System

Abstract

An object of the system according to the embodiment is to make an optimal route or proposal in consideration of traffic conditions, weather, and individual requests when making a travel plan.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a determination unit. The reception unit inputs information of a desired place, a desired date, a desired companion, a desired budget, and a desired restriction / request. The analysis unit analyzes the information received by the reception unit and proposes a route in consideration of traffic conditions and weather. The proposal unit proposes a lunch, a dinner, and a souvenir based on the route proposed by the analysis unit. The determination unit determines a transfer of transportation based on the information proposed by the proposal unit. The proposal unit proposes another method based on the information determined by the determination unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to provide optimal routes and suggestions that took into account traffic conditions, weather, and individual requests when planning a trip.

[0005] The system according to the embodiment aims to propose optimal routes that take into consideration traffic conditions, weather, and individual needs when planning a trip. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a determination unit. The reception unit inputs the desired destination, date, information about accompanying persons, budget, and restrictions / requests. The analysis unit analyzes the information received by the reception unit and proposes a route taking into consideration traffic conditions and weather. The proposal unit suggests lunch, dinner, and souvenirs based on the route proposed by the analysis unit. The determination unit determines transportation connections based on the information proposed by the proposal unit. The proposal unit proposes alternative methods based on the information determined by the determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal routes and suggestions that take into consideration traffic conditions, weather, and individual requests when making travel plans. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A travel support system according to an embodiment of the present invention is a system that, when a user inputs a desired destination, date, travel companion information, budget, restrictions, and requests, proposes routes, lunches, dinners, and souvenirs taking into account the traffic conditions and weather for that day. The travel support system then analyzes the user's desired destination, date, travel companion information, budget, restrictions, and requests. AI then analyzes the information and proposes the optimal route, taking into account traffic conditions and weather. Furthermore, the system also proposes lunches, dinners, and souvenirs. Regarding transportation connections, the system determines whether the user can make a connection from their current location to their next destination on time, and if not, suggests an alternative option. When proposing meals and souvenirs, the system also takes into account allergies and preferences. For example, a travel support system may propose a desired destination, date, travel companion information, budget, restrictions, and requests. The user simply inputs the departure and destination locations, travel dates, travel companion information, budget, restrictions, and requests. The AI ​​then analyzes the input information and proposes the optimal route, taking into account traffic conditions and weather. The AI ​​collects real-time traffic and weather information and calculates the optimal route. Furthermore, the AI ​​also proposes lunches, dinners, and souvenirs. For example, the system will suggest nearby restaurants and souvenir shops based on the time the user is expected to arrive at a tourist spot. The system will make these suggestions while taking into consideration the user's allergies and preferences. When it comes to transportation connections, the AI ​​will also determine whether the user will be able to make it from their current location to their next destination on time. For example, if the user is likely to miss a train, the AI ​​will suggest an alternative means of transportation. This allows the user to enjoy their trip smoothly. This enables the travel support system to suggest optimal routes, meals, and souvenirs based on the information entered by the user. This allows the travel support system to allow users to go on trips without a plan using their smartphone. For example, when a user plans a trip using their smartphone, the AI ​​will suggest optimal routes, meals, and souvenirs, allowing the user to enjoy their trip with peace of mind.

[0029] A travel support system according to an embodiment includes a reception unit, an analysis unit, a suggestion unit, and a determination unit. The reception unit inputs a desired destination, a date, travel companion information, a budget, and restrictions and requests. For example, the reception unit allows a user to input a departure point, a destination, a travel date, travel companion information, a budget, and restrictions and requests. The analysis unit analyzes the information received by the reception unit and proposes a route taking into account traffic conditions and weather. For example, the analysis unit collects real-time traffic and weather information and calculates an optimal route. The suggestion unit suggests lunch, dinner, and souvenirs based on the route proposed by the analysis unit. For example, the suggestion unit suggests nearby restaurants and souvenir shops based on the user's arrival time at a tourist attraction. In this case, the suggestion unit makes suggestions taking into account the user's allergies and preferences. The determination unit determines transportation connections based on the information proposed by the suggestion unit. For example, the determination unit determines whether transportation from the current location to the next destination is on time. The suggestion unit suggests alternative options based on the information determined by the determination unit. For example, if the user cannot make the required transportation, the suggestion unit suggests an alternative means of transportation. This allows the travel support system according to the embodiment to suggest optimal routes, meals, and souvenirs based on the user's input information.

[0030] The suggestion unit can suggest lunches, dinners, and souvenirs based on the user's allergies and preferences. For example, the suggestion unit can suggest restaurants that do not contain ingredients to which the user is allergic. The suggestion unit can also suggest restaurants that offer allergy-friendly menus based on the user's allergy information. The suggestion unit can also customize meal suggestions taking the user's allergy information into consideration. For example, the suggestion unit can analyze the user's preferences and suggest optimal lunches, dinners, and souvenirs. This makes it possible to make suggestions based on the user's allergies and preferences.

[0031] The analysis unit can collect real-time traffic information and weather information and propose a route. For example, the analysis unit collects real-time traffic information from a traffic information service. The analysis unit can also collect real-time weather information from data from the Japan Meteorological Agency. The analysis unit can also collect real-time traffic information using sensor information. For example, the analysis unit proposes an optimal route based on traffic congestion information. The analysis unit can also propose a route that suits the weather based on a weather forecast. This makes it possible to propose an optimal route based on real-time information.

[0032] The determination unit can make a determination based on the arrival time of a transportation facility from the current location to the next destination. The determination unit calculates the arrival time based on, for example, the transportation facility's operation schedule. The determination unit can also calculate the arrival time using a prediction algorithm. The determination unit can also calculate the arrival time based on real-time traffic information. For example, the determination unit calculates the arrival time at the next destination based on the transportation facility's operation schedule. The determination unit can also predict the arrival time of the transportation facility using a prediction algorithm. This allows for smooth transfers between transportation facilities.

[0033] The suggestion unit can suggest an alternative means of transportation if the means of transportation is delayed for the arrival time. For example, the suggestion unit can suggest a taxi. The suggestion unit can also suggest a rental car. The suggestion unit can also suggest a bicycle. For example, the suggestion unit can suggest a taxi if the means of transportation is delayed. The suggestion unit can also suggest a rental car. This makes it possible to suggest an alternative means of transportation even if the means of transportation is not on time.

[0034] The suggestion unit can suggest nearby restaurants and souvenir shops based on the time the user will arrive at the tourist destination. For example, the suggestion unit can suggest nearby restaurants based on the time the user will arrive at the tourist destination. The suggestion unit can also suggest nearby souvenir shops based on the time the user will arrive at the tourist destination. The suggestion unit can also suggest nearby tourist spots based on the time the user will arrive at the tourist destination. For example, the suggestion unit can suggest restaurants based on the time the user will arrive at the tourist destination. The suggestion unit can also suggest souvenir shops. This makes it possible to make suggestions based on the time the user will arrive at the tourist destination.

[0035] The reception unit can analyze the user's past travel history and automatically complete input items. For example, the reception unit can automatically display candidates for places the user wants to go to based on places the user has visited in the past. The reception unit can also automatically complete information about companions that the user has previously input. The reception unit can also automatically set budgets and restrictions based on the user's past travel history. For example, the reception unit can display candidates for places the user wants to go to based on places the user has visited in the past. The reception unit can also complete information about companions that the user has previously input. This makes it possible to automatically complete input items based on the user's past travel history.

[0036] The reception unit can automatically acquire the user's current location information when inputting the information and set it as the departure point. For example, when the user opens the app, the reception unit automatically acquires the user's current location and sets it as the departure point. Furthermore, when the user inputs a destination, the reception unit can also suggest optimal candidate locations taking into account the distance from the current location. Furthermore, when the user uses the app while on the move, the reception unit can also update the current location in real time and reflect it as the departure point. For example, when the user opens the app, the reception unit acquires the user's current location and sets it as the departure point. Furthermore, when the user inputs a destination, the reception unit can also suggest candidate locations taking into account the distance from the current location. In this way, the current location information can be automatically acquired and set as the departure point.

[0037] The reception unit can customize input fields by reflecting the user's past feedback when inputting data. For example, the reception unit automatically customizes the input fields based on feedback provided by the user in the past. The reception unit can also improve the input fields by taking into account points of dissatisfaction the user has had in the past. The reception unit can also analyze the user's past feedback and suggest an optimal input method. For example, the reception unit customizes the input fields based on feedback provided by the user in the past. The reception unit can also improve the input fields by taking into account points of dissatisfaction the user has had in the past. In this way, the input fields can be customized based on past feedback.

[0038] The reception unit can provide the optimal input method at the time of input, taking into consideration device information of the user. For example, if the user is using a smartphone, the reception unit can provide an interface optimized for touch input. Furthermore, if the user is using a tablet, the reception unit can also provide an input method optimized for a large screen. Furthermore, if the user is using a desktop, the reception unit can also provide an interface optimized for keyboard input. For example, if the user is using a smartphone, the reception unit can provide an interface optimized for touch input. Furthermore, if the user is using a tablet, the reception unit can also provide an input method optimized for a large screen. In this way, the optimal input method can be provided based on device information.

[0039] The reception unit can analyze the user's social media activity at the time of input and suggest related input items. For example, the reception unit can suggest potential places the user would like to visit based on places the user has checked in to on social media. The reception unit can also analyze the content of the user's social media posts and suggest related input items. The reception unit can also suggest related input items by referring to the activity of the user's friends on social media. For example, the reception unit can suggest potential places the user would like to visit based on places the user has checked in to on social media. The reception unit can also analyze the content of the user's social media posts and suggest related input items. In this way, related input items can be suggested based on social media activity.

[0040] The reception unit can customize input items by reflecting the user's past travel history when inputting information. For example, the reception unit automatically displays candidate destinations based on places the user has visited in the past. The reception unit can also automatically complete information about companions previously entered by the user. The reception unit can also automatically set budgets and restrictions based on the user's past travel history. For example, the reception unit displays candidate destinations based on places the user has visited in the past. The reception unit can also complete information about companions previously entered by the user. This allows the input items to be customized based on the user's past travel history.

[0041] During analysis, the analysis unit can improve prediction accuracy by referring to past traffic information and weather data. The analysis unit can, for example, propose an optimal route based on past traffic congestion information. The analysis unit can also propose a route that suits the weather by referring to past weather data. The analysis unit can also propose a safe route based on past traffic accident information. For example, the analysis unit can propose an optimal route based on past traffic congestion information. The analysis unit can also propose a route that suits the weather by referring to past weather data. This makes it possible to improve prediction accuracy based on past data.

[0042] During analysis, the analysis unit can propose a route taking the user's travel history into consideration. For example, the analysis unit can propose an optimal route based on routes the user has used in the past. The analysis unit can also propose a route that avoids crowded areas based on the user's past travel history. The analysis unit can also analyze the user's past travel history and propose the most efficient route. For example, the analysis unit can propose an optimal route based on routes the user has used in the past. The analysis unit can also propose a route that avoids crowded areas based on the user's past travel history. This makes it possible to propose an optimal route based on travel history.

[0043] During analysis, the analysis unit can update the route in real time based on the user's current location information. For example, the analysis unit updates the user's current location in real time while the user is moving and proposes an optimal route. The analysis unit can also update the user's current location in real time as the user approaches the destination and propose an optimal route. If the user gets lost, the analysis unit can update the user's current location in real time and propose an optimal route again. For example, the analysis unit updates the user's current location in real time while the user is moving and proposes an optimal route. The analysis unit can also update the user's current location in real time as the user approaches the destination and propose an optimal route. This allows the route to be updated in real time based on the current location information.

[0044] During analysis, the analysis unit can provide an optimal analysis method taking into account device information of the user. For example, if the user is using a smartphone, the analysis unit can provide an analysis method that matches the screen size. Furthermore, if the user is using a tablet, the analysis unit can also provide an analysis method that is optimized for a large screen. Furthermore, if the user is using a desktop, the analysis unit can also provide an analysis method that is optimized for keyboard input. For example, if the user is using a smartphone, the analysis unit can provide an analysis method that matches the screen size. Furthermore, if the user is using a tablet, the analysis unit can also provide an analysis method that is optimized for a large screen. This makes it possible to provide an optimal analysis method based on device information.

[0045] During the analysis, the analysis unit can analyze the user's social media activity and suggest related routes. For example, the analysis unit can suggest an optimal route based on places where the user has checked in on social media. The analysis unit can also analyze the content of the user's social media posts and suggest related routes. The analysis unit can also suggest related routes by taking into account the activities of the user's friends on social media. For example, the analysis unit can suggest an optimal route based on places where the user has checked in on social media. The analysis unit can also analyze the content of the user's social media posts and suggest related routes. In this way, related routes can be suggested based on social media activity.

[0046] The analysis unit can customize the analysis method by reflecting the user's past feedback during analysis. For example, the analysis unit automatically customizes the analysis method based on feedback provided by the user in the past. The analysis unit can also improve the analysis method by taking into account points of dissatisfaction that the user has had in the past. The analysis unit can also analyze the user's past feedback and propose an optimal analysis method. For example, the analysis unit customizes the analysis method based on feedback provided by the user in the past. The analysis unit can also improve the analysis method by taking into account points of dissatisfaction that the user has had in the past. In this way, the analysis method can be customized based on past feedback.

[0047] When making a suggestion, the suggestion unit can make a meal suggestion taking into account the user's allergy information. For example, the suggestion unit can suggest restaurants that do not contain ingredients to which the user is allergic. The suggestion unit can also suggest restaurants that offer allergy-friendly menus based on the user's allergy information. The suggestion unit can also customize the meal suggestion taking into account the user's allergy information. For example, the suggestion unit can suggest restaurants that do not contain ingredients to which the user is allergic. The suggestion unit can also suggest restaurants that offer allergy-friendly menus based on the user's allergy information. This makes it possible to make meal suggestions based on allergy information.

[0048] When making a suggestion, the suggestion unit can analyze the user's preferences and suggest lunch, dinner, or souvenirs. For example, the suggestion unit can suggest the optimal lunch or dinner based on restaurants that the user has visited with preference in the past. The suggestion unit can also analyze the user's preferences and suggest the optimal souvenir. The suggestion unit can also suggest the optimal meal or souvenir based on the user's past selection history. For example, the suggestion unit can suggest the optimal lunch or dinner based on restaurants that the user has visited with preference in the past. The suggestion unit can also analyze the user's preferences and suggest the optimal souvenir. This makes it possible to make optimal suggestions based on the user's preferences.

[0049] When making suggestions, the suggestion unit can improve the accuracy of the suggestions by referring to the user's past travel history. For example, the suggestion unit can suggest optimal tourist spots based on places the user has visited in the past. The suggestion unit can also suggest favorite restaurants and souvenir shops from the user's past travel history. The suggestion unit can also analyze the user's past travel history to make the most appropriate suggestions. For example, the suggestion unit can suggest optimal tourist spots based on places the user has visited in the past. The suggestion unit can also suggest favorite restaurants and souvenir shops from the user's past travel history. This makes it possible to improve the accuracy of suggestions based on the past travel history.

[0050] When making a suggestion, the suggestion unit can make a highly relevant suggestion taking into account the user's geographical location information. The suggestion unit, for example, suggests restaurants and tourist spots close to the user's current location. The suggestion unit can also suggest the optimal means of transportation based on the user's geographical location information. The suggestion unit can also make an optimal suggestion taking into account the distance from the user's current location. For example, the suggestion unit suggests restaurants and tourist spots close to the user's current location. The suggestion unit can also suggest the optimal means of transportation based on the user's geographical location information. This makes it possible to make a highly relevant suggestion based on the geographical location information.

[0051] When making a suggestion, the suggestion unit can analyze the user's social media activity and make a related suggestion. For example, the suggestion unit can make a related suggestion based on places where the user has checked in on social media. The suggestion unit can also analyze the content of the user's posts on social media and suggest related tourist spots and stores. The suggestion unit can also make a related suggestion by referring to the activities of the user's friends on social media. For example, the suggestion unit can make a related suggestion based on places where the user has checked in on social media. The suggestion unit can also analyze the content of the user's posts on social media and suggest related tourist spots and stores. In this way, it is possible to make a related suggestion based on social media activity.

[0052] The suggestion unit can customize the suggestion method by reflecting the user's past feedback when making a suggestion. For example, the suggestion unit automatically customizes the suggestion method based on feedback provided by the user in the past. The suggestion unit can also improve the suggestion method by taking into account points of dissatisfaction that the user has had in the past. The suggestion unit can also analyze the user's past feedback and suggest an optimal suggestion method. For example, the suggestion unit customizes the suggestion method based on feedback provided by the user in the past. The suggestion unit can also improve the suggestion method by taking into account points of dissatisfaction that the user has had in the past. In this way, the suggestion method can be customized based on past feedback.

[0053] When making a judgment, the judgment unit can improve the prediction accuracy of the next transportation means by referring to past traffic information. The judgment unit improves the prediction accuracy of the next transportation means, for example, based on past traffic congestion information. The judgment unit can also improve the prediction accuracy of the next transportation means by referring to past operation statuses of public transportation means. The judgment unit can also suggest safe transportation means based on past traffic accident information. For example, the judgment unit improves the prediction accuracy of the next transportation means based on past traffic congestion information. The judgment unit can also improve the prediction accuracy of the next transportation means by referring to past operation statuses of public transportation means. This makes it possible to improve the prediction accuracy of the next transportation means based on past traffic information.

[0054] When making a determination, the determination unit can make the determination in real time based on the user's current location information. For example, the determination unit updates the user's current location in real time while the user is moving and determines the next means of transportation. The determination unit can also update the user's current location in real time and determine the next means of transportation as the user approaches the destination. If the user gets lost, the determination unit can update the user's current location in real time and determine the optimal means of transportation again. For example, the determination unit updates the user's current location in real time while the user is moving and determines the next means of transportation. The determination unit can also update the user's current location in real time as the user approaches the destination and determine the next means of transportation. This allows for real-time determination based on the current location information.

[0055] When making a determination, the determination unit can improve the accuracy of the determination by referring to the user's past travel history. For example, the determination unit determines the next means of transportation based on the means of transportation used by the user in the past. The determination unit can also determine a means of transportation that avoids congestion based on the user's past travel history. The determination unit can also analyze the user's past travel history and determine the most efficient means of transportation. For example, the determination unit determines the next means of transportation based on the means of transportation used by the user in the past. The determination unit can also determine a means of transportation that avoids congestion based on the user's past travel history. This makes it possible to improve the accuracy of the determination based on the past travel history.

[0056] The determination unit can provide an optimal determination method by taking into consideration device information of the user when making a determination. For example, if the user is using a smartphone, the determination unit can provide a determination method that matches the screen size. Furthermore, if the user is using a tablet, the determination unit can provide a determination method that is optimized for a large screen. Furthermore, if the user is using a desktop, the determination unit can provide a determination method that is optimized for keyboard input. For example, if the user is using a smartphone, the determination unit can provide a determination method that matches the screen size. Furthermore, if the user is using a tablet, the determination unit can provide a determination method that is optimized for a large screen. This makes it possible to provide an optimal determination method based on device information.

[0057] When making a determination, the determination unit can analyze the user's social media activity and make a related determination. For example, the determination unit can determine the next means of transportation based on the location where the user checked in on social media. The determination unit can also analyze the content of the user's posts on social media to determine the related means of transportation. The determination unit can also refer to the activities of the user's friends on social media to determine the related means of transportation. For example, the determination unit can determine the next means of transportation based on the location where the user checked in on social media. The determination unit can also analyze the content of the user's posts on social media to determine the related means of transportation. In this way, a related determination can be made based on social media activity.

[0058] The determination unit can customize the determination method by reflecting the user's past feedback when making a determination. The determination unit automatically customizes the determination method, for example, based on feedback provided by the user in the past. The determination unit can also improve the determination method by taking into account points of dissatisfaction the user has had in the past. The determination unit can also analyze the user's past feedback and propose an optimal determination method. For example, the determination unit customizes the determination method based on feedback provided by the user in the past. The determination unit can also improve the determination method by taking into account points of dissatisfaction the user has had in the past. In this way, the determination method can be customized based on past feedback.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The analysis unit can analyze travel trends based on the user's past travel history and suggest tourist spots and activities that the user prefers. For example, it can analyze data on tourist spots that the user has visited in the past and suggest new tourist spots with similar characteristics. It can also suggest similar activities based on data on activities the user has participated in in the past. It can also suggest the best time to travel by taking into account data on the seasons and weather conditions in which the user has visited in the past. This makes it possible to make more personalized suggestions based on the user's past travel history.

[0061] The determination unit can monitor the user's current health condition and suggest an optimal travel plan based on the health condition. For example, it can monitor the user's heart rate and number of steps and suggest a route that avoids excessive exercise. If the user is tired, it can also suggest a route that includes many rest points. Furthermore, if the user has health problems, it can also suggest nearby medical facilities. This makes it possible to suggest a safe and comfortable travel plan that suits the user's health condition.

[0062] The analysis unit can analyze the user's social media activity and make suggestions based on the places the user's friends have visited and the events they have attended. For example, it can suggest tourist spots and restaurants where the user's friends have checked in. It can also suggest events and activities that the user's friends have attended. It can also suggest related tourist spots and activities based on photos and reviews posted by the user's friends. This makes it possible to make suggestions that utilize the user's social network.

[0063] The analysis unit can analyze the user's past feedback and improve the accuracy of suggestions based on the feedback. For example, it can prioritize suggestions of tourist spots and restaurants that the user has previously rated highly. It can also make suggestions that avoid places that the user has previously rated poorly. Furthermore, it can customize the suggestions based on the user's feedback to make suggestions that will provide greater satisfaction. This makes it possible to make more accurate suggestions based on the user's past feedback.

[0064] The analysis unit can provide the optimal information display method based on the user's device information. For example, if the user is using a smartwatch, information can be provided in the form of a simple notification. If the user is using a smartphone, an interface containing detailed information can be provided. Furthermore, if the user is using a tablet, it is possible to provide an information display method optimized for a large screen. This makes it possible to display information optimally according to the user's device.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit inputs the desired destination, date, travel companion information, budget, restrictions and requests. For example, the user can input the departure and destination, travel date, travel companion information, budget, restrictions and requests. Step 2: The analysis unit analyzes the information received by the reception unit and proposes a route taking into account traffic conditions and weather. For example, it collects real-time traffic and weather information and calculates the optimal route. Step 3: The suggestion unit makes suggestions for lunch, dinner, and souvenirs based on the route proposed by the analysis unit. For example, it suggests nearby restaurants and souvenir shops based on the user's arrival time at a tourist spot. The suggestion takes into account the user's allergies and preferences. Step 4: The determination unit determines whether to transfer between transportation modes based on the information suggested by the suggestion unit, for example, whether the transportation mode from the current location to the next destination will arrive in time. Step 5: The suggestion unit suggests an alternative method based on the information determined by the determination unit. For example, if the transportation is not on time, an alternative transportation method is suggested.

[0067] (Example 2) A travel support system according to an embodiment of the present invention is a system that, when a user inputs a desired destination, date, travel companion information, budget, restrictions, and requests, proposes routes, lunches, dinners, and souvenirs taking into account the traffic conditions and weather for that day. The travel support system then analyzes the user's desired destination, date, travel companion information, budget, restrictions, and requests. AI then analyzes the information and proposes the optimal route, taking into account traffic conditions and weather. Furthermore, the system also proposes lunches, dinners, and souvenirs. Regarding transportation connections, the system determines whether the user can make a connection from their current location to their next destination on time, and if not, suggests an alternative option. When proposing meals and souvenirs, the system also takes into account allergies and preferences. For example, a travel support system may propose a desired destination, date, travel companion information, budget, restrictions, and requests. The user simply inputs the departure and destination locations, travel dates, travel companion information, budget, restrictions, and requests. The AI ​​then analyzes the input information and proposes the optimal route, taking into account traffic conditions and weather. The AI ​​collects real-time traffic and weather information and calculates the optimal route. Furthermore, the AI ​​also proposes lunches, dinners, and souvenirs. For example, the system will suggest nearby restaurants and souvenir shops based on the time the user is expected to arrive at a tourist spot. The system will make these suggestions while taking into consideration the user's allergies and preferences. When it comes to transportation connections, the AI ​​will also determine whether the user will be able to make it from their current location to their next destination on time. For example, if the user is likely to miss a train, the AI ​​will suggest an alternative means of transportation. This allows the user to enjoy their trip smoothly. This enables the travel support system to suggest optimal routes, meals, and souvenirs based on the information entered by the user. This allows the travel support system to allow users to go on trips without a plan using their smartphone. For example, when a user plans a trip using their smartphone, the AI ​​will suggest optimal routes, meals, and souvenirs, allowing the user to enjoy their trip with peace of mind.

[0068] A travel support system according to an embodiment includes a reception unit, an analysis unit, a suggestion unit, and a determination unit. The reception unit inputs a desired destination, a date, travel companion information, a budget, and restrictions and requests. For example, the reception unit allows a user to input a departure point, a destination, a travel date, travel companion information, a budget, and restrictions and requests. The analysis unit analyzes the information received by the reception unit and proposes a route taking into account traffic conditions and weather. For example, the analysis unit collects real-time traffic and weather information and calculates an optimal route. The suggestion unit suggests lunch, dinner, and souvenirs based on the route proposed by the analysis unit. For example, the suggestion unit suggests nearby restaurants and souvenir shops based on the user's arrival time at a tourist attraction. In this case, the suggestion unit makes suggestions taking into account the user's allergies and preferences. The determination unit determines transportation connections based on the information proposed by the suggestion unit. For example, the determination unit determines whether transportation from the current location to the next destination is on time. The suggestion unit suggests alternative options based on the information determined by the determination unit. For example, if the user cannot make the required transportation, the suggestion unit suggests an alternative means of transportation. This allows the travel support system according to the embodiment to suggest optimal routes, meals, and souvenirs based on the user's input information.

[0069] The suggestion unit can suggest lunches, dinners, and souvenirs based on the user's allergies and preferences. For example, the suggestion unit can suggest restaurants that do not contain ingredients to which the user is allergic. The suggestion unit can also suggest restaurants that offer allergy-friendly menus based on the user's allergy information. The suggestion unit can also customize meal suggestions taking the user's allergy information into consideration. For example, the suggestion unit can analyze the user's preferences and suggest optimal lunches, dinners, and souvenirs. This makes it possible to make suggestions based on the user's allergies and preferences.

[0070] The analysis unit can collect real-time traffic information and weather information and propose a route. For example, the analysis unit collects real-time traffic information from a traffic information service. The analysis unit can also collect real-time weather information from data from the Japan Meteorological Agency. The analysis unit can also collect real-time traffic information using sensor information. For example, the analysis unit proposes an optimal route based on traffic congestion information. The analysis unit can also propose a route that suits the weather based on a weather forecast. This makes it possible to propose an optimal route based on real-time information.

[0071] The determination unit can make a determination based on the arrival time of a transportation facility from the current location to the next destination. The determination unit calculates the arrival time based on, for example, the transportation facility's operation schedule. The determination unit can also calculate the arrival time using a prediction algorithm. The determination unit can also calculate the arrival time based on real-time traffic information. For example, the determination unit calculates the arrival time at the next destination based on the transportation facility's operation schedule. The determination unit can also predict the arrival time of the transportation facility using a prediction algorithm. This allows for smooth transfers between transportation facilities.

[0072] The suggestion unit can suggest an alternative means of transportation if the means of transportation is delayed for the arrival time. For example, the suggestion unit can suggest a taxi. The suggestion unit can also suggest a rental car. The suggestion unit can also suggest a bicycle. For example, the suggestion unit can suggest a taxi if the means of transportation is delayed. The suggestion unit can also suggest a rental car. This makes it possible to suggest an alternative means of transportation even if the means of transportation is not on time.

[0073] The suggestion unit can suggest nearby restaurants and souvenir shops based on the time the user will arrive at the tourist destination. For example, the suggestion unit can suggest nearby restaurants based on the time the user will arrive at the tourist destination. The suggestion unit can also suggest nearby souvenir shops based on the time the user will arrive at the tourist destination. The suggestion unit can also suggest nearby tourist spots based on the time the user will arrive at the tourist destination. For example, the suggestion unit can suggest restaurants based on the time the user will arrive at the tourist destination. The suggestion unit can also suggest souvenir shops. This makes it possible to make suggestions based on the time the user will arrive at the tourist destination.

[0074] The reception unit can analyze the user's emotions and adjust the display method of the input interface based on the analyzed user's emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick information input. For example, if the user is feeling stressed, the reception unit can provide a simple interface. Furthermore, if the user is relaxed, the reception unit can provide detailed input options. This makes it possible to display an interface according to the user's emotions.

[0075] The reception unit can analyze the user's past travel history and automatically complete input items. For example, the reception unit can automatically display candidates for places the user wants to go to based on places the user has visited in the past. The reception unit can also automatically complete information about companions that the user has previously input. The reception unit can also automatically set budgets and restrictions based on the user's past travel history. For example, the reception unit can display candidates for places the user wants to go to based on places the user has visited in the past. The reception unit can also complete information about companions that the user has previously input. This makes it possible to automatically complete input items based on the user's past travel history.

[0076] The reception unit can automatically acquire the user's current location information when inputting the information and set it as the departure point. For example, when the user opens the app, the reception unit automatically acquires the user's current location and sets it as the departure point. Furthermore, when the user inputs a destination, the reception unit can also suggest optimal candidate locations taking into account the distance from the current location. Furthermore, when the user uses the app while on the move, the reception unit can also update the current location in real time and reflect it as the departure point. For example, when the user opens the app, the reception unit acquires the user's current location and sets it as the departure point. Furthermore, when the user inputs a destination, the reception unit can also suggest candidate locations taking into account the distance from the current location. In this way, the current location information can be automatically acquired and set as the departure point.

[0077] The reception unit can customize input fields by reflecting the user's past feedback when inputting data. For example, the reception unit automatically customizes the input fields based on feedback provided by the user in the past. The reception unit can also improve the input fields by taking into account points of dissatisfaction the user has had in the past. The reception unit can also analyze the user's past feedback and suggest an optimal input method. For example, the reception unit customizes the input fields based on feedback provided by the user in the past. The reception unit can also improve the input fields by taking into account points of dissatisfaction the user has had in the past. In this way, the input fields can be customized based on past feedback.

[0078] The reception unit can analyze the user's emotions and determine the priority of input items based on the analyzed user's emotions. For example, when the user is feeling stressed, the reception unit can prioritize displaying important input items to enable quick input. Furthermore, when the user is relaxed, the reception unit can provide detailed input items and suggest a customizable input method. Furthermore, when the user is in a hurry, the reception unit can display the most important input items first to enable quick input. For example, when the user is feeling stressed, the reception unit can prioritize displaying important input items. Furthermore, when the user is relaxed, the reception unit can provide detailed input items. In this way, the priority of input items can be determined according to the user's emotions.

[0079] The reception unit can provide the optimal input method at the time of input, taking into consideration device information of the user. For example, if the user is using a smartphone, the reception unit can provide an interface optimized for touch input. Furthermore, if the user is using a tablet, the reception unit can also provide an input method optimized for a large screen. Furthermore, if the user is using a desktop, the reception unit can also provide an interface optimized for keyboard input. For example, if the user is using a smartphone, the reception unit can provide an interface optimized for touch input. Furthermore, if the user is using a tablet, the reception unit can also provide an input method optimized for a large screen. In this way, the optimal input method can be provided based on device information.

[0080] The reception unit can analyze the user's social media activity at the time of input and suggest related input items. For example, the reception unit can suggest potential places the user would like to visit based on places the user has checked in to on social media. The reception unit can also analyze the content of the user's social media posts and suggest related input items. The reception unit can also suggest related input items by referring to the activity of the user's friends on social media. For example, the reception unit can suggest potential places the user would like to visit based on places the user has checked in to on social media. The reception unit can also analyze the content of the user's social media posts and suggest related input items. In this way, related input items can be suggested based on social media activity.

[0081] The reception unit can customize input items by reflecting the user's past travel history when inputting information. For example, the reception unit automatically displays candidate destinations based on places the user has visited in the past. The reception unit can also automatically complete information about companions previously entered by the user. The reception unit can also automatically set budgets and restrictions based on the user's past travel history. For example, the reception unit displays candidate destinations based on places the user has visited in the past. The reception unit can also complete information about companions previously entered by the user. This allows the input items to be customized based on the user's past travel history.

[0082] The analysis unit can analyze the user's emotions and adjust the display method of the analysis results based on the analyzed user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. This makes it possible to display analysis results according to the user's emotions.

[0083] During analysis, the analysis unit can improve prediction accuracy by referring to past traffic information and weather data. The analysis unit can, for example, propose an optimal route based on past traffic congestion information. The analysis unit can also propose a route that suits the weather by referring to past weather data. The analysis unit can also propose a safe route based on past traffic accident information. For example, the analysis unit can propose an optimal route based on past traffic congestion information. The analysis unit can also propose a route that suits the weather by referring to past weather data. This makes it possible to improve prediction accuracy based on past data.

[0084] During analysis, the analysis unit can propose a route taking the user's travel history into consideration. For example, the analysis unit can propose an optimal route based on routes the user has used in the past. The analysis unit can also propose a route that avoids crowded areas based on the user's past travel history. The analysis unit can also analyze the user's past travel history and propose the most efficient route. For example, the analysis unit can propose an optimal route based on routes the user has used in the past. The analysis unit can also propose a route that avoids crowded areas based on the user's past travel history. This makes it possible to propose an optimal route based on travel history.

[0085] During analysis, the analysis unit can update the route in real time based on the user's current location information. For example, the analysis unit updates the user's current location in real time while the user is moving and proposes an optimal route. The analysis unit can also update the user's current location in real time as the user approaches the destination and propose an optimal route. If the user gets lost, the analysis unit can update the user's current location in real time and propose an optimal route again. For example, the analysis unit updates the user's current location in real time while the user is moving and proposes an optimal route. The analysis unit can also update the user's current location in real time as the user approaches the destination and propose an optimal route. This allows the route to be updated in real time based on the current location information.

[0086] The analysis unit can analyze the user's emotions and determine the priority of analysis results based on the analyzed user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize displaying important analysis results and quickly provide information. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results and suggest customizable information. Furthermore, if the user is in a hurry, the analysis unit can display the most important analysis results first and quickly provide information. For example, if the user is feeling stressed, the analysis unit can prioritize displaying important analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. In this way, the priority of analysis results can be determined according to the user's emotions.

[0087] During analysis, the analysis unit can provide an optimal analysis method taking into account device information of the user. For example, if the user is using a smartphone, the analysis unit can provide an analysis method that matches the screen size. Furthermore, if the user is using a tablet, the analysis unit can also provide an analysis method that is optimized for a large screen. Furthermore, if the user is using a desktop, the analysis unit can also provide an analysis method that is optimized for keyboard input. For example, if the user is using a smartphone, the analysis unit can provide an analysis method that matches the screen size. Furthermore, if the user is using a tablet, the analysis unit can also provide an analysis method that is optimized for a large screen. This makes it possible to provide an optimal analysis method based on device information.

[0088] During the analysis, the analysis unit can analyze the user's social media activity and suggest related routes. For example, the analysis unit can suggest an optimal route based on places where the user has checked in on social media. The analysis unit can also analyze the content of the user's social media posts and suggest related routes. The analysis unit can also suggest related routes by taking into account the activities of the user's friends on social media. For example, the analysis unit can suggest an optimal route based on places where the user has checked in on social media. The analysis unit can also analyze the content of the user's social media posts and suggest related routes. In this way, related routes can be suggested based on social media activity.

[0089] The analysis unit can customize the analysis method by reflecting the user's past feedback during analysis. For example, the analysis unit automatically customizes the analysis method based on feedback provided by the user in the past. The analysis unit can also improve the analysis method by taking into account points of dissatisfaction that the user has had in the past. The analysis unit can also analyze the user's past feedback and propose an optimal analysis method. For example, the analysis unit customizes the analysis method based on feedback provided by the user in the past. The analysis unit can also improve the analysis method by taking into account points of dissatisfaction that the user has had in the past. In this way, the analysis method can be customized based on past feedback.

[0090] The suggestion unit can analyze the user's emotions and adjust the way in which suggestions are expressed based on the analyzed user's emotions. For example, if the user is nervous, the suggestion unit can provide a simple and highly visible suggestion method. Furthermore, if the user is relaxed, the suggestion unit can also provide a suggestion method that includes detailed information. Furthermore, if the user is in a hurry, the suggestion unit can also provide a suggestion method that focuses on the main points. For example, if the user is nervous, the suggestion unit can provide a simple and highly visible suggestion method. Furthermore, if the user is relaxed, the suggestion unit can also provide a suggestion method that includes detailed information. This makes it possible to adjust the way in which suggestions are expressed according to the user's emotions.

[0091] When making a suggestion, the suggestion unit can make a meal suggestion taking into account the user's allergy information. For example, the suggestion unit can suggest restaurants that do not contain ingredients to which the user is allergic. The suggestion unit can also suggest restaurants that offer allergy-friendly menus based on the user's allergy information. The suggestion unit can also customize the meal suggestion taking into account the user's allergy information. For example, the suggestion unit can suggest restaurants that do not contain ingredients to which the user is allergic. The suggestion unit can also suggest restaurants that offer allergy-friendly menus based on the user's allergy information. This makes it possible to make meal suggestions based on allergy information.

[0092] When making a suggestion, the suggestion unit can analyze the user's preferences and suggest lunch, dinner, or souvenirs. For example, the suggestion unit can suggest the optimal lunch or dinner based on restaurants that the user has visited with preference in the past. The suggestion unit can also analyze the user's preferences and suggest the optimal souvenir. The suggestion unit can also suggest the optimal meal or souvenir based on the user's past selection history. For example, the suggestion unit can suggest the optimal lunch or dinner based on restaurants that the user has visited with preference in the past. The suggestion unit can also analyze the user's preferences and suggest the optimal souvenir. This makes it possible to make optimal suggestions based on the user's preferences.

[0093] When making suggestions, the suggestion unit can improve the accuracy of the suggestions by referring to the user's past travel history. For example, the suggestion unit can suggest optimal tourist spots based on places the user has visited in the past. The suggestion unit can also suggest favorite restaurants and souvenir shops from the user's past travel history. The suggestion unit can also analyze the user's past travel history to make the most appropriate suggestions. For example, the suggestion unit can suggest optimal tourist spots based on places the user has visited in the past. The suggestion unit can also suggest favorite restaurants and souvenir shops from the user's past travel history. This makes it possible to improve the accuracy of suggestions based on the past travel history.

[0094] The suggestion unit can analyze the user's emotions and determine the priority of suggestions based on the analyzed user's emotions. For example, when the user is feeling stressed, the suggestion unit can prioritize displaying important suggestions and quickly provide information. Furthermore, when the user is relaxed, the suggestion unit can provide detailed suggestions and suggest customizable information. Furthermore, when the user is in a hurry, the suggestion unit can display the most important suggestions first and quickly provide information. For example, when the user is feeling stressed, the suggestion unit can prioritize displaying important suggestions. Furthermore, when the user is relaxed, the suggestion unit can provide detailed suggestions. In this way, the priority of suggestions can be determined according to the user's emotions.

[0095] When making a suggestion, the suggestion unit can make a highly relevant suggestion taking into account the user's geographical location information. The suggestion unit, for example, suggests restaurants and tourist spots close to the user's current location. The suggestion unit can also suggest the optimal means of transportation based on the user's geographical location information. The suggestion unit can also make an optimal suggestion taking into account the distance from the user's current location. For example, the suggestion unit suggests restaurants and tourist spots close to the user's current location. The suggestion unit can also suggest the optimal means of transportation based on the user's geographical location information. This makes it possible to make a highly relevant suggestion based on the geographical location information.

[0096] When making a suggestion, the suggestion unit can analyze the user's social media activity and make a related suggestion. For example, the suggestion unit can make a related suggestion based on places where the user has checked in on social media. The suggestion unit can also analyze the content of the user's posts on social media and suggest related tourist spots and stores. The suggestion unit can also make a related suggestion by referring to the activities of the user's friends on social media. For example, the suggestion unit can make a related suggestion based on places where the user has checked in on social media. The suggestion unit can also analyze the content of the user's posts on social media and suggest related tourist spots and stores. In this way, it is possible to make a related suggestion based on social media activity.

[0097] The suggestion unit can customize the suggestion method by reflecting the user's past feedback when making a suggestion. For example, the suggestion unit automatically customizes the suggestion method based on feedback provided by the user in the past. The suggestion unit can also improve the suggestion method by taking into account points of dissatisfaction that the user has had in the past. The suggestion unit can also analyze the user's past feedback and suggest an optimal suggestion method. For example, the suggestion unit customizes the suggestion method based on feedback provided by the user in the past. The suggestion unit can also improve the suggestion method by taking into account points of dissatisfaction that the user has had in the past. In this way, the suggestion method can be customized based on past feedback.

[0098] The determination unit can analyze the user's emotions and adjust the display method of the determination result based on the analyzed user's emotions. For example, if the user is nervous, the determination unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the determination unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the determination unit can also provide a display method that focuses on the main points. For example, if the user is nervous, the determination unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the determination unit can also provide a display method including detailed information. This makes it possible to display the determination result according to the user's emotions.

[0099] When making a judgment, the judgment unit can improve the prediction accuracy of the next transportation means by referring to past traffic information. The judgment unit improves the prediction accuracy of the next transportation means, for example, based on past traffic congestion information. The judgment unit can also improve the prediction accuracy of the next transportation means by referring to past operation statuses of public transportation means. The judgment unit can also suggest safe transportation means based on past traffic accident information. For example, the judgment unit improves the prediction accuracy of the next transportation means based on past traffic congestion information. The judgment unit can also improve the prediction accuracy of the next transportation means by referring to past operation statuses of public transportation means. This makes it possible to improve the prediction accuracy of the next transportation means based on past traffic information.

[0100] When making a determination, the determination unit can make the determination in real time based on the user's current location information. For example, the determination unit updates the user's current location in real time while the user is moving and determines the next means of transportation. The determination unit can also update the user's current location in real time and determine the next means of transportation as the user approaches the destination. If the user gets lost, the determination unit can update the user's current location in real time and determine the optimal means of transportation again. For example, the determination unit updates the user's current location in real time while the user is moving and determines the next means of transportation. The determination unit can also update the user's current location in real time as the user approaches the destination and determine the next means of transportation. This allows for real-time determination based on the current location information.

[0101] When making a determination, the determination unit can improve the accuracy of the determination by referring to the user's past travel history. For example, the determination unit determines the next means of transportation based on the means of transportation used by the user in the past. The determination unit can also determine a means of transportation that avoids congestion based on the user's past travel history. The determination unit can also analyze the user's past travel history and determine the most efficient means of transportation. For example, the determination unit determines the next means of transportation based on the means of transportation used by the user in the past. The determination unit can also determine a means of transportation that avoids congestion based on the user's past travel history. This makes it possible to improve the accuracy of the determination based on the past travel history.

[0102] The determination unit can analyze the user's emotions and determine the priority of the determination results based on the analyzed user's emotions. For example, when the user is feeling stressed, the determination unit can prioritize displaying important determination results and quickly provide information. Furthermore, when the user is relaxed, the determination unit can provide detailed determination results and suggest customizable information. Furthermore, when the user is in a hurry, the determination unit can first display the most important determination results and quickly provide information. For example, when the user is feeling stressed, the determination unit can prioritize displaying important determination results. Furthermore, when the user is relaxed, the determination unit can provide detailed determination results. In this way, the priority of the determination results can be determined according to the user's emotions.

[0103] The determination unit can provide an optimal determination method by taking into consideration device information of the user when making a determination. For example, if the user is using a smartphone, the determination unit can provide a determination method that matches the screen size. Furthermore, if the user is using a tablet, the determination unit can provide a determination method that is optimized for a large screen. Furthermore, if the user is using a desktop, the determination unit can provide a determination method that is optimized for keyboard input. For example, if the user is using a smartphone, the determination unit can provide a determination method that matches the screen size. Furthermore, if the user is using a tablet, the determination unit can provide a determination method that is optimized for a large screen. This makes it possible to provide an optimal determination method based on device information.

[0104] When making a determination, the determination unit can analyze the user's social media activity and make a related determination. For example, the determination unit can determine the next means of transportation based on the location where the user checked in on social media. The determination unit can also analyze the content of the user's posts on social media to determine the related means of transportation. The determination unit can also refer to the activities of the user's friends on social media to determine the related means of transportation. For example, the determination unit can determine the next means of transportation based on the location where the user checked in on social media. The determination unit can also analyze the content of the user's posts on social media to determine the related means of transportation. In this way, a related determination can be made based on social media activity.

[0105] The determination unit can customize the determination method by reflecting the user's past feedback when making a determination. The determination unit automatically customizes the determination method, for example, based on feedback provided by the user in the past. The determination unit can also improve the determination method by taking into account points of dissatisfaction the user has had in the past. The determination unit can also analyze the user's past feedback and propose an optimal determination method. For example, the determination unit customizes the determination method based on feedback provided by the user in the past. The determination unit can also improve the determination method by taking into account points of dissatisfaction the user has had in the past. In this way, the determination method can be customized based on past feedback. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, suggestion unit, and determination unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and allows the user to input the desired destination, date, travel companion information, budget, and restrictions and requests. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and collects real-time traffic and weather information and calculates the optimal route. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and suggests nearby restaurants and souvenir shops based on the user's arrival time at a tourist spot. The determination unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and determines whether transportation from the current location to the next destination will arrive in time. === Hard Collateral 1-2 === Each of the above-described elements, including the reception unit, analysis unit, suggestion unit, and determination unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing the user to input the desired destination, date, travel companion information, budget, and restrictions and requests by voice. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and collects real-time traffic and weather information and calculates the optimal route. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and suggests nearby restaurants and souvenir shops based on the user's arrival time at a tourist spot. The determination unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and determines whether the user will be able to make it to their next destination by public transportation from their current location. === Hard Collateral 1-3 === Each of the above-described elements, including the reception unit, analysis unit, suggestion unit, and determination unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing the user to input the desired destination, date, travel companion information, budget, and restrictions and requests by voice. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and collects real-time traffic and weather information and calculates the optimal route. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and suggests nearby restaurants and souvenir shops based on the user's arrival time at a tourist spot. The determination unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and determines whether the user will be able to make it to their next destination in time for public transportation from their current location. === Hard Collateral 1-4 === Each of the above-described elements, including the reception unit, analysis unit, suggestion unit, and determination unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, allowing the user to vocally input the desired destination, date, travel companion information, budget, and restrictions and requests. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and collects real-time traffic and weather information and calculates the optimal route. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and suggests nearby restaurants and souvenir shops based on the user's arrival time at a tourist spot. The determination unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and determines whether the user will be able to make it to their next destination by public transportation from their current location.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The analysis unit can analyze travel trends based on the user's past travel history and suggest tourist spots and activities that the user prefers. For example, it can analyze data on tourist spots that the user has visited in the past and suggest new tourist spots with similar characteristics. It can also suggest similar activities based on data on activities the user has participated in in the past. It can also suggest the best time to travel by taking into account data on the seasons and weather conditions in which the user has visited in the past. This makes it possible to make more personalized suggestions based on the user's past travel history.

[0108] The suggestion unit can analyze the user's emotions and suggest refreshing spots during the trip based on the analyzed user's emotions. For example, if the user is feeling stressed, the suggestion unit can suggest relaxing cafes or parks. If the user is tired, the suggestion unit can suggest massage or spa facilities. Furthermore, if the user is excited, the suggestion unit can suggest active activities or events. This makes it possible to suggest refreshing spots according to the user's emotions.

[0109] The determination unit can monitor the user's current health condition and suggest an optimal travel plan based on the health condition. For example, it can monitor the user's heart rate and number of steps and suggest a route that avoids excessive exercise. If the user is tired, it can also suggest a route that includes many rest points. Furthermore, if the user has health problems, it can also suggest nearby medical facilities. This makes it possible to suggest a safe and comfortable travel plan that suits the user's health condition.

[0110] The suggestion unit can analyze the user's emotions and suggest entertainment for the trip based on the analyzed user emotions. For example, if the user is relaxed, a quiet movie or music event can be suggested. If the user is excited, a live concert or sporting event can be suggested. Furthermore, if the user is bored, an active activity or game can be suggested. This makes it possible to suggest entertainment according to the user's emotions.

[0111] The analysis unit can analyze the user's social media activity and make suggestions based on the places the user's friends have visited and the events they have attended. For example, it can suggest tourist spots and restaurants where the user's friends have checked in. It can also suggest events and activities that the user's friends have attended. It can also suggest related tourist spots and activities based on photos and reviews posted by the user's friends. This makes it possible to make suggestions that utilize the user's social network.

[0112] The suggestion unit can analyze the user's emotions and adjust the meal plan for the trip based on the analyzed user's emotions. For example, if the user is feeling stressed, it can suggest restaurants with a relaxing atmosphere. If the user is excited, it can also suggest lively restaurants or bars. Furthermore, if the user is tired, it can also suggest restaurants that serve nutritious meals. This makes it possible to suggest meal plans that correspond to the user's emotions.

[0113] The analysis unit can analyze the user's past feedback and improve the accuracy of suggestions based on the feedback. For example, it can prioritize suggestions of tourist spots and restaurants that the user has previously rated highly. It can also make suggestions that avoid places that the user has previously rated poorly. Furthermore, it can customize the suggestions based on the user's feedback to make suggestions that will provide greater satisfaction. This makes it possible to make more accurate suggestions based on the user's past feedback.

[0114] The suggestion unit can analyze the user's emotions and adjust activities during the trip based on the analyzed user's emotions. For example, if the user is relaxed, a quiet walk or a yoga class can be suggested. If the user is excited, adventure sports or a theme park can be suggested. Furthermore, if the user is bored, an interactive workshop or class can be suggested. This makes it possible to suggest activities according to the user's emotions.

[0115] The analysis unit can provide the optimal information display method based on the user's device information. For example, if the user is using a smartwatch, information can be provided in the form of a simple notification. If the user is using a smartphone, an interface containing detailed information can be provided. Furthermore, if the user is using a tablet, it is possible to provide an information display method optimized for a large screen. This makes it possible to display information optimally according to the user's device.

[0116] The suggestion unit can analyze the user's emotions and suggest rest points during the trip based on the analyzed user emotions. For example, if the user is feeling stressed, a quiet cafe or park can be suggested. Also, if the user is tired, a bench or rest area where the user can relax can be suggested. Furthermore, if the user wants to refresh themselves, it can suggest a scenic observation deck or a rest point in nature. This makes it possible to suggest rest points according to the user's emotions.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit inputs the desired destination, date, travel companion information, budget, restrictions and requests. For example, the user can input the departure and destination, travel date, travel companion information, budget, restrictions and requests. Step 2: The analysis unit analyzes the information received by the reception unit and proposes a route taking into account traffic conditions and weather. For example, it collects real-time traffic and weather information and calculates the optimal route. Step 3: The suggestion unit makes suggestions for lunch, dinner, and souvenirs based on the route proposed by the analysis unit. For example, it suggests nearby restaurants and souvenir shops based on the user's arrival time at a tourist spot. The suggestion takes into account the user's allergies and preferences. Step 4: The determination unit determines whether to transfer between transportation modes based on the information suggested by the suggestion unit, for example, whether the transportation mode from the current location to the next destination will arrive in time. Step 5: The suggestion unit suggests an alternative method based on the information determined by the determination unit. For example, if the transportation is not on time, an alternative transportation method is suggested.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 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.

[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 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.

[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 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.

[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 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.

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

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 desk where you can input the destination, date, travel companion information, budget, restrictions and requests. an analysis unit that analyzes the information received by the reception unit and proposes a route taking into consideration traffic conditions and weather; a suggestion unit that suggests lunch, dinner, and souvenirs based on the route suggested by the analysis unit; a determination unit that determines whether to transfer between transportation modes based on the information proposed by the proposal unit; a suggestion unit that suggests another method based on the information determined by the determination unit. A system characterized by:

2. The proposal unit Suggest lunch, dinner, and souvenirs based on the user's allergies and preferences 2. The system of claim 1.

3. The analysis unit Collect real-time traffic and weather information and suggest routes 2. The system of claim 1.

4. The determination unit Decide based on the arrival time of public transport from your current location to your next destination 2. The system of claim 1.

5. The proposal unit If your transportation is delayed, suggest an alternative means of transportation 2. The system of claim 1.

6. The proposal unit Suggests nearby restaurants and souvenir shops based on the user's arrival time at a tourist spot 2. The system of claim 1.

7. The reception unit Analyzes user emotions and adjusts the display method of the input interface based on the analyzed user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past travel history and auto-complete input fields 2. The system of claim 1.

9. The reception unit When entering information, the user's current location is automatically acquired and set as the starting point.

2. The system of claim 1.

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