System

Abstract

An object of a system according to an embodiment is to automatically generate a movement schedule in consideration of weather, a map, and route information at a travel destination and to notify a traveler of the movement schedule.SOLUTION: A system according to an embodiment includes a weather information acquisition unit, a map information acquisition unit, a route information acquisition unit, a movement schedule generation unit, and a notification unit. The weather information acquisition unit acquires weather information. The map information acquisition unit acquires map information. The route information acquisition unit acquires route information. The movement schedule generation unit generates a movement schedule by integrating information acquired by the weather information acquisition unit, the map information acquisition unit, and the route information acquisition unit. The notification unit notifies the traveler of the movement schedule generated by the movement schedule generation 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] Conventional technology has had the problem of making it difficult to efficiently generate travel schedules that take into account weather, maps, and route information at the travel destination.

[0005] The system according to the embodiment aims to automatically generate a travel schedule that takes into account the weather, maps, and route information at the travel destination and notify the traveler of the schedule. [Means for solving the problem]

[0006] The system according to the embodiment includes a weather information acquisition unit, a map information acquisition unit, a route information acquisition unit, a travel schedule generation unit, and a notification unit. The weather information acquisition unit acquires weather information. The map information acquisition unit acquires map information. The route information acquisition unit acquires route information. The travel schedule generation unit generates a travel schedule by integrating the information acquired by the weather information acquisition unit, map information acquisition unit, and route information acquisition unit. The notification unit notifies the traveler of the travel schedule generated by the travel schedule generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate a travel schedule that takes into account the weather, maps, and route information at the travel destination and notify the traveler of the schedule. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The travel assistance system according to an embodiment of the present invention is a system that assists travelers by automatically generating an optimal travel schedule taking into account local information, even when weather changes or unexpected changes in plans occur during a trip. This allows travelers to travel efficiently and enjoy their trip even in unfamiliar areas.

[0029] The travel assistance system according to the embodiment includes a weather information acquisition unit, a map information acquisition unit, a route information acquisition unit, a travel schedule generation unit, and a notification unit. The weather information acquisition unit acquires weather information. For example, the weather information acquisition unit acquires weather information such as temperature, precipitation, and wind speed in real time. The weather information acquisition unit can also analyze past weather data to predict weather patterns in a specific region. For example, if past data indicates a tendency for heavy rainfall in a specific month, the weather information acquisition unit can provide appropriate advice to travelers based on that information. The weather information acquisition unit can also analyze meteorological satellite data in real time to predict local weather changes. For example, the weather information acquisition unit analyzes cloud movement from satellite data to predict rainfall in a short period of time. The map information acquisition unit acquires map information. For example, the map information acquisition unit acquires map information such as geographic coordinates, road information, and tourist attraction information. The map information acquisition unit can also analyze past travel data of travelers to suggest the most efficient route. For example, the weather information acquisition unit can identify routes that avoid crowds based on past data and suggest them to travelers. Furthermore, the map information acquisition unit can acquire traffic congestion information in real time and propose optimal detour routes. For example, the unit identifies routes that avoid congested roads and proposes them to travelers. The route information acquisition unit acquires route information. For example, the route information acquisition unit acquires route information such as public transportation operation information and route information. The route information acquisition unit can also analyze past public transportation operation data and predict the risk of delays or service suspensions. For example, the unit can identify the frequency of delays on specific routes from past data and issue warnings to travelers. The route information acquisition unit can also acquire transportation operation status in real time and propose optimal transfer routes. For example, the unit can identify transfer routes that avoid routes experiencing delays and propose them to travelers. The travel schedule generation unit generates a travel schedule by integrating information acquired by the weather information acquisition unit, map information acquisition unit, and route information acquisition unit. For example, the travel schedule generation unit proposes a schedule that prioritizes indoor tourist spots when the weather worsens. The travel schedule generation unit can also generate a travel schedule with ample time to spare, taking into account public transportation delay information.Furthermore, the travel schedule generation unit can propose a schedule that includes appropriate rest periods, taking into account the traveler's physical condition and fatigue level. For example, the unit can propose a schedule that includes rest periods after a long trip. The notification unit notifies the traveler of the travel schedule generated by the travel schedule generation unit. For example, the notification unit updates the schedule in real time via a smartphone app and notifies the traveler. Furthermore, if the traveler gets lost, the notification unit can propose an optimal route and provide navigation. Furthermore, the notification unit can provide customized notifications based on the traveler's language and culture. For example, the notification unit can send notifications in the traveler's native language. This allows the travel assistance system according to the embodiment to travel efficiently and enjoy a trip, even in unfamiliar places. For example, the schedule can be flexibly changed in response to changes in weather, and travel can be made with ample time to spare, taking into account delay information on public transportation. Furthermore, even if the traveler gets lost, the notification unit can propose an optimal route and provide navigation, allowing the traveler to enjoy their trip with peace of mind.

[0030] The weather information acquisition unit can analyze weather data and predict local weather patterns. For example, the generation AI analyzes weather data from the past 10 years and predicts seasonal weather patterns for a specific region. For example, if past data shows that there is a tendency for rain to be heavy in a specific month, the unit can provide travelers with appropriate advice based on that information. This makes it possible to predict weather patterns for a specific region and provide travelers with appropriate advice.

[0031] The weather information acquisition unit can analyze meteorological satellite data in real time and predict local weather changes. For example, the weather information acquisition unit uses a generation AI to analyze meteorological satellite data in real time and predict local weather changes in a specific area. For example, it analyzes cloud movement from satellite data and makes a short-term rain forecast. This allows for the prediction of local weather changes and provides travelers with a quick response.

[0032] The weather information acquisition unit can select weather conditions taking into account the traveler's health condition. For example, the generation AI in the weather information acquisition unit selects weather conditions suitable for allergies and chronic illnesses taking into account the traveler's health condition. For example, for a traveler with hay fever, it would suggest outdoor activities on days with low pollen levels. This ensures the safety and comfort of travelers by selecting weather conditions that suit the traveler's health condition.

[0033] The map information acquisition unit can analyze traveler movement data and suggest routes. For example, the generation AI in the map information acquisition unit analyzes past traveler movement data and suggests the most efficient route. For example, it identifies routes that avoid congestion from past data and suggests them to travelers. In this way, by analyzing past movement data, it is possible to suggest the most efficient route.

[0034] The map information acquisition unit can acquire traffic congestion information in real time and suggest detour routes. For example, the generation AI in the map information acquisition unit acquires traffic congestion information in real time and suggests the optimal detour route. For example, it identifies a route that avoids roads with congestion and suggests it to the traveler. This makes it possible to acquire traffic congestion information in real time and suggest the optimal detour route.

[0035] The route information acquisition unit can analyze public transport operation data and predict the risk of delays and cancellations. For example, the generation AI in the route information acquisition unit analyzes past public transport operation data and predicts the risk of delays and cancellations. For example, it identifies the frequency of delays on specific routes from past data and issues a warning to travelers. This makes it possible to provide travelers with appropriate advice by predicting the risk of delays and cancellations.

[0036] The route information acquisition unit can acquire the operation status of public transport in real time and propose transfer routes. For example, the generation AI acquires the operation status of public transport in real time and proposes the optimal transfer route. For example, it identifies transfer routes that avoid routes experiencing delays and proposes them to travelers. This makes it possible to acquire the operation status of public transport in real time and propose the optimal transfer route.

[0037] The travel schedule generation unit can analyze the traveler's schedule data and propose a schedule. For example, the generation AI analyzes the traveler's past schedule data and proposes the most efficient schedule. For example, it identifies time periods to avoid congestion from past data and proposes them to the traveler. In this way, by analyzing past schedule data, the most efficient schedule can be proposed.

[0038] The travel schedule generation unit can acquire the traveler's location information in real time and propose a schedule. For example, the generation AI acquires the traveler's location information in real time and proposes an optimal schedule. For example, it proposes a schedule in which the traveler prioritizes visiting the tourist spots closest to the traveler's current location. This makes it possible to acquire location information in real time and propose an optimal schedule.

[0039] The notification unit can analyze notification data and suggest notification timing. For example, the generation AI analyzes past notification data and suggests the most effective notification timing. For example, it identifies the time of day when travelers are most likely to receive notifications and sends notifications at that time. In this way, by analyzing past notification data, it is possible to suggest the most effective notification timing.

[0040] The notification unit can obtain the traveler's location information in real time and suggest an assistance method. For example, the generation AI can obtain the traveler's location information in real time and suggest the optimal assistance method. For example, if a traveler gets lost, it can guide them to the nearest landmark. This makes it possible to obtain location information in real time and suggest the optimal assistance method.

[0041] The notification unit can provide a customized notification according to the language and culture of the traveler. For example, the generation AI provides a customized notification according to the language and culture of the traveler. For example, the notification unit sends a notification in the traveler's native language. This makes it possible to provide a customized notification according to the language and culture of the traveler.

[0042] The notification unit can suggest an assistance method that suits the traveler's device. For example, the generation AI in the notification unit suggests the optimal assistance method that suits the traveler's device. For example, for travelers who use smartphones, detailed map information can be provided through an app. This makes it possible to suggest the optimal assistance method that suits the traveler's device.

[0043] The travel schedule generation unit can propose a schedule that includes rest periods, taking into account the traveler's physical condition and fatigue level. For example, the generation AI of the travel schedule generation unit proposes a schedule that includes appropriate rest periods, taking into account the traveler's physical condition and fatigue level. For example, it proposes a schedule that allows for rest periods after a long period of travel. In this way, the health and comfort of the traveler can be ensured by proposing a schedule that takes into account the traveler's physical condition and fatigue level.

[0044] The travel schedule generation unit can propose a schedule that includes restaurants, taking into account the traveler's food preferences and allergy information. For example, the generation AI of the travel schedule generation unit proposes a schedule that includes optimal restaurants, taking into account the traveler's food preferences and allergy information. For example, it proposes restaurants that offer vegetarian menus for vegetarian travelers. In this way, by proposing a schedule that takes into account the traveler's food preferences and allergy information, it is possible to increase the traveler's satisfaction with their meals.

[0045] The map information acquisition unit can propose a customized sightseeing route based on the traveler's interests and hobbies. For example, the generation AI of the map information acquisition unit proposes a customized sightseeing route based on the traveler's interests and hobbies. For example, a route that goes around historical sites is proposed to a traveler who loves history. This makes it possible to propose a customized sightseeing route based on the traveler's interests and hobbies.

[0046] The map information acquisition unit can propose a route according to the traveler's means of transportation. For example, the generation AI of the map information acquisition unit proposes the optimal route according to the traveler's means of transportation. For example, it proposes an easy-to-walk route for a traveler traveling on foot. This makes it possible to propose the optimal route according to the traveler's means of transportation.

[0047] The route information acquisition unit can suggest transportation methods taking into account the traveler's travel time and budget. For example, the generation AI can suggest the optimal transportation method taking into account the traveler's travel time and budget. For example, it can suggest whether to choose a taxi, which is a short trip but expensive, or a bus, which takes longer but is cheaper. This makes it possible to suggest the optimal transportation method taking into account the traveler's travel time and budget.

[0048] The route information acquisition unit can suggest transportation methods taking into account the amount and type of luggage a traveler has. For example, the generation AI in the route information acquisition unit can suggest the optimal transportation method taking into account the amount and type of luggage a traveler has. For example, a traveler with a large suitcase can be suggested a taxi where luggage can be stored. This makes it possible to suggest the optimal transportation method taking into account the amount and type of luggage a traveler has.

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

[0050] The travel assistance system can further include a health management unit that monitors the traveler's health condition. For example, the health management unit can monitor the traveler's heart rate and blood pressure in real time, and if abnormalities are detected, can recommend appropriate rest breaks or refer the traveler to a medical institution. The health management unit can also record the traveler's food and water intake and suggest appropriate times for eating and hydration. Furthermore, the health management unit can analyze the traveler's sleep patterns and suggest a schedule to ensure adequate sleep. This allows the traveler to comprehensively manage their health condition and enjoy their trip with peace of mind.

[0051] The travel assistance system can further include a language and culture adaptation unit that provides customized notifications according to the traveler's language and culture. For example, the language and culture adaptation unit can send notifications in the traveler's native language and provide information in a format that the traveler can easily understand. The language and culture adaptation unit can also provide appropriate advice and warnings taking into account the traveler's cultural background. Furthermore, the language and culture adaptation unit can provide information about the culture and customs of the region the traveler is visiting, making it easier for the traveler to adapt to the local culture. This allows the traveler to enjoy their trip with peace of mind without being confused by language or cultural differences.

[0052] The travel assistance system can further include a transportation means adaptation unit that proposes a route according to the traveler's transportation means. For example, if the traveler is traveling on foot, the transportation means adaptation unit can propose an easy-to-walk route. The transportation means adaptation unit can also propose a route that includes a bicycle-only road for a traveler who uses a bicycle. Furthermore, the transportation means adaptation unit can propose a barrier-free route for a traveler who uses a wheelchair, allowing the traveler to travel comfortably. This makes it possible to propose the optimal route according to the traveler's transportation means, thereby increasing the convenience of the trip.

[0053] The travel assistance system can further include a dietary management unit that considers the traveler's dietary preferences and allergy information and proposes a schedule that includes restaurants. For example, the dietary management unit can propose appropriate restaurants based on the traveler's dietary preferences and allergy information. In addition, if the traveler needs to avoid certain ingredients, the dietary management unit can also propose menus based on that information. Furthermore, if the traveler desires a healthy diet, the dietary management unit can propose restaurants that offer nutritionally balanced menus. This makes it possible to propose a schedule that takes into account the traveler's dietary preferences and allergy information, thereby increasing the traveler's satisfaction with their meals.

[0054] The travel assistance system can further include a luggage management unit that considers the amount and type of luggage of the traveler and suggests a means of transportation. For example, if the traveler has a large suitcase, the luggage management unit can suggest a taxi where the luggage can be stored. In addition, if the traveler is traveling light, the luggage management unit can also suggest a route using public transportation. Furthermore, if the traveler needs to carry specific luggage, the luggage management unit can suggest a means of transportation suitable for that luggage. This makes it possible to suggest the optimal means of transportation taking into account the amount and type of luggage of the traveler, thereby increasing the convenience of the trip.

[0055] The travel assistance system can further include a budget management unit that considers the travel time and budget of the traveler and proposes transportation methods. For example, the budget management unit can propose the optimal transportation method based on the traveler's travel time and budget. The budget management unit can also propose an expensive taxi if the traveler wants to travel quickly. Furthermore, the budget management unit can propose a slower but cheaper bus if the traveler wants to save on budget. This makes it possible to propose the optimal transportation method taking the traveler's travel time and budget into consideration, thereby increasing the convenience of travel.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The weather information acquisition unit acquires weather information. For example, it can acquire weather information such as temperature, precipitation, and wind speed in real time, and can also analyze past weather data to predict weather patterns in specific areas. It can also analyze meteorological satellite data in real time to predict local weather changes. Step 2: The map information acquisition unit acquires map information. For example, it acquires map information such as geographic coordinates, road information, and tourist destination information, and can analyze past travel data of travelers to propose the most efficient route. It also acquires traffic congestion information in real time and proposes the optimal detour route. Step 3: The route information acquisition unit acquires route information. For example, it acquires route information such as public transport operation information and route information, and can analyze past public transport operation data to predict the risk of delays and cancellations. It also acquires the operation status of public transport in real time and suggests optimal transfer routes. Step 4: The travel schedule generation unit generates a travel schedule by integrating the information acquired by the weather information acquisition unit, map information acquisition unit, and route information acquisition unit. For example, if the weather worsens, it proposes a schedule that prioritizes indoor tourist spots and generates a travel schedule with ample time to spare, taking into account information about delays in public transportation. It also proposes a schedule that includes appropriate rest periods, taking into account the traveler's physical condition and fatigue level. Step 5: The notification unit notifies the traveler of the travel schedule generated by the travel schedule generation unit. For example, the schedule can be updated in real time via a smartphone app and notified to the traveler. Furthermore, if the traveler becomes lost, the system can suggest the best route and provide navigation. It also provides notifications customized to the traveler's language and culture.

[0058] (Example 2) The travel assistance system according to an embodiment of the present invention is a system that assists travelers by automatically generating an optimal travel schedule taking into account local information, even when weather changes or unexpected changes in plans occur during a trip. This allows travelers to travel efficiently and enjoy their trip even in unfamiliar areas.

[0059] The travel assistance system according to the embodiment includes a weather information acquisition unit, a map information acquisition unit, a route information acquisition unit, a travel schedule generation unit, and a notification unit. The weather information acquisition unit acquires weather information. For example, the weather information acquisition unit acquires weather information such as temperature, precipitation, and wind speed in real time. The weather information acquisition unit can also analyze past weather data to predict weather patterns in a specific region. For example, if past data indicates a tendency for heavy rainfall in a specific month, the weather information acquisition unit can provide appropriate advice to travelers based on that information. The weather information acquisition unit can also analyze meteorological satellite data in real time to predict local weather changes. For example, the weather information acquisition unit analyzes cloud movement from satellite data to predict rainfall in a short period of time. The map information acquisition unit acquires map information. For example, the map information acquisition unit acquires map information such as geographic coordinates, road information, and tourist attraction information. The map information acquisition unit can also analyze past travel data of travelers to suggest the most efficient route. For example, the weather information acquisition unit can identify routes that avoid crowds based on past data and suggest them to travelers. Furthermore, the map information acquisition unit can acquire traffic congestion information in real time and propose optimal detour routes. For example, the unit identifies routes that avoid congested roads and proposes them to travelers. The route information acquisition unit acquires route information. For example, the route information acquisition unit acquires route information such as public transportation operation information and route information. The route information acquisition unit can also analyze past public transportation operation data and predict the risk of delays or service suspensions. For example, the unit can identify the frequency of delays on specific routes from past data and issue warnings to travelers. The route information acquisition unit can also acquire transportation operation status in real time and propose optimal transfer routes. For example, the unit can identify transfer routes that avoid routes experiencing delays and propose them to travelers. The travel schedule generation unit generates a travel schedule by integrating information acquired by the weather information acquisition unit, map information acquisition unit, and route information acquisition unit. For example, the travel schedule generation unit proposes a schedule that prioritizes indoor tourist spots when the weather worsens. The travel schedule generation unit can also generate a travel schedule with ample time to spare, taking into account public transportation delay information.Furthermore, the travel schedule generation unit can propose a schedule that includes appropriate rest periods, taking into account the traveler's physical condition and fatigue level. For example, the unit can propose a schedule that includes rest periods after a long trip. The notification unit notifies the traveler of the travel schedule generated by the travel schedule generation unit. For example, the notification unit updates the schedule in real time via a smartphone app and notifies the traveler. Furthermore, if the traveler gets lost, the notification unit can propose an optimal route and provide navigation. Furthermore, the notification unit can provide customized notifications based on the traveler's language and culture. For example, the notification unit can send notifications in the traveler's native language. This allows the travel assistance system according to the embodiment to travel efficiently and enjoy a trip, even in unfamiliar places. For example, the schedule can be flexibly changed in response to changes in weather, and travel can be made with ample time to spare, taking into account delay information on public transportation. Furthermore, even if the traveler gets lost, the notification unit can propose an optimal route and provide navigation, allowing the traveler to enjoy their trip with peace of mind.

[0060] The weather information acquisition unit can analyze weather data and predict local weather patterns. For example, the generation AI analyzes weather data from the past 10 years and predicts seasonal weather patterns for a specific region. For example, if past data shows that there is a tendency for rain to be heavy in a specific month, the unit can provide travelers with appropriate advice based on that information. This makes it possible to predict weather patterns for a specific region and provide travelers with appropriate advice.

[0061] The weather information acquisition unit can analyze meteorological satellite data in real time and predict local weather changes. For example, the weather information acquisition unit uses a generation AI to analyze meteorological satellite data in real time and predict local weather changes in a specific area. For example, it analyzes cloud movement from satellite data and makes a short-term rain forecast. This allows for the prediction of local weather changes and provides travelers with a quick response.

[0062] The weather information acquisition unit can select weather conditions taking into account the traveler's health condition. For example, the generation AI in the weather information acquisition unit selects weather conditions suitable for allergies and chronic illnesses taking into account the traveler's health condition. For example, for a traveler with hay fever, it would suggest outdoor activities on days with low pollen levels. This ensures the safety and comfort of travelers by selecting weather conditions that suit the traveler's health condition.

[0063] The map information acquisition unit can analyze traveler movement data and suggest routes. For example, the generation AI in the map information acquisition unit analyzes past traveler movement data and suggests the most efficient route. For example, it identifies routes that avoid congestion from past data and suggests them to travelers. In this way, by analyzing past movement data, it is possible to suggest the most efficient route.

[0064] The map information acquisition unit can acquire traffic congestion information in real time and suggest detour routes. For example, the generation AI in the map information acquisition unit acquires traffic congestion information in real time and suggests the optimal detour route. For example, it identifies a route that avoids roads with congestion and suggests it to the traveler. This makes it possible to acquire traffic congestion information in real time and suggest the optimal detour route.

[0065] The route information acquisition unit can analyze public transport operation data and predict the risk of delays and cancellations. For example, the generation AI in the route information acquisition unit analyzes past public transport operation data and predicts the risk of delays and cancellations. For example, it identifies the frequency of delays on specific routes from past data and issues a warning to travelers. This makes it possible to provide travelers with appropriate advice by predicting the risk of delays and cancellations.

[0066] The route information acquisition unit can acquire the operation status of public transport in real time and propose transfer routes. For example, the generation AI acquires the operation status of public transport in real time and proposes the optimal transfer route. For example, it identifies transfer routes that avoid routes experiencing delays and proposes them to travelers. This makes it possible to acquire the operation status of public transport in real time and propose the optimal transfer route.

[0067] The travel schedule generation unit can analyze the traveler's schedule data and propose a schedule. For example, the generation AI analyzes the traveler's past schedule data and proposes the most efficient schedule. For example, it identifies time periods to avoid congestion from past data and proposes them to the traveler. In this way, by analyzing past schedule data, the most efficient schedule can be proposed.

[0068] The travel schedule generation unit can acquire the traveler's location information in real time and propose a schedule. For example, the generation AI acquires the traveler's location information in real time and proposes an optimal schedule. For example, it proposes a schedule in which the traveler prioritizes visiting the tourist spots closest to the traveler's current location. This makes it possible to acquire location information in real time and propose an optimal schedule.

[0069] The notification unit can analyze notification data and suggest notification timing. For example, the generation AI analyzes past notification data and suggests the most effective notification timing. For example, it identifies the time of day when travelers are most likely to receive notifications and sends notifications at that time. In this way, by analyzing past notification data, it is possible to suggest the most effective notification timing.

[0070] The notification unit can obtain the traveler's location information in real time and suggest an assistance method. For example, the generation AI can obtain the traveler's location information in real time and suggest the optimal assistance method. For example, if a traveler gets lost, it can guide them to the nearest landmark. This makes it possible to obtain location information in real time and suggest the optimal assistance method.

[0071] The notification unit can provide a customized notification according to the language and culture of the traveler. For example, the generation AI provides a customized notification according to the language and culture of the traveler. For example, the notification unit sends a notification in the traveler's native language. This makes it possible to provide a customized notification according to the language and culture of the traveler.

[0072] The notification unit can suggest an assistance method that suits the traveler's device. For example, the generation AI in the notification unit suggests the optimal assistance method that suits the traveler's device. For example, for travelers who use smartphones, detailed map information can be provided through an app. This makes it possible to suggest the optimal assistance method that suits the traveler's device.

[0073] The notification unit uses the emotion estimation function to analyze the emotions that travelers have toward notifications and assistance, and can provide notifications and assistance that are emotionally satisfying. For example, the generation AI in the notification unit uses the emotion estimation function to analyze the emotions that travelers have toward notifications and assistance, and can provide notifications and assistance that are emotionally satisfying. For example, if a traveler is feeling stressed, the generation AI can suggest a relaxing activity. In this way, by using the emotion estimation function, notifications and assistance that are emotionally satisfying can be provided to travelers.

[0074] The travel schedule generation unit can propose a schedule that includes rest periods, taking into account the traveler's physical condition and fatigue level. For example, the generation AI of the travel schedule generation unit proposes a schedule that includes appropriate rest periods, taking into account the traveler's physical condition and fatigue level. For example, it proposes a schedule that allows for rest periods after a long period of travel. In this way, the health and comfort of the traveler can be ensured by proposing a schedule that takes into account the traveler's physical condition and fatigue level.

[0075] The travel schedule generation unit can propose a schedule that includes restaurants, taking into account the traveler's food preferences and allergy information. For example, the generation AI of the travel schedule generation unit proposes a schedule that includes optimal restaurants, taking into account the traveler's food preferences and allergy information. For example, it proposes restaurants that offer vegetarian menus for vegetarian travelers. In this way, by proposing a schedule that takes into account the traveler's food preferences and allergy information, it is possible to increase the traveler's satisfaction with their meals.

[0076] The travel schedule generation unit can use the emotion estimation function to analyze the emotions that travelers have about the schedule and propose a schedule that includes activities that will elicit positive emotions. For example, the generation AI can use the emotion estimation function to analyze the emotions that travelers have about the schedule and propose a schedule that includes activities that will elicit positive emotions. For example, the travel schedule generation unit can propose a schedule that prioritizes activities that travelers are looking forward to. In this way, by using the emotion estimation function, it is possible to propose a schedule that includes activities that will elicit positive emotions from travelers.

[0077] The map information acquisition unit can propose a customized sightseeing route based on the traveler's interests and hobbies. For example, the generation AI of the map information acquisition unit proposes a customized sightseeing route based on the traveler's interests and hobbies. For example, a route that goes around historical sites is proposed to a traveler who loves history. This makes it possible to propose a customized sightseeing route based on the traveler's interests and hobbies.

[0078] The map information acquisition unit can propose a route according to the traveler's means of transportation. For example, the generation AI of the map information acquisition unit proposes the optimal route according to the traveler's means of transportation. For example, it proposes an easy-to-walk route for a traveler traveling on foot. This makes it possible to propose the optimal route according to the traveler's means of transportation.

[0079] The route information acquisition unit can suggest transportation methods taking into account the traveler's travel time and budget. For example, the generation AI can suggest the optimal transportation method taking into account the traveler's travel time and budget. For example, it can suggest whether to choose a taxi, which is a short trip but expensive, or a bus, which takes longer but is cheaper. This makes it possible to suggest the optimal transportation method taking into account the traveler's travel time and budget.

[0080] The route information acquisition unit can suggest transportation methods taking into account the amount and type of luggage a traveler has. For example, the generation AI in the route information acquisition unit can suggest the optimal transportation method taking into account the amount and type of luggage a traveler has. For example, a traveler with a large suitcase can be suggested a taxi where luggage can be stored. This makes it possible to suggest the optimal transportation method taking into account the amount and type of luggage a traveler has.

[0081] The route information acquisition unit can use the emotion estimation function to analyze the emotions that travelers have toward public transportation and suggest transportation methods that will elicit positive emotions. For example, the generation AI in the route information acquisition unit can use the emotion estimation function to analyze the emotions that travelers have toward public transportation and suggest transportation methods that will elicit positive emotions. For example, if a traveler prioritizes comfort, taxis or ride-sharing can be suggested. In this way, by using the emotion estimation function, transportation methods that will elicit positive emotions can be suggested to travelers.

[0082] The map information acquisition unit can use the emotion estimation function to analyze the emotions that travelers have toward places they want to visit and suggest tourist routes that will elicit positive emotions. For example, the generation AI in the map information acquisition unit can use the emotion estimation function to analyze the emotions that travelers have toward places they want to visit and suggest tourist routes that will elicit positive emotions. For example, it can suggest a route that prioritizes visiting places that travelers are looking forward to. In this way, the emotion estimation function can be used to suggest tourist routes that will elicit positive emotions in travelers.

[0083] The travel schedule generation unit uses the emotion estimation function to analyze the emotions that travelers have about the schedule and can propose a schedule that is emotionally satisfying. For example, the generation AI uses the emotion estimation function to analyze the emotions that travelers have about the schedule and propose a schedule that is emotionally satisfying. For example, it proposes a schedule that prioritizes activities that travelers are looking forward to. In this way, by using the emotion estimation function, it is possible to propose a schedule that is emotionally satisfying to travelers.

[0084] The weather information acquisition unit uses the emotion estimation function to analyze the emotions that travelers have about the weather and can propose a schedule that prioritizes weather conditions that will allow the travelers to have a comfortable stay. For example, the generation AI uses the emotion estimation function to analyze the emotions that travelers have about the weather and proposes a schedule that prioritizes weather conditions that will allow the travelers to have a comfortable stay. For example, if a traveler dislikes rain, they will prioritize outdoor activities on sunny days. In this way, by using the emotion estimation function, a schedule that prioritizes comfortable weather conditions can be proposed to the travelers.

[0085] The weather information acquisition unit can use the emotion estimation function to analyze the emotions that travelers have about the weather and suggest activities that will elicit positive emotions. For example, the generation AI in the weather information acquisition unit can use the emotion estimation function to analyze the emotions that travelers have about the weather and suggest activities that will elicit positive emotions. For example, on sunny days, a picnic or hike can be suggested. In this way, the emotion estimation function can be used to suggest activities that will elicit positive emotions in travelers.

[0086] The map information acquisition unit uses the emotion estimation function to analyze the emotions that travelers have about places they want to visit and can suggest routes that will provide high emotional satisfaction. For example, the generation AI in the map information acquisition unit uses the emotion estimation function to analyze the emotions that travelers have about places they want to visit and can suggest routes that will provide high emotional satisfaction. For example, it can suggest a route that prioritizes visiting tourist spots that travelers are looking forward to. In this way, by using the emotion estimation function, it is possible to suggest routes that will provide high emotional satisfaction to travelers.

[0087] The route information acquisition unit can use the emotion estimation function to analyze the emotions that travelers have toward public transportation and propose routes that will provide a comfortable journey. For example, the generation AI in the route information acquisition unit uses the emotion estimation function to analyze the emotions that travelers have toward public transportation and propose routes that will provide a comfortable journey. For example, if a traveler dislikes crowded areas, a route during less crowded times can be proposed. In this way, by using the emotion estimation function, a route that will provide a comfortable journey for the traveler can be proposed.

[0088] The notification unit can use the emotion estimation function to analyze the emotions that travelers have toward notifications and assistance, and provide notifications and assistance that elicit positive emotions. For example, the generation AI in the notification unit can use the emotion estimation function to analyze the emotions that travelers have toward notifications and assistance, and provide notifications and assistance that are emotionally satisfying. For example, if a traveler is feeling stressed, the system can suggest a relaxing activity. In this way, the emotion estimation function can be used to provide notifications and assistance that elicit positive emotions from travelers.

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

[0090] The travel assistance system can further include a health management unit that monitors the traveler's health condition. For example, the health management unit can monitor the traveler's heart rate and blood pressure in real time, and if abnormalities are detected, can recommend appropriate rest breaks or refer the traveler to a medical institution. The health management unit can also record the traveler's food and water intake and suggest appropriate times for eating and hydration. Furthermore, the health management unit can analyze the traveler's sleep patterns and suggest a schedule to ensure adequate sleep. This allows the traveler to comprehensively manage their health condition and enjoy their trip with peace of mind.

[0091] The travel assistance system can further include an interest analysis unit that proposes a customized sightseeing route based on the traveler's interests and hobbies. For example, the interest analysis unit can analyze the traveler's past travel history and social media posts to identify tourist spots that interest the traveler. The interest analysis unit can also analyze the traveler's feelings about places they want to visit and propose a sightseeing route that will provide high emotional satisfaction. Furthermore, the interest analysis unit can propose activities that match the traveler's hobbies, maximizing the enjoyment of the trip. This makes it possible to provide a customized sightseeing route based on the traveler's interests and hobbies, thereby increasing the travel satisfaction.

[0092] The travel assistance system can further include a language and culture adaptation unit that provides customized notifications according to the traveler's language and culture. For example, the language and culture adaptation unit can send notifications in the traveler's native language and provide information in a format that the traveler can easily understand. The language and culture adaptation unit can also provide appropriate advice and warnings taking into account the traveler's cultural background. Furthermore, the language and culture adaptation unit can provide information about the culture and customs of the region the traveler is visiting, making it easier for the traveler to adapt to the local culture. This allows the traveler to enjoy their trip with peace of mind without being confused by language or cultural differences.

[0093] The travel assistance system can further include an emotion analysis unit that estimates the traveler's emotions and suggests activities based on those emotions. For example, the emotion analysis unit can estimate the traveler's emotions from their facial expressions and voice and suggest activities that will help the traveler relax. If the traveler is feeling stressed, the emotion analysis unit can also suggest places and activities that will help the traveler relax. Furthermore, if the traveler is having fun, the emotion analysis unit can suggest even more enjoyable activities, thereby increasing the travel satisfaction. In this way, activities can be suggested based on the traveler's emotions, maximizing the enjoyment of the trip.

[0094] The travel assistance system can further include a transportation means adaptation unit that proposes a route according to the traveler's transportation means. For example, if the traveler is traveling on foot, the transportation means adaptation unit can propose an easy-to-walk route. The transportation means adaptation unit can also propose a route that includes a bicycle-only road for a traveler who uses a bicycle. Furthermore, the transportation means adaptation unit can propose a barrier-free route for a traveler who uses a wheelchair, allowing the traveler to travel comfortably. This makes it possible to propose the optimal route according to the traveler's transportation means, thereby increasing the convenience of the trip.

[0095] The travel assistance system can further include a dietary management unit that considers the traveler's dietary preferences and allergy information and proposes a schedule that includes restaurants. For example, the dietary management unit can propose appropriate restaurants based on the traveler's dietary preferences and allergy information. In addition, if the traveler needs to avoid certain ingredients, the dietary management unit can also propose menus based on that information. Furthermore, if the traveler desires a healthy diet, the dietary management unit can propose restaurants that offer nutritionally balanced menus. This makes it possible to propose a schedule that takes into account the traveler's dietary preferences and allergy information, thereby increasing the traveler's satisfaction with their meals.

[0096] The travel assistance system may further include an emotion notification unit that estimates the emotion of the traveler and provides a notification based on the emotion. For example, the emotion notification unit may analyze the emotion of the traveler and send a notification that helps the traveler relax. If the traveler is feeling stressed, the emotion notification unit may also send a notification suggesting a relaxing activity. If the traveler is having fun, the emotion notification unit may send a notification suggesting an even more enjoyable activity, thereby increasing the travel satisfaction. In this way, the traveler's enjoyment can be maximized by providing a notification based on the traveler's emotion.

[0097] The travel assistance system can further include a luggage management unit that considers the amount and type of luggage of the traveler and suggests a means of transportation. For example, if the traveler has a large suitcase, the luggage management unit can suggest a taxi where the luggage can be stored. In addition, if the traveler is traveling light, the luggage management unit can also suggest a route using public transportation. Furthermore, if the traveler needs to carry specific luggage, the luggage management unit can suggest a means of transportation suitable for that luggage. This makes it possible to suggest the optimal means of transportation taking into account the amount and type of luggage of the traveler, thereby increasing the convenience of the trip.

[0098] The travel assistance system can further include an emotion scheduler that estimates the traveler's emotions and proposes a schedule based on the emotions. For example, the emotion scheduler can analyze the traveler's emotions and propose a schedule that allows the traveler to relax. If the traveler is feeling stressed, the emotion scheduler can also propose a schedule that includes relaxing activities. If the traveler is having fun, the emotion scheduler can propose a schedule that includes even more enjoyable activities, thereby increasing the travel satisfaction. In this way, a schedule based on the traveler's emotions can be proposed, maximizing the enjoyment of the trip.

[0099] The travel assistance system can further include a budget management unit that considers the travel time and budget of the traveler and proposes transportation methods. For example, the budget management unit can propose the optimal transportation method based on the traveler's travel time and budget. The budget management unit can also propose an expensive taxi if the traveler wants to travel quickly. Furthermore, the budget management unit can propose a slower but cheaper bus if the traveler wants to save on budget. This makes it possible to propose the optimal transportation method taking the traveler's travel time and budget into consideration, thereby increasing the convenience of travel.

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

[0101] Step 1: The weather information acquisition unit acquires weather information. For example, it can acquire weather information such as temperature, precipitation, and wind speed in real time, and can also analyze past weather data to predict weather patterns in specific areas. It can also analyze meteorological satellite data in real time to predict local weather changes. Step 2: The map information acquisition unit acquires map information. For example, it acquires map information such as geographic coordinates, road information, and tourist destination information, and can analyze past travel data of travelers to propose the most efficient route. It also acquires traffic congestion information in real time and proposes the optimal detour route. Step 3: The route information acquisition unit acquires route information. For example, it acquires route information such as public transport operation information and route information, and can analyze past public transport operation data to predict the risk of delays and cancellations. It also acquires the operation status of public transport in real time and suggests optimal transfer routes. Step 4: The travel schedule generation unit generates a travel schedule by integrating the information acquired by the weather information acquisition unit, map information acquisition unit, and route information acquisition unit. For example, if the weather worsens, it proposes a schedule that prioritizes indoor tourist spots and generates a travel schedule with ample time to spare, taking into account information about delays in public transportation. It also proposes a schedule that includes appropriate rest periods, taking into account the traveler's physical condition and fatigue level. Step 5: The notification unit notifies the traveler of the travel schedule generated by the travel schedule generation unit. For example, the schedule can be updated in real time via a smartphone app and notified to the traveler. Furthermore, if the traveler becomes lost, the system can suggest the best route and provide navigation. It also provides notifications customized to the traveler's language and culture.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0146] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0169] 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 weather information acquisition unit that acquires weather information; a map information acquisition unit that acquires map information; a route information acquisition unit that acquires route information; a travel schedule generation unit that generates a travel schedule by integrating the information acquired by the weather information acquisition unit, the map information acquisition unit, and the route information acquisition unit; a notification unit that notifies the traveler of the travel schedule generated by the travel schedule generation unit. A system characterized by:

2. The weather information acquisition unit Analyzing meteorological satellite data in real time to predict local weather changes 2. The system of claim 1.

3. The map information acquisition unit Obtaining real-time traffic congestion information and suggesting detour routes 2. The system of claim 1.

4. The route information acquisition unit Obtaining real-time information on transportation status and suggesting transfer routes 2. The system of claim 1.

5. The travel schedule generation unit Obtaining the traveler's location information in real time and suggesting a schedule 2. The system of claim 1.

6. The notification unit Obtaining the traveler's location information in real time and suggesting assistance methods 2. The system of claim 1.

7. The notification unit Analyzing the emotions felt by the traveler regarding the notification or assistance, and providing the notification or assistance that is emotionally satisfying 2. The system of claim 1.

Images