System

Abstract

A system according to an embodiment aims to provide information and support for inbound travelers to enjoy Nippon Travel Agency to the maximum extent possible.SOLUTION: A system includes a reception part, a proposal part, a movement part, an information provision part, and a cooperation part. The reception unit receives input information of a traveler. The proposing section analyzes the information received by the receiving section and proposes an appropriate travel plan. The movement unit supports movement based on the plan proposed by the proposal unit. The information providing unit provides information corresponding to the local rule and the problem. The cooperation unit cooperates with the domestic application.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not provide enough information or support to help inbound tourists enjoy their trip to Japan to the fullest, and there is room for improvement.

[0005] The system according to the embodiment aims to provide information and support to inbound tourists to help them enjoy their trip to Japan to the fullest. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a proposal unit, a movement unit, an information provision unit, and a linking unit. The reception unit accepts information input by the traveler. The proposal unit analyzes the information accepted by the reception unit and proposes an appropriate travel plan. The movement unit supports movement based on the plan proposed by the proposal unit. The information provision unit provides information corresponding to local rules and issues. The linking unit links with domestic apps. [Effects of the Invention]

[0007] The system according to the embodiment can provide information and support to inbound tourists to help them enjoy their trip to Japan to the fullest. [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 system according to an embodiment of the present invention provides a smartphone app and device set equipped with a Japan Guide for inbound travelers. This system accepts travelers' input information, proposes optimal travel plans, supports their travel, provides information on local rules and troubleshooting tips, and links with domestic apps. For example, when travelers input their desired places to visit and activities of interest, the system analyzes the information and proposes optimal travel plans. The system also provides public transportation information and route guidance to help travelers navigate smoothly. Furthermore, the system provides information on potential issues travelers may encounter in Japan, such as how to separate garbage and what to do in an emergency. The system also links with domestic apps, allowing travelers to maximize their enjoyment of Japan using restaurant review apps and electronic payment apps. This allows the system to provide comprehensive support for travelers to maximize their enjoyment of Japan. For example, by inputting the places travelers want to visit and activities they are interested in, the system proposes optimal travel plans and provides public transportation information and route guidance. Furthermore, by providing information on local rules and troubleshooting tips and linking with domestic apps, travelers can enjoy their stay in Japan with peace of mind.

[0029] A travel support system according to an embodiment includes a reception unit, a proposal unit, a transportation unit, an information provision unit, and a linking unit. The reception unit accepts input information from a traveler. The input information includes, but is not limited to, text, audio, and images. The reception unit accepts information, for example, by inputting places the traveler wants to visit and activities they are interested in. The reception unit can also accept voice input using speech recognition technology. The proposal unit analyzes the information accepted by the reception unit and proposes an optimal travel plan. For example, techniques such as natural language processing and data mining are used for the analysis. For example, if a traveler inputs "I want to go sightseeing in Tokyo," the proposal unit proposes a plan based on information about tourist spots and events in Tokyo. The transportation unit supports transportation based on the plan proposed by the proposal unit. For example, the transportation unit provides information about public transportation and route guidance. For example, if a traveler inputs "I want to go from Shinjuku to Shibuya," the transportation unit proposes an optimal route and displays transfer information and travel time. The information provision unit provides information about local rules and solutions to problems. The information providing unit provides information on problems that travelers may face in Japan, such as how to separate garbage and what to do in an emergency. The linking unit links with domestic apps. For example, the linking unit links with a restaurant rating app or an electronic payment app, allowing travelers to use these apps. As a result, the travel support system according to the embodiment can provide comprehensive support to travelers to enjoy Japan to the fullest.

[0030] The suggestion unit can suggest appropriate travel plans by inputting places that the traveler wants to visit and activities that interest them. For example, if the traveler inputs "I want to visit temples in Kyoto," the suggestion unit can suggest plans based on Kyoto's temples and related tourist spots. Also, if the traveler inputs "I want to enjoy skiing," the suggestion unit can suggest plans based on ski resorts and related activities. Furthermore, if the traveler inputs "I want to enjoy shopping," the suggestion unit can suggest plans based on shopping malls and related stores. In this way, the suggestion unit can provide optimal travel plans based on the traveler's interests.

[0031] The transportation unit can provide information on public transportation and route guidance. For example, if a traveler inputs "I want to go from Osaka to Kyoto," the transportation unit can suggest the optimal route and display transfer information and travel time. Furthermore, if a traveler inputs "I want to take the Shinkansen," the transportation unit can provide Shinkansen timetables and fare information. Furthermore, if a traveler inputs "I want to take the bus," the transportation unit can provide bus route and stop information. In this way, the transportation unit can support the traveler to travel smoothly.

[0032] The information providing unit can provide information on how to separate garbage, what to do in an emergency, and problems that travelers may face in Japan. For example, if a traveler inputs, "I want to know how to separate garbage," the information providing unit can provide information on how to separate burnable garbage, non-burnable garbage, recyclable items, etc. If a traveler inputs, "I want to know how to respond in an emergency," the information providing unit can also provide information on evacuation sites, emergency contact information, first aid, etc. Furthermore, if a traveler inputs, "I want to know local rules in Japan," the information providing unit can also provide information on etiquette in public places, traffic rules, etc. In this way, the information providing unit can support travelers so that they can enjoy their stay in Japan with peace of mind.

[0033] The linking unit can link with a restaurant rating app or an electronic payment app to enable travelers to use these apps. For example, if a traveler inputs "I want to find restaurants with high ratings," the linking unit links with the restaurant rating app to suggest restaurants with high ratings. Also, if a traveler inputs "I want to use electronic payment," the linking unit can link with the electronic payment app to make payments. Furthermore, if a traveler inputs "I want to know information about tourist spots," the linking unit can link with a tourist information providing app to provide information about tourist spots. In this way, the linking unit can support travelers in using domestic apps to enjoy Japan to the fullest.

[0034] The reception unit can analyze the user's past input history and select an appropriate reception method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also provide predictive input based on information previously input by the user, simplifying the input work. Furthermore, the reception unit can predict and suggest an input method to be used during a specific time period from the user's past input history. This allows the reception unit to provide the optimal reception method based on the user's past input history. The past input history includes, for example, the input date and time, input content, and input format. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0035] When receiving input information, the reception unit can filter the input information based on the user's current travel situation and areas of interest. For example, the reception unit may preferentially receive information about the area where the user is currently staying. The reception unit may also preferentially receive information related to activities in which the user is interested. Furthermore, the reception unit can filter and receive information related to the user's travel plans. This enables the reception unit to receive information based on the user's current situation and interests. The current travel situation includes, for example, a place of stay, a means of transportation, a travel itinerary, etc. The areas of interest include, for example, hobbies, activities of interest, places to visit, etc. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI.

[0036] When receiving input information, the reception unit can select an appropriate reception means depending on the user's input method. For example, if the user selects voice input, the reception unit can receive information using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also receive information using text analysis technology. Furthermore, if the user selects image input, the reception unit can also receive information using image recognition technology. This enables the reception unit to receive information optimally depending on the user's input method. Input methods include, for example, voice input, text input, image input, etc. Appropriate reception means include, for example, a voice recognition system, a text analysis system, an image recognition system, etc. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0037] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the travel plan. For example, the suggestion unit provides detailed information for an important travel plan. The suggestion unit can also provide concise information for a short travel plan. Furthermore, the suggestion unit can also provide a detailed schedule for a long travel plan. This enables the suggestion unit to make a proposal with an appropriate level of detail depending on the importance of the travel plan. Examples of the importance of a travel plan include the user's priorities, time constraints, budget, etc. Examples of the level of detail of the proposal include an overview level, a detailed level, a step-by-step explanation, etc. 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.

[0038] When making a proposal, the suggestion unit can apply different suggestion algorithms depending on the category of the travel plan. For example, in the case of a sightseeing plan, the suggestion unit may make suggestions focusing on tourist spots. In addition, in the case of a dining plan, the suggestion unit may make suggestions focusing on restaurants. In addition, in the case of a shopping plan, the suggestion unit may make suggestions focusing on shopping areas. This enables the suggestion unit to make appropriate suggestions depending on the category of the travel plan. Categories of travel plans include, for example, sightseeing, dining, shopping, and activities. The suggestion algorithm includes, for example, a recommendation system, a machine learning algorithm, and the like. 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.

[0039] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit makes an optimal suggestion based on, for example, suggestions that the user has accepted in the past. The suggestion unit can also analyze the user's preference trends from the user's past suggestion results and reflect them in the suggestion. Furthermore, the suggestion unit can analyze the user's past suggestion results and improve the suggestion algorithm. This enables the suggestion unit to make highly accurate suggestions based on the user's past suggestion results. The past suggestion results include, for example, the date and time of the suggestion, the content of the suggestion, and user feedback. To improve the accuracy of the suggestion, for example, the feedback is reflected, the algorithm is improved, and so on. 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.

[0040] During transportation support, the transportation unit can propose an appropriate route taking into account the congestion status of public transportation. For example, the transportation unit can propose a route that avoids busy times. The transportation unit can also propose a route that avoids crowded public transportation. Furthermore, the transportation unit can propose the optimal means of transportation depending on the congestion status. This enables the transportation unit to propose an optimal route depending on the congestion status of public transportation. The congestion status of public transportation includes, for example, real-time data, past data, and predicted data. An appropriate route includes, for example, travel time, number of transfers, and congestion level. Some or all of the above-mentioned processing in the transportation unit may be performed using, for example, AI, or may be performed without using AI.

[0041] When providing transportation support, the transportation unit can select the optimal transportation means by referring to the user's past transportation history. The transportation unit, for example, suggests the optimal transportation means based on transportation means used by the user in the past. The transportation unit can also suggest transportation means that avoid congestion based on the user's past transportation history. Furthermore, the transportation unit can analyze the user's past transportation history and suggest the most efficient transportation means. This enables the transportation unit to select the optimal transportation means based on the user's past transportation history. The past transportation history includes, for example, travel date and time, transportation means, and travel route. The optimal transportation means includes, for example, travel time, cost, and comfort. Some or all of the above-mentioned processing in the transportation unit may be performed, for example, using AI, or may be performed without using AI.

[0042] During transportation support, the transportation unit can provide route guidance in real time based on the user's current location information. For example, the transportation unit updates the user's current location in real time while the user is traveling and provides route guidance. The transportation unit can also update the user's current location in real time as the user approaches the destination and suggest an optimal route. Furthermore, if the user becomes lost, the transportation unit can update the user's current location in real time and provide route guidance again. This enables the transportation unit to provide real-time route guidance based on the user's current location information. Current location information includes, for example, GPS data, Wi-Fi location information, beacons, etc. To provide route guidance in real time, for example, map display, voice guidance, real-time updates, etc. are performed. Some or all of the above-described processing in the transportation unit may be performed using, for example, AI, or may be performed without using AI.

[0043] When providing information, the information providing unit can select appropriate information based on the user's current situation. For example, the information providing unit can prioritize providing information about the area where the user is currently staying. The information providing unit can also prioritize providing information related to a problem the user is currently facing. Furthermore, the information providing unit can select optimal information based on the user's current situation. This enables the information providing unit to provide optimal information based on the user's current situation. The current situation includes, for example, a place of stay, a means of transportation, a travel itinerary, etc. Appropriate information includes, for example, the user's interests, current situation, past history, etc. Some or all of the above-mentioned processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI.

[0044] When providing information, the information providing unit can improve the accuracy of the information by referring to the user's past information provision history. The information providing unit, for example, provides optimal information based on information the user has received in the past. The information providing unit can also analyze the user's past information provision history to reflect the user's preference trends in the information. Furthermore, the information providing unit can analyze the user's past information provision history to improve the information provision algorithm. This enables the information providing unit to provide highly accurate information based on the user's past information provision history. The past information provision history includes, for example, the date and time of provision, the content of provision, and user feedback. To improve the accuracy of the information, for example, feedback is reflected, the algorithm is improved, and so on. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI.

[0045] The information providing unit can improve the information provision method by reflecting user feedback when providing information. The information providing unit can, for example, suggest an optimal information provision method based on feedback previously provided by the user. The information providing unit can also preferentially suggest a specific information provision method based on the user's past feedback. Furthermore, the information providing unit can analyze the user's past feedback and improve the information provision method. This enables the information providing unit to improve the optimal information provision method based on the user's feedback. Feedback includes, for example, user ratings, comments, survey results, etc. To improve the information provision method, for example, feedback can be reflected, algorithms can be improved, etc. Some or all of the above-mentioned processing in the information providing unit can be performed using AI, for example, or without AI.

[0046] During collaboration, the collaboration unit can select an appropriate app based on the user's current travel situation. For example, if the user is sightseeing, the collaboration unit can preferentially suggest a tourist information app. Furthermore, if the user is traveling, the collaboration unit can also preferentially suggest a transportation information app. Furthermore, if the user is eating, the collaboration unit can also preferentially suggest a restaurant rating app. This enables the collaboration unit to select an optimal app based on the user's current travel situation. The current travel situation includes, for example, a place of stay, a means of transportation, a travel itinerary, etc. An appropriate app includes, for example, the user's interests, current situation, past history, etc. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI.

[0047] During collaboration, the collaboration unit can improve the accuracy of collaboration by referring to the user's past collaboration history. The collaboration unit, for example, suggests optimal apps based on apps the user has used in the past. The collaboration unit can also analyze the user's preferences from the user's past collaboration history and reflect the results in the apps. The collaboration unit can also analyze the user's past collaboration history and improve the collaboration algorithm. This enables the collaboration unit to perform collaboration with high accuracy based on the user's past collaboration history. The past collaboration history includes, for example, the date and time of collaboration, the content of collaboration, and user feedback. To improve the accuracy of collaboration, for example, feedback is reflected, the algorithm is improved, and so on. Some or all of the above-described processing in the collaboration unit may be performed using AI, or may be performed without using AI.

[0048] The collaboration unit can improve the collaboration method by reflecting user feedback during collaboration. The collaboration unit can, for example, propose an optimal collaboration method based on feedback provided by the user in the past. The collaboration unit can also preferentially propose a specific collaboration method based on the user's past feedback. The collaboration unit can also analyze the user's past feedback and improve the collaboration method. This enables the collaboration unit to improve the optimal collaboration method based on user feedback. Feedback includes, for example, user ratings, comments, survey results, etc. To improve the collaboration method, for example, the feedback can be reflected, the algorithm can be improved, etc. Some or all of the above-mentioned processing in the collaboration unit may be performed using AI, for example, or without AI.

[0049] During linking, the linking unit can select an appropriate app by taking into account the user's geographical location information. For example, the linking unit may preferentially suggest a tourist information app for the area where the user is currently staying. The linking unit may also preferentially suggest a transportation information app for the area where the user is currently staying. The linking unit may also preferentially suggest a restaurant rating app for the area where the user is currently staying. This enables the linking unit to select an optimal app based on the user's geographical location information. Geographical location information includes, for example, GPS data, Wi-Fi location information, beacons, etc. Appropriate apps include, for example, the user's interests, current situation, past history, etc. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI.

[0050] During linking, the linking unit can analyze the user's social media activity and suggest related apps. For example, the linking unit can suggest apps related to places the user has checked in to on social media. The linking unit can also analyze the content of the user's social media posts and suggest related apps. Furthermore, the linking unit can suggest related apps based on the activity of the user's friends on social media. This enables the linking unit to suggest optimal apps based on the user's social media activity. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. Related apps include, for example, the user's interests, current status, past history, etc. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI.

[0051] The collaboration unit can customize the collaboration method by reflecting the user's past feedback during collaboration. The collaboration unit, for example, suggests an optimal collaboration method based on feedback provided by the user in the past. The collaboration unit can also preferentially suggest a specific collaboration method based on the user's past feedback. The collaboration unit can also analyze the user's past feedback and improve the collaboration method. This enables the collaboration unit to customize the optimal collaboration method based on the user's past feedback. Past feedback includes, for example, user ratings, comments, survey results, etc. To customize the collaboration method, for example, feedback is reflected, algorithms are improved, etc. Some or all of the above-mentioned processing in the collaboration unit may be performed using AI, for example, or without AI.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The suggestion unit can analyze the user's past travel history and suggest similar travel plans. For example, it can suggest the user's next travel plan based on places the user has visited and activities the user has participated in in the past. It can also make new suggestions based on tourist spots and restaurants the user has previously liked. It can also suggest plans tailored to specific seasons or events based on the user's past travel history. This allows the suggestion unit to provide the user with the optimal travel plan based on the user's past travel history.

[0054] The mobility unit can monitor traffic conditions in real time while the user is traveling and suggest optimal routes. For example, it can suggest detour routes based on traffic congestion and accident information. It can also suggest alternative means of transportation based on information about delays in public transportation. Furthermore, if the user is in a hurry, it can prioritize suggesting routes that will reach the destination in the shortest time. This enables the mobility unit to provide optimal mobility support according to real-time traffic conditions.

[0055] The information providing unit can provide appropriate information based on the user's current weather information. For example, if it is raining, it can suggest indoor activities that can be enjoyed. On a hot day, it can also suggest cool places or places where you can enjoy cold drinks. On a cold day, it can also suggest warm places or places where you can enjoy hot drinks. This allows the information providing unit to provide optimal information according to the user's current weather conditions.

[0056] The linking unit can link with the user's social media account and make suggestions based on the travel history of friends. For example, suggestions can be made to the user based on tourist spots visited by friends and activities participated in. Suggestions can also be made to the user based on restaurants and accommodations that friends have given high ratings to. Furthermore, new travel plans can be suggested to the user based on travel photos and comments shared by friends. This enables the linking unit to make optimal suggestions based on the user's social media activity.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception unit receives input information from the traveler. The input information can include text, audio, images, etc. For example, the information is received by inputting places the traveler wants to visit or activities they are interested in. Voice input can also be received using voice recognition technology. Step 2: The proposal unit analyzes the information received by the reception unit and proposes the optimal travel plan. The analysis uses techniques such as natural language processing and data mining. For example, if a traveler inputs "I want to go sightseeing in Tokyo," the system will propose a plan based on information about Tokyo's tourist spots and events. Step 3: The travel unit supports travel based on the plan proposed by the proposal unit. For example, it provides information on public transportation and route guidance. If a traveler inputs "I want to go from Shinjuku to Shibuya," it suggests the optimal route and displays transfer information and travel time. Step 4: The Information Department provides information on local rules and issues that travelers may encounter in Japan, such as how to separate garbage and what to do in an emergency. Step 5: The collaboration department will collaborate with domestic apps, such as restaurant rating apps and electronic payment apps, to enable travelers to use these apps.

[0059] (Example 2) A system according to an embodiment of the present invention provides a smartphone app and device set equipped with a Japan Guide for inbound travelers. This system accepts travelers' input information, proposes optimal travel plans, supports their travel, provides information on local rules and troubleshooting tips, and links with domestic apps. For example, when travelers input their desired places to visit and activities of interest, the system analyzes the information and proposes optimal travel plans. The system also provides public transportation information and route guidance to help travelers navigate smoothly. Furthermore, the system provides information on potential issues travelers may encounter in Japan, such as how to separate garbage and what to do in an emergency. The system also links with domestic apps, allowing travelers to maximize their enjoyment of Japan using restaurant review apps and electronic payment apps. This allows the system to provide comprehensive support for travelers to maximize their enjoyment of Japan. For example, by inputting the places travelers want to visit and activities they are interested in, the system proposes optimal travel plans and provides public transportation information and route guidance. Furthermore, by providing information on local rules and troubleshooting tips and linking with domestic apps, travelers can enjoy their stay in Japan with peace of mind.

[0060] A travel support system according to an embodiment includes a reception unit, a proposal unit, a transportation unit, an information provision unit, and a linking unit. The reception unit accepts input information from a traveler. The input information includes, but is not limited to, text, audio, and images. The reception unit accepts information, for example, by inputting places the traveler wants to visit and activities they are interested in. The reception unit can also accept voice input using speech recognition technology. The proposal unit analyzes the information accepted by the reception unit and proposes an optimal travel plan. For example, techniques such as natural language processing and data mining are used for the analysis. For example, if a traveler inputs "I want to go sightseeing in Tokyo," the proposal unit proposes a plan based on information about tourist spots and events in Tokyo. The transportation unit supports transportation based on the plan proposed by the proposal unit. For example, the transportation unit provides information about public transportation and route guidance. For example, if a traveler inputs "I want to go from Shinjuku to Shibuya," the transportation unit proposes an optimal route and displays transfer information and travel time. The information provision unit provides information about local rules and solutions to problems. The information providing unit provides information on problems that travelers may face in Japan, such as how to separate garbage and what to do in an emergency. The linking unit links with domestic apps. For example, the linking unit links with a restaurant rating app or an electronic payment app, allowing travelers to use these apps. As a result, the travel support system according to the embodiment can provide comprehensive support to travelers to enjoy Japan to the fullest.

[0061] The suggestion unit can suggest appropriate travel plans by inputting places that the traveler wants to visit and activities that interest them. For example, if the traveler inputs "I want to visit temples in Kyoto," the suggestion unit can suggest plans based on Kyoto's temples and related tourist spots. Also, if the traveler inputs "I want to enjoy skiing," the suggestion unit can suggest plans based on ski resorts and related activities. Furthermore, if the traveler inputs "I want to enjoy shopping," the suggestion unit can suggest plans based on shopping malls and related stores. In this way, the suggestion unit can provide optimal travel plans based on the traveler's interests.

[0062] The transportation unit can provide information on public transportation and route guidance. For example, if a traveler inputs "I want to go from Osaka to Kyoto," the transportation unit can suggest the optimal route and display transfer information and travel time. Furthermore, if a traveler inputs "I want to take the Shinkansen," the transportation unit can provide Shinkansen timetables and fare information. Furthermore, if a traveler inputs "I want to take the bus," the transportation unit can provide bus route and stop information. In this way, the transportation unit can support the traveler to travel smoothly.

[0063] The information providing unit can provide information on how to separate garbage, what to do in an emergency, and problems that travelers may face in Japan. For example, if a traveler inputs, "I want to know how to separate garbage," the information providing unit can provide information on how to separate burnable garbage, non-burnable garbage, recyclable items, etc. If a traveler inputs, "I want to know how to respond in an emergency," the information providing unit can also provide information on evacuation sites, emergency contact information, first aid, etc. Furthermore, if a traveler inputs, "I want to know local rules in Japan," the information providing unit can also provide information on etiquette in public places, traffic rules, etc. In this way, the information providing unit can support travelers so that they can enjoy their stay in Japan with peace of mind.

[0064] The linking unit can link with a restaurant rating app or an electronic payment app to enable travelers to use these apps. For example, if a traveler inputs "I want to find restaurants with high ratings," the linking unit links with the restaurant rating app to suggest restaurants with high ratings. Also, if a traveler inputs "I want to use electronic payment," the linking unit can link with the electronic payment app to make payments. Furthermore, if a traveler inputs "I want to know information about tourist spots," the linking unit can link with a tourist information providing app to provide information about tourist spots. In this way, the linking unit can support travelers in using domestic apps to enjoy Japan to the fullest.

[0065] The reception unit can estimate the user's emotions and adjust the timing of receiving input information based on the estimated user emotions. For example, if the user is excited, the reception unit can quickly receive input information and provide immediate feedback. Furthermore, if the user is tired, the reception unit can slowly receive input information to allow the user to enter information in a relaxed state. Furthermore, if the user is feeling anxious, the reception unit can gradually receive input information to provide a sense of security. This enables the reception unit to receive information at an appropriate timing according to the user's emotions. The emotion estimation is performed using techniques such as facial expression recognition, voice analysis, and text analysis. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0066] The reception unit can analyze the user's past input history and select an appropriate reception method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also provide predictive input based on information previously input by the user, simplifying the input work. Furthermore, the reception unit can predict and suggest an input method to be used during a specific time period from the user's past input history. This allows the reception unit to provide the optimal reception method based on the user's past input history. The past input history includes, for example, the input date and time, input content, and input format. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0067] When receiving input information, the reception unit can filter the input information based on the user's current travel situation and areas of interest. For example, the reception unit may preferentially receive information about the area where the user is currently staying. The reception unit may also preferentially receive information related to activities in which the user is interested. Furthermore, the reception unit can filter and receive information related to the user's travel plans. This enables the reception unit to receive information based on the user's current situation and interests. The current travel situation includes, for example, a place of stay, a means of transportation, a travel itinerary, etc. The areas of interest include, for example, hobbies, activities of interest, places to visit, etc. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI.

[0068] When receiving input information, the reception unit can select an appropriate reception means depending on the user's input method. For example, if the user selects voice input, the reception unit can receive information using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also receive information using text analysis technology. Furthermore, if the user selects image input, the reception unit can also receive information using image recognition technology. This enables the reception unit to receive information optimally depending on the user's input method. Input methods include, for example, voice input, text input, image input, etc. Appropriate reception means include, for example, a voice recognition system, a text analysis system, an image recognition system, etc. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0069] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user's emotions. For example, if the user is excited, the suggestion unit can make a visually stimulating suggestion. Furthermore, if the user is relaxed, the suggestion unit can make a suggestion using calming expressions. Furthermore, if the user is feeling anxious, the suggestion unit can make a suggestion using expressions that give a sense of security. This enables the suggestion unit to make a suggestion using an appropriate expression method according to the user's emotions. The estimation of emotions is performed using technologies such as facial expression recognition, voice analysis, and text analysis. 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.

[0070] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the travel plan. For example, the suggestion unit provides detailed information for an important travel plan. The suggestion unit can also provide concise information for a short travel plan. Furthermore, the suggestion unit can also provide a detailed schedule for a long travel plan. This enables the suggestion unit to make a proposal with an appropriate level of detail depending on the importance of the travel plan. Examples of the importance of a travel plan include the user's priorities, time constraints, budget, etc. Examples of the level of detail of the proposal include an overview level, a detailed level, a step-by-step explanation, etc. 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.

[0071] When making a proposal, the suggestion unit can apply different suggestion algorithms depending on the category of the travel plan. For example, in the case of a sightseeing plan, the suggestion unit may make suggestions focusing on tourist spots. In addition, in the case of a dining plan, the suggestion unit may make suggestions focusing on restaurants. In addition, in the case of a shopping plan, the suggestion unit may make suggestions focusing on shopping areas. This enables the suggestion unit to make appropriate suggestions depending on the category of the travel plan. Categories of travel plans include, for example, sightseeing, dining, shopping, and activities. The suggestion algorithm includes, for example, a recommendation system, a machine learning algorithm, and the like. 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.

[0072] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit makes an optimal suggestion based on, for example, suggestions that the user has accepted in the past. The suggestion unit can also analyze the user's preference trends from the user's past suggestion results and reflect them in the suggestion. Furthermore, the suggestion unit can analyze the user's past suggestion results and improve the suggestion algorithm. This enables the suggestion unit to make highly accurate suggestions based on the user's past suggestion results. The past suggestion results include, for example, the date and time of the suggestion, the content of the suggestion, and user feedback. To improve the accuracy of the suggestion, for example, the feedback is reflected, the algorithm is improved, and so on. 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.

[0073] The transportation unit can estimate the user's emotions and adjust the transportation support method based on the estimated user's emotions. For example, if the user is nervous, the transportation unit can provide simple, highly visible route guidance. Furthermore, if the user is relaxed, the transportation unit can also provide detailed route guidance. Furthermore, if the user is in a hurry, the transportation unit can preferentially suggest the shortest route. This enables the transportation unit to provide appropriate transportation support according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. Some or all of the above-mentioned processing in the transportation unit may be performed using, for example, AI, or may be performed without using AI.

[0074] During transportation support, the transportation unit can propose an appropriate route taking into account the congestion status of public transportation. For example, the transportation unit can propose a route that avoids busy times. The transportation unit can also propose a route that avoids crowded public transportation. Furthermore, the transportation unit can propose the optimal means of transportation depending on the congestion status. This enables the transportation unit to propose an optimal route depending on the congestion status of public transportation. The congestion status of public transportation includes, for example, real-time data, past data, and predicted data. An appropriate route includes, for example, travel time, number of transfers, and congestion level. Some or all of the above-mentioned processing in the transportation unit may be performed using, for example, AI, or may be performed without using AI.

[0075] When providing transportation support, the transportation unit can select the optimal transportation means by referring to the user's past transportation history. The transportation unit, for example, suggests the optimal transportation means based on transportation means used by the user in the past. The transportation unit can also suggest transportation means that avoid congestion based on the user's past transportation history. Furthermore, the transportation unit can analyze the user's past transportation history and suggest the most efficient transportation means. This enables the transportation unit to select the optimal transportation means based on the user's past transportation history. The past transportation history includes, for example, travel date and time, transportation means, and travel route. The optimal transportation means includes, for example, travel time, cost, and comfort. Some or all of the above-mentioned processing in the transportation unit may be performed, for example, using AI, or may be performed without using AI.

[0076] During transportation support, the transportation unit can provide route guidance in real time based on the user's current location information. For example, the transportation unit updates the user's current location in real time while the user is traveling and provides route guidance. The transportation unit can also update the user's current location in real time as the user approaches the destination and suggest an optimal route. Furthermore, if the user becomes lost, the transportation unit can update the user's current location in real time and provide route guidance again. This enables the transportation unit to provide real-time route guidance based on the user's current location information. Current location information includes, for example, GPS data, Wi-Fi location information, beacons, etc. To provide route guidance in real time, for example, map display, voice guidance, real-time updates, etc. are performed. Some or all of the above-described processing in the transportation unit may be performed using, for example, AI, or may be performed without using AI.

[0077] The information providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, when the user is nervous, the information providing unit provides simple, highly visible information. Furthermore, when the user is relaxed, the information providing unit can provide detailed information. Furthermore, when the user is feeling anxious, the information providing unit can provide information that gives a sense of security. This enables the information providing unit to provide appropriate information according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. Some or all of the above-mentioned processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI.

[0078] When providing information, the information providing unit can select appropriate information based on the user's current situation. For example, the information providing unit can prioritize providing information about the area where the user is currently staying. The information providing unit can also prioritize providing information related to a problem the user is currently facing. Furthermore, the information providing unit can select optimal information based on the user's current situation. This enables the information providing unit to provide optimal information based on the user's current situation. The current situation includes, for example, a place of stay, a means of transportation, a travel itinerary, etc. Appropriate information includes, for example, the user's interests, current situation, past history, etc. Some or all of the above-mentioned processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI.

[0079] When providing information, the information providing unit can improve the accuracy of the information by referring to the user's past information provision history. The information providing unit, for example, provides optimal information based on information the user has received in the past. The information providing unit can also analyze the user's past information provision history to reflect the user's preference trends in the information. Furthermore, the information providing unit can analyze the user's past information provision history to improve the information provision algorithm. This enables the information providing unit to provide highly accurate information based on the user's past information provision history. The past information provision history includes, for example, the date and time of provision, the content of provision, and user feedback. To improve the accuracy of the information, for example, feedback is reflected, the algorithm is improved, and so on. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI.

[0080] The information providing unit can improve the information provision method by reflecting user feedback when providing information. The information providing unit can, for example, suggest an optimal information provision method based on feedback previously provided by the user. The information providing unit can also preferentially suggest a specific information provision method based on the user's past feedback. Furthermore, the information providing unit can analyze the user's past feedback and improve the information provision method. This enables the information providing unit to improve the optimal information provision method based on the user's feedback. Feedback includes, for example, user ratings, comments, survey results, etc. To improve the information provision method, for example, feedback can be reflected, algorithms can be improved, etc. Some or all of the above-mentioned processing in the information providing unit can be performed using AI, for example, or without AI.

[0081] The linking unit can estimate the user's emotions and select apps to link based on the estimated user emotions. For example, if the user is excited, the linking unit can preferentially suggest entertainment apps. Furthermore, if the user is relaxed, the linking unit can preferentially suggest relaxation apps. Furthermore, if the user is feeling anxious, the linking unit can preferentially suggest apps that provide a sense of security. This enables the linking unit to select the optimal app according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. Some or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI.

[0082] During collaboration, the collaboration unit can select an appropriate app based on the user's current travel situation. For example, if the user is sightseeing, the collaboration unit can preferentially suggest a tourist information app. Furthermore, if the user is traveling, the collaboration unit can also preferentially suggest a transportation information app. Furthermore, if the user is eating, the collaboration unit can also preferentially suggest a restaurant rating app. This enables the collaboration unit to select an optimal app based on the user's current travel situation. The current travel situation includes, for example, a place of stay, a means of transportation, a travel itinerary, etc. An appropriate app includes, for example, the user's interests, current situation, past history, etc. Some or all of the above-described processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI.

[0083] During collaboration, the collaboration unit can improve the accuracy of collaboration by referring to the user's past collaboration history. The collaboration unit, for example, suggests optimal apps based on apps the user has used in the past. The collaboration unit can also analyze the user's preferences from the user's past collaboration history and reflect the results in the apps. The collaboration unit can also analyze the user's past collaboration history and improve the collaboration algorithm. This enables the collaboration unit to perform collaboration with high accuracy based on the user's past collaboration history. The past collaboration history includes, for example, the date and time of collaboration, the content of collaboration, and user feedback. To improve the accuracy of collaboration, for example, feedback is reflected, the algorithm is improved, and so on. Some or all of the above-described processing in the collaboration unit may be performed using AI, or may be performed without using AI.

[0084] The collaboration unit can improve the collaboration method by reflecting user feedback during collaboration. The collaboration unit can, for example, propose an optimal collaboration method based on feedback provided by the user in the past. The collaboration unit can also preferentially propose a specific collaboration method based on the user's past feedback. The collaboration unit can also analyze the user's past feedback and improve the collaboration method. This enables the collaboration unit to improve the optimal collaboration method based on user feedback. Feedback includes, for example, user ratings, comments, survey results, etc. To improve the collaboration method, for example, the feedback can be reflected, the algorithm can be improved, etc. Some or all of the above-mentioned processing in the collaboration unit may be performed using AI, for example, or without AI.

[0085] The linking unit can estimate the user's emotions and determine the priority of linked apps based on the estimated user emotions. For example, if the user is excited, the linking unit can preferentially suggest entertainment apps. Furthermore, if the user is relaxed, the linking unit can preferentially suggest relaxation apps. Furthermore, if the user is feeling anxious, the linking unit can preferentially suggest apps that provide a sense of security. This enables the linking unit to determine the optimal priority of apps according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. Some or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI.

[0086] During linking, the linking unit can select an appropriate app by taking into account the user's geographical location information. For example, the linking unit may preferentially suggest a tourist information app for the area where the user is currently staying. The linking unit may also preferentially suggest a transportation information app for the area where the user is currently staying. The linking unit may also preferentially suggest a restaurant rating app for the area where the user is currently staying. This enables the linking unit to select an optimal app based on the user's geographical location information. Geographical location information includes, for example, GPS data, Wi-Fi location information, beacons, etc. Appropriate apps include, for example, the user's interests, current situation, past history, etc. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI.

[0087] During linking, the linking unit can analyze the user's social media activity and suggest related apps. For example, the linking unit can suggest apps related to places the user has checked in to on social media. The linking unit can also analyze the content of the user's social media posts and suggest related apps. Furthermore, the linking unit can suggest related apps based on the activity of the user's friends on social media. This enables the linking unit to suggest optimal apps based on the user's social media activity. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc. Related apps include, for example, the user's interests, current status, past history, etc. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI.

[0088] The collaboration unit can customize the collaboration method by reflecting the user's past feedback during collaboration. The collaboration unit, for example, suggests an optimal collaboration method based on feedback provided by the user in the past. The collaboration unit can also preferentially suggest a specific collaboration method based on the user's past feedback. The collaboration unit can also analyze the user's past feedback and improve the collaboration method. This enables the collaboration unit to customize the optimal collaboration method based on the user's past feedback. Past feedback includes, for example, user ratings, comments, survey results, etc. To customize the collaboration method, for example, feedback is reflected, algorithms are improved, etc. Some or all of the above-mentioned processing in the collaboration unit may be performed using AI, for example, or without AI. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, suggestion unit, movement unit, information provision unit, and linking unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and accepts information input by the traveler. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information accepted by the reception unit and proposes an optimal travel plan. The movement unit is realized, for example, by the control unit 46A of the smart device 14 and supports movement based on the proposed plan. The information provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides information on local rules and problems. The linking unit links with a domestic app, for example, via the communication I / F 44 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, suggestion unit, movement unit, information provision unit, and 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 reception unit is realized by the microphone 238 of the smart glasses 214 and accepts input information from the traveler. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information accepted by the reception unit and proposes an optimal travel plan. The movement unit is realized, for example, by the control unit 46A of the smart glasses 214 and supports movement based on the proposed plan. The information provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides information on local rules and problems. The linking unit links with a domestic app, for example, via the communication I / F 44 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, suggestion unit, movement unit, information provision unit, and linking unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314 and accepts input information from the traveler. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information accepted by the reception unit and proposes an optimal travel plan. The movement unit is realized, for example, by the control unit 46A of the headset terminal 314 and supports movement based on the proposed plan. The information provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides information on local rules and problems. The linking unit links with a domestic app, for example, via the communication I / F 44 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, suggestion unit, movement unit, information provision unit, and linking unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and accepts input information from the traveler. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information accepted by the reception unit and proposes an optimal travel plan. The movement unit is realized, for example, by the control unit 46A of the robot 414 and supports movement based on the proposed plan. The information provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides information on local rules and problems. The linking unit links with a domestic app, for example, via the communication I / F 44 of the robot 414.

[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] When accepting information input by the user, the reception unit can monitor the user's current health condition and provide appropriate feedback. For example, if the user is tired, the reception unit can simplify the input work and adjust it so that it can be completed in a short time. Also, if the user is feeling stressed, the reception unit can provide an interface that helps the user relax. Furthermore, if the user has a health problem, the reception unit can provide information on appropriate medical institutions. This enables the reception unit to accept optimal information according to the user's health condition.

[0091] The suggestion unit can analyze the user's past travel history and suggest similar travel plans. For example, it can suggest the user's next travel plan based on places the user has visited and activities the user has participated in in the past. It can also make new suggestions based on tourist spots and restaurants the user has previously liked. It can also suggest plans tailored to specific seasons or events based on the user's past travel history. This allows the suggestion unit to provide the user with the optimal travel plan based on the user's past travel history.

[0092] The mobility unit can monitor traffic conditions in real time while the user is traveling and suggest optimal routes. For example, it can suggest detour routes based on traffic congestion and accident information. It can also suggest alternative means of transportation based on information about delays in public transportation. Furthermore, if the user is in a hurry, it can prioritize suggesting routes that will reach the destination in the shortest time. This enables the mobility unit to provide optimal mobility support according to real-time traffic conditions.

[0093] The information providing unit can provide appropriate information based on the user's current weather information. For example, if it is raining, it can suggest indoor activities that can be enjoyed. On a hot day, it can also suggest cool places or places where you can enjoy cold drinks. On a cold day, it can also suggest warm places or places where you can enjoy hot drinks. This allows the information providing unit to provide optimal information according to the user's current weather conditions.

[0094] The linking unit can link with the user's social media account and make suggestions based on the travel history of friends. For example, suggestions can be made to the user based on tourist spots visited by friends and activities participated in. Suggestions can also be made to the user based on restaurants and accommodations that friends have given high ratings to. Furthermore, new travel plans can be suggested to the user based on travel photos and comments shared by friends. This enables the linking unit to make optimal suggestions based on the user's social media activity.

[0095] The reception unit can estimate the user's emotions and customize the method of receiving input information based on the estimated user emotions. For example, if the user is excited, a simple and intuitive interface can be provided. Alternatively, if the user is relaxed, detailed input options can be provided. Furthermore, if the user is feeling anxious, a guide message that provides a sense of security can be displayed. This enables the reception unit to receive information optimally according to the user's emotions.

[0096] The suggestion unit can estimate the user's emotions and adjust the content of suggestions based on the estimated user emotions. For example, if the user is excited, the suggestion unit can suggest active activities. If the user is relaxed, the suggestion unit can suggest relaxation activities. Furthermore, if the user is feeling anxious, the suggestion unit can suggest activities that will give the user a sense of security. This enables the suggestion unit to make optimal suggestions according to the user's emotions.

[0097] The mobility unit can estimate the user's emotions and adjust the mobility support method based on the estimated user's emotions. For example, if the user is nervous, it can provide simple, highly visible route guidance. If the user is relaxed, it can also provide detailed route guidance. Furthermore, if the user is in a hurry, it can also preferentially suggest the shortest route. This enables the mobility unit to provide appropriate mobility support according to the user's emotions.

[0098] The information providing unit can estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, if the user is nervous, simple, highly visible information can be provided. If the user is relaxed, detailed information can be provided. Furthermore, if the user is feeling anxious, information that gives a sense of security can be provided. This enables the information providing unit to provide appropriate information according to the user's emotions.

[0099] The linking unit can estimate the user's emotions and select apps to link based on the estimated user emotions. For example, if the user is excited, entertainment apps can be preferentially suggested. Also, if the user is relaxed, relaxation apps can be preferentially suggested. Furthermore, if the user is feeling anxious, apps that give a sense of security can be preferentially suggested. This enables the linking unit to select the optimal app according to the user's emotions.

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

[0101] Step 1: The reception unit receives input information from the traveler. The input information can include text, audio, images, etc. For example, the information is received by inputting places the traveler wants to visit or activities they are interested in. Voice input can also be received using voice recognition technology. Step 2: The proposal unit analyzes the information received by the reception unit and proposes the optimal travel plan. The analysis uses techniques such as natural language processing and data mining. For example, if a traveler inputs "I want to go sightseeing in Tokyo," the system will propose a plan based on information about Tokyo's tourist spots and events. Step 3: The travel unit supports travel based on the plan proposed by the proposal unit. For example, it provides information on public transportation and route guidance. If a traveler inputs "I want to go from Shinjuku to Shibuya," it suggests the optimal route and displays transfer information and travel time. Step 4: The Information Department provides information on local rules and issues that travelers may encounter in Japan, such as how to separate garbage and what to do in an emergency. Step 5: The collaboration department will collaborate with domestic apps, such as restaurant rating apps and electronic payment apps, to enable travelers to use these apps.

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

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

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

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

[0153] The data processing system 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 reception unit that receives input information of a traveler; a proposal unit that analyzes the information received by the reception unit and proposes an appropriate travel plan; a movement unit that supports movement based on the plan proposed by the proposal unit; An information department that provides information on local rules and issues; A linking unit that links with domestic applications. A system characterized by:

2. The proposal unit By inputting the places you want to visit and the activities you are interested in, we can suggest suitable travel plans.

2. The system of claim 1.

3. The moving unit is Providing public transport information and route guidance 2. The system of claim 1.

4. The information providing unit Providing information on how to separate garbage, what to do in an emergency, and other issues travelers may face in Japan 2. The system of claim 1.

5. The linking unit is Link with restaurant rating apps or electronic payment apps to allow travelers to use these apps 2. The system of claim 1.

6. The reception unit Estimates user emotions and adjusts the timing of accepting input information based on the estimated user emotions.

2. The system of claim 1.

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

8. The reception unit As input information is received, it is filtered based on the user's current travel situation and interests.

2. The system of claim 1.

9. The reception unit When accepting input information, select an appropriate acceptance method depending on the user's input method.

2. The system of claim 1.

10. The proposal unit Inferring user emotions and adjusting the presentation of suggestions based on the estimated user emotions 2. The system of claim 1.

Images