payment linking unit can provide the optimal payment options based on the payment method the traveler is currently using.The payment linking unit can also estimate the traveler's emotions and adjust the payment linking method based on the estimated emotions. For example, if the traveler is feeling stressed, a simple and quick payment method can be provided. As a result, the Japan Guide smartphone app system for inbound travelers according to the embodiment allows travelers to efficiently plan their trip to Japan, travel, respond to emergencies, find popular local shops, and make payments smoothly.

[0030] The suggestion unit can analyze the traveler's past travel history and select an appropriate suggestion method. For example, the suggestion unit can suggest similar places and experiences based on tourist spots and activities that the traveler has visited in the past. The suggestion unit can also suggest similar dishes and restaurants based on meals and restaurants that the traveler has previously preferred. The suggestion unit can also suggest similar events based on events and festivals that the traveler has previously attended. This enables optimal suggestions based on the travel history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the traveler's past travel history data into the generation AI and cause the generation AI to select the optimal suggestion method.

[0031] When making suggestions, the suggestion unit can filter based on the traveler's current interests. For example, the suggestion unit can suggest tourist destinations based on themes in which the traveler is currently interested (e.g., history, nature, art). The suggestion unit can also suggest activities based on activities in which the traveler is currently interested (e.g., hiking, shopping, gourmet food). The suggestion unit can also suggest events based on events in which the traveler is currently interested (e.g., concerts, exhibitions, sporting events). This enables suggestions based on the traveler's current interests. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the traveler's current interests and interest data into the generation AI and have the generation AI perform filtering.

[0032] When making a proposal, the suggestion unit can prioritize highly relevant plans by taking into account the traveler's geographical location information. The suggestion unit, for example, prioritizes suggesting tourist attractions and activities close to the traveler's current location. The suggestion unit can also suggest local specialties and famous dishes in the area where the traveler is staying. The suggestion unit can also suggest transportation methods that are easily accessible from the traveler's current location. This makes it possible to propose an optimal plan based on the traveler's current location. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the traveler's geographical location information into the generation AI and cause the generation AI to prioritize highly relevant plans.

[0033] When making a suggestion, the suggestion unit can analyze the traveler's social media activity and suggest related plans. For example, the suggestion unit can suggest related tourist attractions and activities based on photos and posts shared by the traveler on social media. The suggestion unit can also suggest places visited by influencers or friends followed by the traveler. The suggestion unit can also suggest events and festivals in which the traveler has expressed interest on social media. This makes it possible to suggest optimal plans based on social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the traveler's social media activity data into a generation AI and have the generation AI suggest related plans.

[0034] When providing traffic information, the traffic information providing unit can provide appropriate information by referring to the traveler's past travel history. The traffic information providing unit, for example, provides optimal traffic information based on the means of transportation and routes used by the traveler in the past. The traffic information providing unit can also provide alternatives by taking into account means of transportation and routes that the traveler has avoided in the past. The traffic information providing unit can also analyze the traveler's past travel patterns and suggest the most efficient means of transportation. This makes it possible to provide optimal traffic information based on the past travel history. Some or all of the above-mentioned processing in the traffic information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the traffic information providing unit can input the traveler's past travel history data into the generation AI and cause the generation AI to provide optimal traffic information.

[0035] When providing traffic information, the traffic information providing unit can customize the information based on the traveler's current travel situation. For example, the traffic information providing unit provides next transfer information based on the means of transportation currently used by the traveler. The traffic information providing unit can also provide the traveler with the optimal route from their current location to their destination in real time. The traffic information providing unit can also provide the traveler with an estimated arrival time based on their current travel speed. This makes it possible to provide optimal traffic information based on their current travel situation. Some or all of the above-mentioned processing in the traffic information providing unit may be performed using AI, for example, or may be performed without using AI. For example, the traffic information providing unit can input the traveler's current travel situation data into the generation AI and have the generation AI customize the information.

[0036] When providing traffic information, the traffic information providing unit can provide appropriate information by taking into account the traveler's geographical location information. The traffic information providing unit, for example, provides the closest means of transportation from the traveler's current location. The traffic information providing unit can also provide the optimal route from the traveler's current location to the destination. The traffic information providing unit can also provide the traffic conditions at the traveler's current location in real time. This makes it possible to provide optimal traffic information based on the geographical location information. Some or all of the above-mentioned processing in the traffic information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the traffic information providing unit can input the traveler's geographical location information to the generation AI and cause the generation AI to provide optimal information.

[0037] The traffic information providing unit can analyze the traveler's social media activity and provide related information when providing traffic information. The traffic information providing unit can provide related traffic information based on, for example, means of transportation and routes shared by the traveler on social media. The traffic information providing unit can also suggest means of transportation used by influencers or friends followed by the traveler. The traffic information providing unit can also provide means of transportation and routes in which the traveler has shown interest on social media. This makes it possible to provide optimal traffic information based on social media activity. Some or all of the above-mentioned processing in the traffic information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the traffic information providing unit can input the traveler's social media activity data into a generation AI and cause the generation AI to provide related information.

[0038] When providing emergency response guidance, the emergency response guidance unit can refer to the traveler's past emergency response history and provide an appropriate method. The emergency response guidance unit can, for example, provide the optimal response method based on emergency situations the traveler has experienced in the past. The emergency response guidance unit can also prioritize displaying emergency contact information that the traveler has used in the past. The emergency response guidance unit can also analyze the traveler's past emergency response history and suggest the most effective response method. This makes it possible to provide the optimal emergency response method based on the past emergency response history. Some or all of the above-mentioned processing in the emergency response guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the emergency response guidance unit can input the traveler's past emergency response history data into a generation AI and have the generation AI provide the optimal emergency response method.

[0039] The emergency response guidance unit can customize a response method based on the traveler's current situation when providing emergency response guidance. The emergency response guidance unit, for example, provides emergency contact information for the traveler's current location. The emergency response guidance unit can also provide an emergency response method according to the traveler's current situation. The emergency response guidance unit can also provide the traveler with the optimal emergency response method based on the traveler's current health condition. This makes it possible to provide the optimal emergency response method based on the current situation. Some or all of the above-mentioned processing in the emergency response guidance unit may be performed using AI, for example, or may be performed without using AI. For example, the emergency response guidance unit can input the traveler's current situation data into the generation AI and have the generation AI customize the response method.

[0040] The emergency response guidance unit can provide an appropriate response method by taking into account the traveler's geographical location information when providing emergency response guidance. The emergency response guidance unit, for example, provides the nearest emergency contact information to the traveler's current location. The emergency response guidance unit can also provide the nearest medical institution to the traveler's current location. The emergency response guidance unit can also provide the nearest evacuation site to the traveler's current location. This makes it possible to provide an optimal emergency response method based on the geographical location information. Some or all of the above-mentioned processing in the emergency response guidance unit may be performed using AI, for example, or may be performed without using AI. For example, the emergency response guidance unit can input the traveler's geographical location information to the generation AI and have the generation AI provide the optimal response method.

[0041] The emergency response guidance unit can analyze the traveler's social media activity and provide relevant response methods when providing emergency response guidance. For example, the emergency response guidance unit can provide relevant response methods based on emergency situations shared by the traveler on social media. The emergency response guidance unit can also suggest emergency response methods used by influencers or friends followed by the traveler. The emergency response guidance unit can also provide emergency response methods in which the traveler has shown interest on social media. This makes it possible to provide the optimal emergency response method based on social media activity. Some or all of the above-mentioned processing in the emergency response guidance unit may be performed using AI, for example, or may be performed without using AI. For example, the emergency response guidance unit can input the traveler's social media activity data into a generation AI and cause the generation AI to provide relevant response methods.

[0042] When linking ratings, the rating linking unit can refer to the traveler's past rating history and provide an appropriate linking method. For example, the rating linking unit can provide related rating information based on restaurants and tourist attractions that the traveler has previously rated. The rating linking unit can also prioritize suggesting places that the traveler has previously given high ratings. The rating linking unit can also analyze the traveler's past rating history and provide the most relevant rating information. This makes it possible to provide an optimal rating linking method based on the past rating history. Some or all of the above-mentioned processing in the rating linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the rating linking unit can input the traveler's past rating history data into the generation AI and cause the generation AI to provide an optimal rating linking method.

[0043] The rating linking unit can customize the linking content based on the traveler's current interests and concerns when linking ratings. For example, the rating linking unit provides rating information based on themes (e.g., gourmet food, art, outdoors) in which the traveler is currently interested. The rating linking unit can also provide rating information based on activities (e.g., hiking, shopping, sightseeing) in which the traveler is currently interested. The rating linking unit can also provide rating information based on events (e.g., festivals, concerts, exhibitions) in which the traveler is currently interested. This makes it possible to provide an optimal rating linking method based on current interests and concerns. Some or all of the above-described processing in the rating linking unit may be performed using, or without, AI, for example. For example, the rating linking unit can input the traveler's current interest and concern data into the generation AI and have the generation AI customize the linking content.

[0044] The evaluation linking unit can provide an appropriate linking method by taking into account the traveler's geographical location information when linking ratings. The evaluation linking unit, for example, provides evaluation information of restaurants and tourist attractions close to the traveler's current location. The evaluation linking unit can also provide evaluation information of local specialties and famous dishes in the traveler's current location. The evaluation linking unit can also provide evaluation information that is easily accessible from the traveler's current location. This makes it possible to provide an optimal evaluation linking method based on geographical location information. Some or all of the above-mentioned processing in the evaluation linking unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation linking unit can input the traveler's geographical location information into the generation AI and cause the generation AI to provide the optimal linking method.

[0045] The rating linking unit can analyze the traveler's social media activity during rating linking and provide related linking content. For example, the rating linking unit can provide related rating information based on photos and posts shared by the traveler on social media. The rating linking unit can also suggest places rated by influencers or friends followed by the traveler. The rating linking unit can also provide rating information for restaurants and tourist spots that the traveler has shown interest in on social media. This makes it possible to provide an optimal rating linking method based on social media activity. Some or all of the above-mentioned processing in the rating linking unit may be performed using, or without, AI, for example. For example, the rating linking unit can input the traveler's social media activity data into the generation AI and cause the generation AI to provide related linking content.

[0046] During payment integration, the payment integration unit can refer to the traveler's past payment history and provide an appropriate integration method. For example, the payment integration unit can provide optimal payment options based on payment methods used by the traveler in the past. The payment integration unit can also provide alternatives by taking into account payment methods that the traveler has avoided in the past. The payment integration unit can also analyze the traveler's past payment history and suggest the most efficient payment method. This makes it possible to provide an optimal payment integration method based on the past payment history. Some or all of the above-mentioned processing in the payment integration unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment integration unit can input the traveler's past payment history data into the generation AI and have the generation AI provide the optimal payment integration method.

[0047] During payment integration, the payment integration unit can customize the integration content based on the traveler's current payment status. The payment integration unit, for example, provides the optimal payment option based on the payment method currently used by the traveler. The payment integration unit can also provide the traveler with a payment method that suits their current payment status. The payment integration unit can also provide the traveler with the optimal payment method based on their current payment history. This makes it possible to provide the optimal payment integration method based on their current payment status. Some or all of the above-mentioned processing in the payment integration unit may be performed using AI, for example, or may be performed without using AI. For example, the payment integration unit can input the traveler's current payment status data into the generation AI and have the generation AI customize the integration content.

[0048] The payment linking unit can provide an appropriate linking method during payment linking, taking into account the traveler's geographical location information. The payment linking unit, for example, provides the payment method closest to the traveler's current location. The payment linking unit can also provide payment information for local specialties and specialty dishes in the traveler's current location. The payment linking unit can also provide payment information that is easily accessible from the traveler's current location. This makes it possible to provide an optimal payment linking method based on geographical location information. Some or all of the above-mentioned processing in the payment linking unit may be performed using AI, for example, or may be performed without using AI. For example, the payment linking unit can input the traveler's geographical location information into the generation AI and cause the generation AI to provide the optimal linking method.

[0049] The payment integration unit can analyze the traveler's social media activity during payment integration and provide related integration content. For example, the payment integration unit can provide related payment information based on the payment methods and stores the traveler has shared on social media. The payment integration unit can also suggest payment methods used by influencers or friends the traveler follows. The payment integration unit can also provide information on payment methods and stores that the traveler has shown interest in on social media. This makes it possible to provide the optimal payment integration method based on social media activity. Some or all of the above-mentioned processing in the payment integration unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment integration unit can input the traveler's social media activity data into the generation AI and have the generation AI provide related integration content.

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

[0051] The suggestion unit can propose travel plans taking into account the cultural background of the traveler. For example, if the traveler is interested in a particular culture or religion, it can suggest tourist spots and events related to that culture or religion. Also, if the traveler is interested in a particular food culture, it can suggest restaurants and dishes related to that food culture. Furthermore, if the traveler has a particular historical background, it can suggest tourist spots and museums related to that background. This makes it possible to provide the optimal travel plan according to the traveler's cultural background.

[0052] The transportation information providing unit can suggest transportation methods taking into account the eco-consciousness of the traveler. For example, if the traveler desires an environmentally friendly means of transportation, it can suggest the use of public transportation or bicycles. Also, if the traveler wants to reduce their carbon footprint, it can suggest eco-friendly transportation methods with priority. Furthermore, if the traveler is interested in eco-tourism, it can suggest environmentally friendly tourist spots and activities. In this way, it is possible to provide optimal transportation information according to the traveler's eco-consciousness.

[0053] The emergency response information section can provide emergency response instructions taking into account the traveler's language ability. For example, if the traveler does not speak English, emergency response instructions can be provided in the traveler's native language. Also, if the traveler speaks multiple languages, emergency response instructions can be provided in the language that the traveler can understand most easily. Furthermore, if the traveler uses sign language, emergency response instructions can be provided in sign language. This makes it possible to provide the most appropriate emergency response instructions according to the traveler's language ability.

[0054] The evaluation linking unit can provide evaluation information taking into consideration the traveler's dietary restrictions. For example, if the traveler is vegetarian, evaluation information on vegetarian-friendly restaurants can be provided. Also, if the traveler has allergies, evaluation information on restaurants that cater to allergies can be provided. Furthermore, if the traveler is interested in a particular food culture, evaluation information on restaurants related to that food culture can be provided. This makes it possible to provide optimal evaluation information according to the traveler's dietary restrictions.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The suggestion unit learns the traveler's preferences and past behavioral data to propose the optimal travel plan. The suggestion unit suggests the best tourist spots and activities based on the traveler's past travel history and preferences. It can also filter based on the traveler's current interests and propose highly relevant plans. It can also estimate the traveler's emotions and adjust the proposed travel plan based on the estimated emotions. Step 2: The traffic information provider provides real-time traffic information. The traffic information provider can provide the closest transportation option to the traveler's current location. It can also provide optimal information by referring to the traveler's past travel history. Step 3: The emergency response guidance unit provides emergency response instructions. The emergency response guidance unit can provide the nearest emergency contact point to the traveler's current location. It can also estimate the traveler's emotions and adjust emergency response instructions based on the estimated emotions. Step 4: The rating linking unit links with restaurant rating apps to find popular local restaurants. The rating linking unit can provide rating information for restaurants close to the traveler's current location. It can also refer to the traveler's past rating history to provide the optimal linking method. Step 5: The payment integration unit integrates with the electronic payment app to facilitate payment. The payment integration unit can provide optimal payment options based on the payment method currently used by the traveler. It can also estimate the traveler's emotions and adjust the payment integration method based on the estimated emotions.

[0057] (Example 2) A smartphone app system for inbound travelers equipped with a Japan guide, according to an embodiment of the present invention, allows travelers to efficiently plan and navigate their trips to Japan, respond to emergencies, find popular local restaurants, and make payments smoothly. This system includes a proposal unit that learns travelers' preferences and past behavioral data to propose optimal travel plans, a transportation information provider that provides real-time transportation information, an emergency response guidance unit that provides emergency response instructions, a rating integration unit that connects with restaurant review apps to find popular local restaurants, and a payment integration unit that connects with electronic payment apps to make payments smoothly. For example, when a traveler plans a trip to Japan, AI learns the traveler's preferences and past behavioral data to propose optimal travel plans. Next, to support travel during the trip, the system provides real-time transportation information and emergency response instructions. Furthermore, to address local rules and issues, the system provides information on Japanese culture, etiquette, and emergency response methods, and includes a translation function to support communication across language barriers. Finally, the system connects with other domestic apps to make travelers' stays in Japan more convenient and enjoyable. For example, the system can connect with restaurant review apps to find popular local restaurants, and connect with electronic payment apps to make payments smoothly. This allows the Japan Guide smartphone app system for inbound tourists to efficiently plan their trip to Japan, navigate, respond to emergencies, find popular local shops, and make payments smoothly.

[0058] According to an embodiment, a smartphone app system for inbound tourists equipped with a Japan Guide app includes a suggestion unit, a transportation information provision unit, an emergency response guidance unit, a rating linkage unit, and a payment linkage unit. The suggestion unit learns a traveler's preferences and past behavioral data to suggest an optimal travel plan. The suggestion unit, for example, suggests optimal tourist spots and activities based on the traveler's past travel history and preferences. The suggestion unit can also filter based on the traveler's current interests and suggest highly relevant plans. For example, the suggestion unit can suggest tourist spots based on the traveler's current interests (e.g., history, nature, art). The suggestion unit can also estimate the traveler's emotions and adjust the proposed travel plan based on the estimated emotions. For example, if a traveler is feeling stressed, the suggestion unit can suggest tourist spots and activities that will help them relax. The transportation information provision unit provides real-time transportation information. For example, the transportation information provision unit can provide the closest transportation option to the traveler's current location. The transportation information provision unit can also provide optimal information by referring to the traveler's past travel history. For example, the transportation information providing unit can provide optimal transportation information based on the transportation means and routes the traveler has used in the past. The emergency response guidance unit provides guidance on how to respond in an emergency. For example, the emergency response guidance unit can provide the nearest emergency contact information to the traveler's current location. The emergency response guidance unit can also estimate the traveler's emotions and adjust emergency response methods based on the estimated emotions. For example, if the traveler is in a panic, the emergency response guidance unit can provide emergency response methods in a calm voice. The rating linking unit can link with a restaurant rating app to find popular local restaurants. For example, the rating linking unit can provide rating information for restaurants near the traveler's current location. The rating linking unit can also provide the optimal linking method by referring to the traveler's past rating history. For example, the rating linking unit can provide related rating information based on the traveler's past ratings of restaurants and tourist attractions. The payment linking unit can link with an electronic payment app to make smooth payments. For example, the payment linking unit can provide the optimal payment options based on the payment method the traveler is currently using.The payment linking unit can also estimate the traveler's emotions and adjust the payment linking method based on the estimated emotions. For example, if the traveler is feeling stressed, a simple and quick payment method can be provided. As a result, the Japan Guide smartphone app system for inbound travelers according to the embodiment allows travelers to efficiently plan their trip to Japan, travel, respond to emergencies, find popular local shops, and make payments smoothly.

[0059] The suggestion unit can estimate the traveler's emotions and adjust the proposed travel plan based on the estimated traveler's emotions. For example, if the traveler is feeling stressed, the suggestion unit can suggest relaxing tourist spots and activities. If the traveler is excited, the suggestion unit can also suggest adventurous or exciting activities. If the traveler is tired, the suggestion unit can also suggest resting places and relaxation facilities. This makes it possible to propose an optimal travel plan based on the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the traveler's emotion data into the generation AI and cause the generation AI to adjust the proposed travel plan based on the emotion.

[0060] The suggestion unit can analyze the traveler's past travel history and select an appropriate suggestion method. For example, the suggestion unit can suggest similar places and experiences based on tourist spots and activities that the traveler has visited in the past. The suggestion unit can also suggest similar dishes and restaurants based on meals and restaurants that the traveler has previously preferred. The suggestion unit can also suggest similar events based on events and festivals that the traveler has previously attended. This enables optimal suggestions based on the travel history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the traveler's past travel history data into the generation AI and cause the generation AI to select the optimal suggestion method.

[0061] When making suggestions, the suggestion unit can filter based on the traveler's current interests. For example, the suggestion unit can suggest tourist destinations based on themes in which the traveler is currently interested (e.g., history, nature, art). The suggestion unit can also suggest activities based on activities in which the traveler is currently interested (e.g., hiking, shopping, gourmet food). The suggestion unit can also suggest events based on events in which the traveler is currently interested (e.g., concerts, exhibitions, sporting events). This enables suggestions based on the traveler's current interests. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the traveler's current interests and interest data into the generation AI and have the generation AI perform filtering.

[0062] The suggestion unit can estimate the traveler's emotions and prioritize the itinerary to be proposed based on the estimated traveler's emotions. For example, if the traveler wants to relax, the suggestion unit can prioritize relaxation facilities and natural scenery. If the traveler wants to be active, the suggestion unit can prioritize activities and sporting events. If the traveler wants to have a cultural experience, the suggestion unit can prioritize museums and traditional events. This allows the itinerary to be proposed based on the traveler's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit may be performed using AI, or without AI. For example, the suggestion unit can input the traveler's emotion data into the generation AI and cause the generation AI to prioritize the itinerary based on the emotion.

[0063] When making a proposal, the suggestion unit can prioritize highly relevant plans by taking into account the traveler's geographical location information. The suggestion unit, for example, prioritizes suggesting tourist attractions and activities close to the traveler's current location. The suggestion unit can also suggest local specialties and famous dishes in the area where the traveler is staying. The suggestion unit can also suggest transportation methods that are easily accessible from the traveler's current location. This makes it possible to propose an optimal plan based on the traveler's current location. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the traveler's geographical location information into the generation AI and cause the generation AI to prioritize highly relevant plans.

[0064] When making a suggestion, the suggestion unit can analyze the traveler's social media activity and suggest related plans. For example, the suggestion unit can suggest related tourist attractions and activities based on photos and posts shared by the traveler on social media. The suggestion unit can also suggest places visited by influencers or friends followed by the traveler. The suggestion unit can also suggest events and festivals in which the traveler has expressed interest on social media. This makes it possible to suggest optimal plans based on social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the traveler's social media activity data into a generation AI and have the generation AI suggest related plans.

[0065] The traffic information providing unit can estimate the traveler's emotions and adjust the method of providing traffic information based on the estimated traveler's emotions. For example, if the traveler is stressed, the traffic information providing unit can provide simple and easy-to-understand traffic information. Furthermore, if the traveler is relaxed, the traffic information providing unit can also provide detailed traffic information. Furthermore, if the traveler is in a hurry, the traffic information providing unit can prioritize the shortest route or the fastest means of transportation. This allows optimal traffic information to be provided according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the traffic information providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the traffic information providing unit can input traveler's emotion data into the generation AI and cause the generation AI to adjust the method of providing traffic information based on the emotion.

[0066] When providing traffic information, the traffic information providing unit can provide appropriate information by referring to the traveler's past travel history. The traffic information providing unit, for example, provides optimal traffic information based on the means of transportation and routes used by the traveler in the past. The traffic information providing unit can also provide alternatives by taking into account means of transportation and routes that the traveler has avoided in the past. The traffic information providing unit can also analyze the traveler's past travel patterns and suggest the most efficient means of transportation. This makes it possible to provide optimal traffic information based on the past travel history. Some or all of the above-mentioned processing in the traffic information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the traffic information providing unit can input the traveler's past travel history data into the generation AI and cause the generation AI to provide optimal traffic information.

[0067] When providing traffic information, the traffic information providing unit can customize the information based on the traveler's current travel situation. For example, the traffic information providing unit provides next transfer information based on the means of transportation currently used by the traveler. The traffic information providing unit can also provide the traveler with the optimal route from their current location to their destination in real time. The traffic information providing unit can also provide the traveler with an estimated arrival time based on their current travel speed. This makes it possible to provide optimal traffic information based on their current travel situation. Some or all of the above-mentioned processing in the traffic information providing unit may be performed using AI, for example, or may be performed without using AI. For example, the traffic information providing unit can input the traveler's current travel situation data into the generation AI and have the generation AI customize the information.

[0068] The traffic information providing unit can estimate the traveler's emotions and prioritize traffic information based on the estimated traveler's emotions. For example, if the traveler is feeling stressed, the traffic information providing unit can prioritize providing the simplest and most understandable information. Furthermore, if the traveler is relaxed, the traffic information providing unit can prioritize providing detailed information. Furthermore, if the traveler is in a hurry, the traffic information providing unit can prioritize providing the fastest means of transportation or the shortest route. This allows traffic information to be provided in a prioritized order according to the traveler's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the traffic information providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the traffic information providing unit can input the traveler's emotion data into the generation AI and cause the generation AI to prioritize traffic information based on the emotion.

[0069] When providing traffic information, the traffic information providing unit can provide appropriate information by taking into account the traveler's geographical location information. The traffic information providing unit, for example, provides the closest means of transportation from the traveler's current location. The traffic information providing unit can also provide the optimal route from the traveler's current location to the destination. The traffic information providing unit can also provide the traffic conditions at the traveler's current location in real time. This makes it possible to provide optimal traffic information based on the geographical location information. Some or all of the above-mentioned processing in the traffic information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the traffic information providing unit can input the traveler's geographical location information to the generation AI and cause the generation AI to provide optimal information.

[0070] The traffic information providing unit can analyze the traveler's social media activity and provide related information when providing traffic information. The traffic information providing unit can provide related traffic information based on, for example, means of transportation and routes shared by the traveler on social media. The traffic information providing unit can also suggest means of transportation used by influencers or friends followed by the traveler. The traffic information providing unit can also provide means of transportation and routes in which the traveler has shown interest on social media. This makes it possible to provide optimal traffic information based on social media activity. Some or all of the above-mentioned processing in the traffic information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the traffic information providing unit can input the traveler's social media activity data into a generation AI and cause the generation AI to provide related information.

[0071] The emergency response guidance unit can estimate the traveler's emotions and adjust emergency response methods based on the estimated traveler's emotions. For example, if the traveler is in a panic, the emergency response guidance unit can provide emergency response methods in a calm voice. Furthermore, if the traveler is calm, the emergency response guidance unit can provide detailed emergency response methods. Furthermore, if the traveler is feeling anxious, the emergency response guidance unit can provide emergency response methods that give the traveler a sense of security. This makes it possible to provide an optimal emergency response method according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the emergency response guidance unit may be performed using, for example, AI, or without AI. For example, the emergency response guidance unit can input the traveler's emotion data into the generation AI and have the generation AI adjust the emergency response method based on the emotion.

[0072] When providing emergency response guidance, the emergency response guidance unit can refer to the traveler's past emergency response history and provide an appropriate method. The emergency response guidance unit can, for example, provide the optimal response method based on emergency situations the traveler has experienced in the past. The emergency response guidance unit can also prioritize displaying emergency contact information that the traveler has used in the past. The emergency response guidance unit can also analyze the traveler's past emergency response history and suggest the most effective response method. This makes it possible to provide the optimal emergency response method based on the past emergency response history. Some or all of the above-mentioned processing in the emergency response guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the emergency response guidance unit can input the traveler's past emergency response history data into a generation AI and have the generation AI provide the optimal emergency response method.

[0073] The emergency response guidance unit can customize a response method based on the traveler's current situation when providing emergency response guidance. The emergency response guidance unit, for example, provides emergency contact information for the traveler's current location. The emergency response guidance unit can also provide an emergency response method according to the traveler's current situation. The emergency response guidance unit can also provide the traveler with the optimal emergency response method based on the traveler's current health condition. This makes it possible to provide the optimal emergency response method based on the current situation. Some or all of the above-mentioned processing in the emergency response guidance unit may be performed using AI, for example, or may be performed without using AI. For example, the emergency response guidance unit can input the traveler's current situation data into the generation AI and have the generation AI customize the response method.

[0074] The emergency response guidance unit can estimate the traveler's emotions and prioritize emergency response methods based on the estimated traveler's emotions. For example, if the traveler is in a panic, the emergency response guidance unit can prioritize providing the simplest and quickest response method. Furthermore, if the traveler is calm, the emergency response guidance unit can prioritize providing detailed response methods. Furthermore, if the traveler is feeling anxious, the emergency response guidance unit can prioritize response methods that provide a sense of security. This allows emergency response methods to be provided in order of priority according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the emergency response guidance unit may be performed using, for example, AI, or without AI. For example, the emergency response guidance unit can input traveler's emotion data into the generation AI and have the generation AI prioritize emergency response methods based on emotions.

[0075] The emergency response guidance unit can provide an appropriate response method by taking into account the traveler's geographical location information when providing emergency response guidance. The emergency response guidance unit, for example, provides the nearest emergency contact information to the traveler's current location. The emergency response guidance unit can also provide the nearest medical institution to the traveler's current location. The emergency response guidance unit can also provide the nearest evacuation site to the traveler's current location. This makes it possible to provide an optimal emergency response method based on the geographical location information. Some or all of the above-mentioned processing in the emergency response guidance unit may be performed using AI, for example, or may be performed without using AI. For example, the emergency response guidance unit can input the traveler's geographical location information to the generation AI and have the generation AI provide the optimal response method.

[0076] The emergency response guidance unit can analyze the traveler's social media activity and provide relevant response methods when providing emergency response guidance. For example, the emergency response guidance unit can provide relevant response methods based on emergency situations shared by the traveler on social media. The emergency response guidance unit can also suggest emergency response methods used by influencers or friends followed by the traveler. The emergency response guidance unit can also provide emergency response methods in which the traveler has shown interest on social media. This makes it possible to provide the optimal emergency response method based on social media activity. Some or all of the above-mentioned processing in the emergency response guidance unit may be performed using AI, for example, or may be performed without using AI. For example, the emergency response guidance unit can input the traveler's social media activity data into a generation AI and cause the generation AI to provide relevant response methods.

[0077] The rating linking unit can estimate the traveler's emotions and adjust the rating linking method based on the estimated traveler's emotions. For example, if the traveler is relaxed, the rating linking unit can provide detailed rating information. If the traveler is in a hurry, the rating linking unit can also provide concise rating information. If the traveler is excited, the rating linking unit can also provide visually appealing rating information. This makes it possible to provide an optimal rating linking method according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the rating linking unit can be performed using AI, for example, or without AI. For example, the rating linking unit can input traveler's emotion data into the generation AI and cause the generation AI to adjust the rating linking method based on the emotion.

[0078] When linking ratings, the rating linking unit can refer to the traveler's past rating history and provide an appropriate linking method. For example, the rating linking unit can provide related rating information based on restaurants and tourist attractions that the traveler has previously rated. The rating linking unit can also prioritize suggesting places that the traveler has previously given high ratings. The rating linking unit can also analyze the traveler's past rating history and provide the most relevant rating information. This makes it possible to provide an optimal rating linking method based on the past rating history. Some or all of the above-mentioned processing in the rating linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the rating linking unit can input the traveler's past rating history data into the generation AI and cause the generation AI to provide an optimal rating linking method.

[0079] The rating linking unit can customize the linking content based on the traveler's current interests and concerns when linking ratings. For example, the rating linking unit provides rating information based on themes (e.g., gourmet food, art, outdoors) in which the traveler is currently interested. The rating linking unit can also provide rating information based on activities (e.g., hiking, shopping, sightseeing) in which the traveler is currently interested. The rating linking unit can also provide rating information based on events (e.g., festivals, concerts, exhibitions) in which the traveler is currently interested. This makes it possible to provide an optimal rating linking method based on current interests and concerns. Some or all of the above-described processing in the rating linking unit may be performed using, or without, AI, for example. For example, the rating linking unit can input the traveler's current interest and concern data into the generation AI and have the generation AI customize the linking content.

[0080] The evaluation linking unit can estimate the traveler's emotions and determine the priority of evaluation linking based on the estimated traveler's emotions. For example, if the traveler is relaxed, the evaluation linking unit can prioritize providing detailed evaluation information. Furthermore, if the traveler is in a hurry, the evaluation linking unit can prioritize providing concise evaluation information. Furthermore, if the traveler is excited, the evaluation linking unit can prioritize providing visually appealing evaluation information. This makes it possible to provide a evaluation linking method with priorities according to the traveler's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation linking unit can be performed using, for example, AI, or without AI. For example, the evaluation linking unit can input traveler's emotion data into the generation AI and cause the generation AI to determine the priority of review linking based on emotions.

[0081] The evaluation linking unit can provide an appropriate linking method by taking into account the traveler's geographical location information when linking ratings. The evaluation linking unit, for example, provides evaluation information of restaurants and tourist attractions close to the traveler's current location. The evaluation linking unit can also provide evaluation information of local specialties and famous dishes in the traveler's current location. The evaluation linking unit can also provide evaluation information that is easily accessible from the traveler's current location. This makes it possible to provide an optimal evaluation linking method based on geographical location information. Some or all of the above-mentioned processing in the evaluation linking unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation linking unit can input the traveler's geographical location information into the generation AI and cause the generation AI to provide the optimal linking method.

[0082] The rating linking unit can analyze the traveler's social media activity during rating linking and provide related linking content. For example, the rating linking unit can provide related rating information based on photos and posts shared by the traveler on social media. The rating linking unit can also suggest places rated by influencers or friends followed by the traveler. The rating linking unit can also provide rating information for restaurants and tourist spots that the traveler has shown interest in on social media. This makes it possible to provide an optimal rating linking method based on social media activity. Some or all of the above-mentioned processing in the rating linking unit may be performed using, or without, AI, for example. For example, the rating linking unit can input the traveler's social media activity data into the generation AI and cause the generation AI to provide related linking content.

[0083] The payment integration unit can estimate the traveler's emotions and adjust the payment integration method based on the estimated traveler's emotions. For example, if the traveler is stressed, the payment integration unit can provide a simple and quick payment method. Furthermore, if the traveler is relaxed, the payment integration unit can also provide detailed payment options. Furthermore, if the traveler is in a hurry, the payment integration unit can prioritize the quickest payment method. This makes it possible to provide the optimal payment integration method according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the payment integration unit can be performed using, for example, AI, or without AI. For example, the payment integration unit can input the traveler's emotion data into the generation AI and cause the generation AI to adjust the payment integration method based on the emotion.

[0084] During payment integration, the payment integration unit can refer to the traveler's past payment history and provide an appropriate integration method. For example, the payment integration unit can provide optimal payment options based on payment methods used by the traveler in the past. The payment integration unit can also provide alternatives by taking into account payment methods that the traveler has avoided in the past. The payment integration unit can also analyze the traveler's past payment history and suggest the most efficient payment method. This makes it possible to provide an optimal payment integration method based on the past payment history. Some or all of the above-mentioned processing in the payment integration unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment integration unit can input the traveler's past payment history data into the generation AI and have the generation AI provide the optimal payment integration method.

[0085] During payment integration, the payment integration unit can customize the integration content based on the traveler's current payment status. The payment integration unit, for example, provides the optimal payment option based on the payment method currently used by the traveler. The payment integration unit can also provide the traveler with a payment method that suits their current payment status. The payment integration unit can also provide the traveler with the optimal payment method based on their current payment history. This makes it possible to provide the optimal payment integration method based on their current payment status. Some or all of the above-mentioned processing in the payment integration unit may be performed using AI, for example, or may be performed without using AI. For example, the payment integration unit can input the traveler's current payment status data into the generation AI and have the generation AI customize the integration content.

[0086] The payment linking unit can estimate the traveler's emotions and determine the priority of payment linkage based on the estimated traveler's emotions. For example, if the traveler is stressed, the payment linking unit can prioritize providing the simplest and fastest payment method. Furthermore, if the traveler is relaxed, the payment linking unit can prioritize providing detailed payment options. Furthermore, if the traveler is in a hurry, the payment linking unit can prioritize providing the fastest payment method. This allows payment linkage methods to be provided in order of priority according to the traveler's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the payment linking unit can be performed using, for example, AI, or without AI. For example, the payment linking unit can input traveler's emotion data into the generation AI and cause the generation AI to determine the priority of payment linkage based on the emotion.

[0087] The payment linking unit can provide an appropriate linking method during payment linking, taking into account the traveler's geographical location information. The payment linking unit, for example, provides the payment method closest to the traveler's current location. The payment linking unit can also provide payment information for local specialties and specialty dishes in the traveler's current location. The payment linking unit can also provide payment information that is easily accessible from the traveler's current location. This makes it possible to provide an optimal payment linking method based on geographical location information. Some or all of the above-mentioned processing in the payment linking unit may be performed using AI, for example, or may be performed without using AI. For example, the payment linking unit can input the traveler's geographical location information into the generation AI and cause the generation AI to provide the optimal linking method.

[0088] The payment integration unit can analyze the traveler's social media activity during payment integration and provide related integration content. For example, the payment integration unit can provide related payment information based on the payment methods and stores the traveler has shared on social media. The payment integration unit can also suggest payment methods used by influencers or friends the traveler follows. The payment integration unit can also provide information on payment methods and stores that the traveler has shown interest in on social media. This makes it possible to provide the optimal payment integration method based on social media activity. Some or all of the above-mentioned processing in the payment integration unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment integration unit can input the traveler's social media activity data into the generation AI and have the generation AI provide related integration content. === Hard Collateral 1-1 === Each of the above-described elements, including the suggestion unit, traffic information providing unit, emergency response guidance unit, rating linking unit, and payment linking unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the suggestion unit is implemented by the control unit 46A of the smart device 14 and learns the traveler's preferences and past behavioral data to propose an optimal travel plan. The traffic information providing unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides real-time traffic information. The emergency response guidance unit is implemented, for example, by the control unit 46A of the smart device 14 and provides instructions on how to respond in an emergency. The rating linking unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and links with a restaurant rating app to find popular local restaurants. The payment linking unit is implemented, for example, by the control unit 46A of the smart device 14 and links with an electronic payment app to make smooth payments. === Hard Collateral 1-2 === Each of the multiple elements, including the suggestion unit, traffic information providing unit, emergency response guidance unit, rating linking unit, and payment linking unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the smart glasses 214 and learns the traveler's preferences and past behavior data to suggest an optimal travel plan. The traffic information providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides real-time traffic information. The emergency response guidance unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides instructions on how to respond in an emergency. The rating linking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and links with a restaurant rating app to find popular local restaurants. The payment linking unit is realized, for example, by the control unit 46A of the smart glasses 214 and links with an electronic payment app to make smooth payments. === Hard Collateral 1-3 === Each of the above-described elements, including the suggestion unit, traffic information provision unit, emergency response guidance unit, rating linkage unit, and payment linkage unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the suggestion unit is implemented by the control unit 46A of the headset terminal 314 and learns the traveler's preferences and past behavior data to propose an optimal travel plan. The traffic information provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides real-time traffic information. The emergency response guidance unit is implemented, for example, by the control unit 46A of the headset terminal 314 and provides instructions on how to respond in an emergency. The rating linkage unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and links with a restaurant rating app to find popular local restaurants. The payment linkage unit is implemented, for example, by the control unit 46A of the headset terminal 314 and links with an electronic payment app to make smooth payments. === Hard Collateral 1-4 === Each of the multiple elements, including the suggestion unit, traffic information provision unit, emergency response guidance unit, rating linkage unit, and payment linkage unit, described above, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the suggestion unit is implemented by the control unit 46A of the robot 414 and learns the traveler's preferences and past behavioral data to propose an optimal travel plan. The traffic information provision unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides real-time traffic information. The emergency response guidance unit is implemented, for example, by the control unit 46A of the robot 414 and provides instructions on how to respond in an emergency. The rating linkage unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and links with a restaurant rating app to find popular local restaurants. The payment linkage unit is implemented, for example, by the control unit 46A of the robot 414 and links with an electronic payment app to make smooth payments.

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

[0090] The suggestion unit can monitor the traveler's health condition and adjust the travel plan based on the health condition. For example, if the traveler feels tired, it can suggest relaxing tourist spots and activities. If the traveler is active, it can suggest activities such as hiking or sporting events. Furthermore, if the traveler has a specific health problem, it can suggest tourist spots and facilities that address that problem. This makes it possible to provide the traveler with an optimal travel plan based on their health condition.

[0091] The suggestion unit can propose travel plans taking into account the cultural background of the traveler. For example, if the traveler is interested in a particular culture or religion, it can suggest tourist spots and events related to that culture or religion. Also, if the traveler is interested in a particular food culture, it can suggest restaurants and dishes related to that food culture. Furthermore, if the traveler has a particular historical background, it can suggest tourist spots and museums related to that background. This makes it possible to provide the optimal travel plan according to the traveler's cultural background.

[0092] The transportation information providing unit can suggest transportation methods taking into account the eco-consciousness of the traveler. For example, if the traveler desires an environmentally friendly means of transportation, it can suggest the use of public transportation or bicycles. Also, if the traveler wants to reduce their carbon footprint, it can suggest eco-friendly transportation methods with priority. Furthermore, if the traveler is interested in eco-tourism, it can suggest environmentally friendly tourist spots and activities. In this way, it is possible to provide optimal transportation information according to the traveler's eco-consciousness.

[0093] The emergency response information section can provide emergency response instructions taking into account the traveler's language ability. For example, if the traveler does not speak English, emergency response instructions can be provided in the traveler's native language. Also, if the traveler speaks multiple languages, emergency response instructions can be provided in the language that the traveler can understand most easily. Furthermore, if the traveler uses sign language, emergency response instructions can be provided in sign language. This makes it possible to provide the most appropriate emergency response instructions according to the traveler's language ability.

[0094] The evaluation linking unit can provide evaluation information taking into consideration the traveler's dietary restrictions. For example, if the traveler is vegetarian, evaluation information on vegetarian-friendly restaurants can be provided. Also, if the traveler has allergies, evaluation information on restaurants that cater to allergies can be provided. Furthermore, if the traveler is interested in a particular food culture, evaluation information on restaurants related to that food culture can be provided. This makes it possible to provide optimal evaluation information according to the traveler's dietary restrictions.

[0095] The suggestion unit can estimate the traveler's emotions and adjust the proposed travel plan based on the estimated emotions. For example, if the traveler is feeling stressed, it can suggest relaxing tourist spots and activities. If the traveler is excited, it can suggest adventurous and exciting activities. Furthermore, if the traveler is tired, it can suggest places to rest and relaxation facilities. This makes it possible to provide the optimal travel plan according to the traveler's emotions.

[0096] The traffic information providing unit can estimate the traveler's emotions and adjust the way traffic information is provided based on the estimated emotions. For example, if the traveler is feeling stressed, simple and easy-to-understand traffic information can be provided. If the traveler is relaxed, detailed traffic information can be provided. Furthermore, if the traveler is in a hurry, the shortest route or the fastest means of transportation can be provided with priority. This makes it possible to provide optimal traffic information according to the traveler's emotions.

[0097] The emergency response guidance unit can estimate the traveler's emotions and adjust emergency response methods based on the estimated emotions. For example, if the traveler is in a panic, the emergency response methods can be explained in a calm voice. If the traveler is calm, detailed emergency response methods can be provided. Furthermore, if the traveler is feeling anxious, emergency response methods that give a sense of security can be provided. In this way, the optimal emergency response method can be provided according to the traveler's emotions.

[0098] The rating linking unit can estimate the traveler's emotions and adjust the rating linking method based on the estimated emotions. For example, if the traveler is relaxed, detailed rating information can be provided. If the traveler is in a hurry, concise rating information can be provided. Furthermore, if the traveler is excited, visually appealing rating information can be provided. This makes it possible to provide an optimal rating linking method according to the traveler's emotions.

[0099] The payment integration unit can estimate the traveler's emotions and adjust the payment integration method based on the estimated emotions. For example, if the traveler is stressed, a simple and quick payment method can be provided. If the traveler is relaxed, detailed payment options can be provided. Furthermore, if the traveler is in a hurry, the quickest payment method can be given priority. This makes it possible to provide the optimal payment integration method according to the traveler's emotions.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The suggestion unit learns the traveler's preferences and past behavioral data to propose the optimal travel plan. The suggestion unit suggests the best tourist spots and activities based on the traveler's past travel history and preferences. It can also filter based on the traveler's current interests and propose highly relevant plans. It can also estimate the traveler's emotions and adjust the proposed travel plan based on the estimated emotions. Step 2: The traffic information provider provides real-time traffic information. The traffic information provider can provide the closest transportation option to the traveler's current location. It can also provide optimal information by referring to the traveler's past travel history. Step 3: The emergency response guidance unit provides emergency response instructions. The emergency response guidance unit can provide the nearest emergency contact point to the traveler's current location. It can also estimate the traveler's emotions and adjust emergency response instructions based on the estimated emotions. Step 4: The rating linking unit links with restaurant rating apps to find popular local restaurants. The rating linking unit can provide rating information for restaurants close to the traveler's current location. It can also refer to the traveler's past rating history to provide the optimal linking method. Step 5: The payment integration unit integrates with the electronic payment app to facilitate payment. The payment integration unit can provide optimal payment options based on the payment method currently used by the traveler. It can also estimate the traveler's emotions and adjust the payment integration method based on the estimated emotions.

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

[0103] 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.

[0104] 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.

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

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

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

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

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

[0119] 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.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

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

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

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

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

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

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

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

[0140] The data processing device 12 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

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

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0149] 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.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

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

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] [Explanation of symbols]

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

Claims

1. A proposal section that learns traveler preferences and past behavior data to propose appropriate travel plans; a traffic information providing unit that provides real-time traffic information; an emergency response information section that provides information on how to respond in an emergency; The evaluation collaboration department works with restaurant rating apps to find popular local restaurants, A payment linking unit that links with an electronic payment app to make smooth payments. A system characterized by:

2. The proposal unit Estimate traveler sentiment and tailor itinerary suggestions based on the estimated traveler sentiment 2. The system of claim 1.

3. The proposal unit Analyze the traveler's past travel history and select the appropriate suggestion method 2. The system of claim 1.

4. The proposal unit Filter suggestions based on the traveler's current interests 2. The system of claim 1.

5. The proposal unit Estimate traveler sentiment and prioritize travel plans based on the estimated traveler sentiment 2. The system of claim 1.

6. The proposal unit When making suggestions, the app takes into account the traveler's geographic location to prioritize relevant itineraries.

2. The system of claim 1.

7. The proposal unit When making a recommendation, analyze the traveler's social media activity and suggest relevant plans 2. The system of claim 1.

8. The aforementioned traffic information provision department, Estimate traveler sentiment and adjust how traffic information is provided based on the estimated traveler sentiment 2. The system of claim 1.

9. The aforementioned traffic information provision department, When providing traffic information, provide appropriate information by referring to the traveler's past travel history.

2. The system of claim 1.

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