System

Abstract

An object of a system according to an embodiment is to provide an optimal drive route based on a user's preference and a vehicle type.SOLUTION: A system according to an embodiment includes a route generation unit, an integration unit, a feeling information utilization unit, a navigation unit, and a proposal unit. The route generation unit generates a route based on the user's preference and the vehicle type. The integration unit integrates the map information and the landscape information on the basis of the route generated by the route generation unit. The emotion information utilizing unit utilizes the emotion information of the driver based on the information integrated by the integration unit. The navigation unit performs navigation based on the information utilized by the emotion information utilizing unit. The suggestion unit suggests a stop-by spot based on the route navigated by the navigation 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 technologies have had the problem of making it difficult to generate optimal driving routes that match the user's preferences and vehicle type.

[0005] The system according to the embodiment aims to provide an optimal driving route based on the user's preferences and vehicle type. [Means for solving the problem]

[0006] The system according to the embodiment includes a route generation unit, an integration unit, an emotion information utilization unit, a navigation unit, and a suggestion unit. The route generation unit generates a route based on a user's preferences and vehicle type. The integration unit integrates map information and scenic information based on the route generated by the route generation unit. The emotion information utilization unit utilizes the driver's emotion information based on the information integrated by the integration unit. The navigation unit performs navigation based on the information utilized by the emotion information utilization unit. The suggestion unit suggests stop-off spots based on the route navigated by the navigation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal driving route based on the user's preferences and vehicle type. [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) A driving route suggestion system according to an embodiment of the present invention is a system that generates a driving route according to a user's preferences and vehicle type, and navigates the user. As a result, the driving route suggestion system suggests an optimal driving route according to the user's preferences and vehicle type, allowing the user to enjoy driving.

[0029] A driving route suggestion system according to an embodiment includes a route generation unit, an integration unit, an emotion information utilization unit, a navigation unit, and a suggestion unit. The route generation unit generates a route based on a user's preferences and vehicle type. For example, if a user inputs, "I want to take a scenic mountain road," the route generation unit analyzes the instruction and proposes a route that includes a mountain road. The route generation unit can also select an optimal route based on the user's vehicle type. For example, it can propose an off-road route for an SUV and a winding road for a sports car. The integration unit integrates map information and landscape information based on the route generated by the route generation unit. For example, in addition to generating a route from map information, it can also optimize the route by taking into account scenic spots and tourist attractions. The integration unit can also utilize a database of tourist attractions to propose an attractive route for the user. The emotion information utilization unit utilizes the driver's emotion information based on the information integrated by the integration unit. For example, it can propose a similar route based on information that other users have rated the road as "pleasant." The emotion information utilization unit can also utilize a database of past driver emotion information to propose an optimal route for the user. The navigation unit performs navigation based on the information utilized by the emotion information utilization unit. For example, the navigation unit provides specific instructions such as "Turn right at the next intersection." The navigation unit can also optimize the route by taking real-time traffic information into consideration. The suggestion unit suggests stopover spots based on the route navigated by the navigation unit. For example, the suggestion unit makes a suggestion such as "There's a beautiful lake nearby. Why don't you stop by?" The suggestion unit can also suggest recommended spots in the vicinity in addition to the stopover spots specified by the user. As a result, the driving route suggestion system according to the embodiment suggests an optimal driving route according to the user's preferences and vehicle type, allowing the user to enjoy a drive. For example, by passing through mountain roads or country roads, the user can enjoy a drive while fully appreciating the beauty of nature. Furthermore, visiting tourist spots adds to the enjoyment of the trip. Furthermore, by taking into consideration the emotion information of other drivers, the user can achieve a more satisfying drive.

[0030] The route generation unit can analyze the user's past driving history, learn preferences, and reflect them in the next route generation. The route generation unit, for example, collects data on routes chosen by the user in the past and spots visited, and analyzes preferences. For example, if a user prefers mountain roads or seaside routes, the route generation unit can suggest a similar route for the next time. The route generation unit can also learn preferences based on the user's past driving history and reflect them in the next route generation. For example, the route generation unit can reflect the user's evaluations of spots visited in the past in the next route. This allows the user's past driving history to be analyzed, preferences to be learned, and reflected in the next route generation.

[0031] The route generation unit can collect real-time feedback from the user and dynamically optimize the route while driving. The route generation unit, for example, collects feedback from the user in real time while driving and dynamically optimizes the route. For example, if the user provides feedback that "this road is crowded," the route generation unit suggests an alternative route. The route generation unit can also dynamically optimize the route based on the user's real-time feedback. For example, if the user provides feedback that "this road has beautiful scenery," the route is optimized based on that information. In this way, the route can be dynamically optimized while driving by collecting real-time feedback from the user.

[0032] The route generation unit can propose the optimal route for each season based on the user's preferences. The route generation unit, for example, builds a system that proposes the optimal route for each season based on the user's preferences. For example, in spring, it proposes a route that includes cherry blossom viewing spots, and in autumn, it proposes a route that includes autumn foliage spots. The route generation unit can also propose the optimal route taking into consideration seasonal events and scenery. For example, in summer, it proposes a seaside route, and in winter, it proposes a route where you can enjoy snowy scenery. In this way, it is possible to propose the optimal route for each season based on the user's preferences.

[0033] The route generation unit can generate a route that is appropriate not only for the vehicle type but also for the user's driving skills and experience. The route generation unit, for example, builds a system that generates an optimal route taking into account the user's driving skills and experience. For example, it proposes an easy route for beginners and a challenging route for experienced drivers. The route generation unit can also propose an optimal route based on the user's driving skills and experience. For example, it selects an appropriate route based on an evaluation of the user's driving technique. This makes it possible to generate a route that is appropriate not only for the vehicle type but also for the user's driving skills and experience.

[0034] When analyzing the user's input, the route generation unit uses natural language processing technology to accurately understand even ambiguous instructions. For example, the route generation unit incorporates natural language processing technology into the generation AI to build a system that can accurately understand even ambiguous instructions from the user. For example, it can analyze an ambiguous instruction such as "I want to take a road with a good view" and propose the optimal route. The route generation unit can also accurately analyze the user's input using natural language processing technology. For example, it can use morphological analysis and grammatical analysis to accurately understand the user's intention. This allows it to accurately understand even ambiguous instructions from the user and propose the optimal route.

[0035] The route generation unit can generate multiple route candidates based on the user's input and provide the user with options. The route generation unit, for example, builds a system that generates multiple route candidates based on the user's input and provides the user with options. For example, it can propose a scenic route, the shortest route, a route that includes tourist spots, etc. The route generation unit can also generate multiple route candidates according to the user's preferences and conditions. For example, if the user inputs "I want to take a mountain path," it will propose multiple mountain path routes. This allows the user to be provided with multiple route candidates and increase their options.

[0036] The route generation unit can also suggest nearby event information based on the stop-off spots input by the user. The route generation unit, for example, builds a system that also suggests nearby event information based on the stop-off spots input by the user. For example, if a user inputs "I want to go to an art museum," events held in the vicinity will also be suggested. The route generation unit can also collect and suggest nearby event information based on the user's stop-off spots. For example, a local event calendar can be used to provide the user with the most suitable event information. This makes it possible to suggest nearby event information based on the stop-off spots input by the user.

[0037] The route generation unit can also suggest other routes that include similar roads based on the roads the user wants to enjoy input. The route generation unit, for example, builds a system that suggests other routes that include similar roads based on the roads the user wants to enjoy input. For example, if the user inputs "I want to take a mountain road," routes that include other mountain roads are also suggested. The route generation unit can also suggest other routes that include similar roads based on the user's preferences. For example, if the user inputs "I want to enjoy roads along the sea," other seaside routes are also suggested. In this way, other routes that include similar roads can be suggested based on the roads the user wants to enjoy.

[0038] The integration unit can also take real-time weather information into consideration when integrating map information and landscape information. For example, the integration unit builds a system that takes real-time weather information into consideration when integrating map information and landscape information. For example, it can suggest a safe route when it is raining and a scenic route when it is sunny. The integration unit can also suggest an optimal route based on weather information. For example, if it is snowing, it can suggest a route where you can enjoy the snowy scenery. This makes it possible to take real-time weather information into consideration when integrating map information and landscape information.

[0039] The integration unit can propose a route that offers the most beautiful scenery at a specific time of day based on the scenery information. The integration unit, for example, builds a system that proposes a route that offers the most beautiful scenery at a specific time of day based on the scenery information. For example, it proposes a route where you can see a sunset in the evening. The integration unit can also propose the optimal scenic route depending on the time of day. For example, it proposes a route where you can see the sunrise in the morning and a route where you can enjoy the night view in the evening. In this way, it is possible to propose a route where you can see the most beautiful scenery at a specific time of day.

[0040] The integration unit can integrate information on local culture and history in addition to map information and landscape information to suggest cultural routes. The integration unit, for example, integrates information on local culture and history in addition to map information and landscape information to build a system that suggests cultural routes. For example, it suggests routes that include historical buildings and cultural heritage sites. The integration unit can also suggest optimal routes based on information on culture and history. For example, it suggests routes that include local festivals and events. This makes it possible to integrate information on local culture and history in addition to map information and landscape information to suggest cultural routes.

[0041] The integration unit can propose routes that allow users to enjoy different scenery for each season based on the scenery information. The integration unit, for example, builds a system that proposes routes that allow users to enjoy different scenery for each season based on the scenery information. For example, it proposes a route where you can see rows of cherry blossoms in spring and autumn leaves in autumn. The integration unit can also propose optimal routes taking into account the scenery of each season. For example, it proposes a seaside route in summer and a route where you can enjoy snowy scenery in winter. This makes it possible to propose routes that allow users to enjoy different scenery for each season.

[0042] The emotion information utilization unit can filter spots based on the user's interests when utilizing a database of tourist spots. For example, the emotion information utilization unit builds a system that filters spots based on the user's interests when the generation AI utilizes a database of tourist spots. For example, if a user inputs "I'm interested in historical places," the emotion information utilization unit will suggest historical tourist spots. The emotion information utilization unit can also suggest optimal tourist spots based on the user's interests. For example, if a user inputs "I like nature," the emotion information utilization unit will suggest spots with natural landscapes. This makes it possible to filter tourist spots based on the user's interests.

[0043] The emotion information utilization unit can suggest new spots that the user has not visited based on a database of tourist spots. The emotion information utilization unit, for example, builds a system that suggests new spots that the user has not visited based on the database of tourist spots. For example, if the user inputs "I'm looking for a new place," the emotion information utilization unit can suggest tourist spots that have not been visited. The emotion information utilization unit can also suggest new spots based on the user's past visit history. For example, it can suggest tourist spots in areas that the user has not visited before. This makes it possible to suggest new tourist spots that the user has not visited.

[0044] The emotion information utilization unit can integrate local gourmet information in addition to a database of tourist spots and suggest routes for enjoying meals. The emotion information utilization unit, for example, builds a system that integrates local gourmet information in addition to a database of tourist spots and suggests routes for enjoying meals. For example, it suggests a route that combines tourist spots and local restaurants. The emotion information utilization unit can also suggest optimal gourmet spots according to the user's preferences. For example, if the user inputs "I want to enjoy local specialties," it will suggest restaurants where you can enjoy those specialties. In this way, it is possible to integrate local gourmet information in addition to a database of tourist spots and suggest routes for enjoying meals.

[0045] The emotion information utilization unit can suggest routes based on a specific theme to the user based on a database of tourist spots. The emotion information utilization unit, for example, builds a system that suggests routes based on a specific theme to the user based on a database of tourist spots. For example, it suggests a route that goes around historical tourist spots or a route that allows users to enjoy natural scenery. The emotion information utilization unit can also suggest optimal themed routes based on the user's interests and concerns. For example, if the user inputs "I'm interested in art," it suggests a route that goes around art galleries and museums. This allows the user to suggest routes based on a specific theme.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The route generation unit can also suggest routes that suit the user's driving style. For example, it can suggest winding roads with many curves to a user who prefers aggressive driving, and suggest straight roads with beautiful scenery to a user who prefers relaxed driving. The route generation unit can also learn the user's driving style and reflect this in the next route suggestion. For example, it can analyze past driving data and optimize the route based on the user's preferred driving style. This makes it possible to suggest the optimal route that suits the user's driving style.

[0048] The route generation unit can also propose routes that take into account the user's health condition. For example, for a user who finds it difficult to drive for long periods of time, it can propose a route with many rest stops, and for a user who is confident in their physical strength, it can propose a long-distance drive. The route generation unit can also collect the user's health data and reflect it in the next route proposal. For example, it can analyze data from a wearable device and optimize the route based on the user's health condition. This makes it possible to propose the optimal route according to the user's health condition.

[0049] The route generation unit can suggest routes based on specific themes based on the user's hobbies and interests. For example, it can suggest a route that visits historical sites to a history buff, and a route that allows nature lovers to enjoy natural scenery. The route generation unit can also learn the user's hobbies and interests and reflect this in the next route suggestion. For example, it can analyze past visit history and optimize the route based on the user's favorite themes. This makes it possible to suggest the optimal route based on the user's hobbies and interests.

[0050] The route generation unit can also propose a route that takes into consideration whether the user is accompanied by a pet. For example, it can propose a route with many spots where pets can rest, or a route that includes pet-friendly facilities. The route generation unit can also propose an optimal route based on the type and characteristics of the user's pet. For example, it can propose a route that has a dog run for a user who is accompanied by a dog. This makes it possible to propose an optimal route that takes into consideration whether the user is accompanied by a pet.

[0051] The route generation unit can also suggest fuel-efficient routes based on the user's awareness of eco-driving. For example, it can suggest roads with good fuel efficiency or routes that encourage eco-driving. The route generation unit can also learn the user's awareness of eco-driving and reflect this in the next route proposal. For example, it can analyze past driving data and optimize the route based on the user's preferred eco-driving. This makes it possible to suggest the optimal route based on the user's awareness of eco-driving.

[0052] The route generation unit can also suggest routes where users can enjoy music based on their musical preferences while driving. For example, it can suggest routes that include locations where music festivals are held, or spots where users can enjoy music while driving. The route generation unit can also learn the user's musical preferences and reflect them in the next route proposal. For example, it can analyze data on music events that the user has attended in the past and optimize the route based on the user's preferred music. This allows it to suggest the optimal route based on the user's musical preferences.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The route generation unit generates a route based on the user's preferences and vehicle type. For example, if the user inputs, "I want to take a scenic mountain road," the route generation unit analyzes the instruction and suggests a route that includes a mountain road. The route generation unit can also select the optimal route depending on the user's vehicle type. For example, it suggests an off-road route for an SUV and a winding road for a sports car. Step 2: The integration unit integrates map information and landscape information based on the route generated by the route generation unit. For example, in addition to generating a route from map information, the integration unit also optimizes the route by taking into account scenic locations and tourist attractions. The integration unit can also utilize a database of tourist attractions to suggest routes that are attractive to the user. Step 3: The emotion information utilization unit utilizes the driver's emotion information based on the information integrated by the integration unit. For example, based on information that other users have rated the road as "pleasant," the unit can suggest a similar route. The emotion information utilization unit can also use a database of past driver emotion information to suggest the optimal route for the user. Step 4: The navigation unit performs navigation based on the information utilized by the emotion information utilization unit. For example, it provides specific instructions such as "Turn right at the next intersection." The navigation unit can also optimize the route by taking real-time traffic information into account. Step 5: The suggestion unit suggests stop-off spots based on the route navigated by the navigation unit. For example, it may suggest, "There's a beautiful lake nearby. Why don't you stop by?" In addition to the stop-off spots specified by the user, the suggestion unit can also suggest recommended spots in the surrounding area.

[0055] (Example 2) A driving route suggestion system according to an embodiment of the present invention is a system that generates a driving route according to a user's preferences and vehicle type, and navigates the user. As a result, the driving route suggestion system suggests an optimal driving route according to the user's preferences and vehicle type, allowing the user to enjoy driving.

[0056] A driving route suggestion system according to an embodiment includes a route generation unit, an integration unit, an emotion information utilization unit, a navigation unit, and a suggestion unit. The route generation unit generates a route based on a user's preferences and vehicle type. For example, if a user inputs, "I want to take a scenic mountain road," the route generation unit analyzes the instruction and proposes a route that includes a mountain road. The route generation unit can also select an optimal route based on the user's vehicle type. For example, it can propose an off-road route for an SUV and a winding road for a sports car. The integration unit integrates map information and landscape information based on the route generated by the route generation unit. For example, in addition to generating a route from map information, it can also optimize the route by taking into account scenic spots and tourist attractions. The integration unit can also utilize a database of tourist attractions to propose an attractive route for the user. The emotion information utilization unit utilizes the driver's emotion information based on the information integrated by the integration unit. For example, it can propose a similar route based on information that other users have rated the road as "pleasant." The emotion information utilization unit can also utilize a database of past driver emotion information to propose an optimal route for the user. The navigation unit performs navigation based on the information utilized by the emotion information utilization unit. For example, the navigation unit provides specific instructions such as "Turn right at the next intersection." The navigation unit can also optimize the route by taking real-time traffic information into consideration. The suggestion unit suggests stopover spots based on the route navigated by the navigation unit. For example, the suggestion unit makes a suggestion such as "There's a beautiful lake nearby. Why don't you stop by?" The suggestion unit can also suggest recommended spots in the vicinity in addition to the stopover spots specified by the user. As a result, the driving route suggestion system according to the embodiment suggests an optimal driving route according to the user's preferences and vehicle type, allowing the user to enjoy a drive. For example, by passing through mountain roads or country roads, the user can enjoy a drive while fully appreciating the beauty of nature. Furthermore, visiting tourist spots adds to the enjoyment of the trip. Furthermore, by taking into consideration the emotion information of other drivers, the user can achieve a more satisfying drive.

[0057] The route generation unit can analyze the user's past driving history, learn preferences, and reflect them in the next route generation. The route generation unit, for example, collects data on routes chosen by the user in the past and spots visited, and analyzes preferences. For example, if a user prefers mountain roads or seaside routes, the route generation unit can suggest a similar route for the next time. The route generation unit can also learn preferences based on the user's past driving history and reflect them in the next route generation. For example, the route generation unit can reflect the user's evaluations of spots visited in the past in the next route. This allows the user's past driving history to be analyzed, preferences to be learned, and reflected in the next route generation.

[0058] The route generation unit can collect real-time feedback from the user and dynamically optimize the route while driving. The route generation unit, for example, collects feedback from the user in real time while driving and dynamically optimizes the route. For example, if the user provides feedback that "this road is crowded," the route generation unit suggests an alternative route. The route generation unit can also dynamically optimize the route based on the user's real-time feedback. For example, if the user provides feedback that "this road has beautiful scenery," the route is optimized based on that information. In this way, the route can be dynamically optimized while driving by collecting real-time feedback from the user.

[0059] The route generation unit can use the emotion estimation function to analyze the emotions felt by the user while driving in real time and suggest a route that will elicit positive emotions. The route generation unit, for example, uses the emotion estimation function to analyze the emotions felt by the user while driving in real time. For example, it analyzes the user's facial expressions and voice and suggests a route that will elicit strong positive emotions. The route generation unit can also use the emotion estimation function to analyze the user's emotions in real time and suggest a route that will elicit positive emotions. For example, if the user feels that "this scenery is wonderful," it suggests a route that includes that scenery. In this way, it is possible to analyze the emotions felt by the user while driving in real time and suggest a route that will elicit positive emotions.

[0060] The route generation unit can propose the optimal route for each season based on the user's preferences. The route generation unit, for example, builds a system that proposes the optimal route for each season based on the user's preferences. For example, in spring, it proposes a route that includes cherry blossom viewing spots, and in autumn, it proposes a route that includes autumn foliage spots. The route generation unit can also propose the optimal route taking into consideration seasonal events and scenery. For example, in summer, it proposes a seaside route, and in winter, it proposes a route where you can enjoy snowy scenery. In this way, it is possible to propose the optimal route for each season based on the user's preferences.

[0061] The route generation unit can generate a route that is appropriate not only for the vehicle type but also for the user's driving skills and experience. The route generation unit, for example, builds a system that generates an optimal route taking into account the user's driving skills and experience. For example, it proposes an easy route for beginners and a challenging route for experienced drivers. The route generation unit can also propose an optimal route based on the user's driving skills and experience. For example, it selects an appropriate route based on an evaluation of the user's driving technique. This makes it possible to generate a route that is appropriate not only for the vehicle type but also for the user's driving skills and experience.

[0062] The route generation unit can use the emotion estimation function to analyze the expectations the user feels before going on a drive and propose a route that meets those expectations. The route generation unit, for example, uses the emotion estimation function to analyze the expectations the user feels before going on a drive. For example, it analyzes the user's facial expressions and voice and proposes a route that is highly anticipated. The route generation unit can also propose an optimal route based on the user's expectations. For example, if the user feels that "this drive is fun," it proposes a route that meets those expectations. In this way, it is possible to analyze the expectations the user feels before going on a drive and propose a route that meets those expectations.

[0063] When analyzing the user's input, the route generation unit uses natural language processing technology to accurately understand even ambiguous instructions. For example, the route generation unit incorporates natural language processing technology into the generation AI to build a system that can accurately understand even ambiguous instructions from the user. For example, it can analyze an ambiguous instruction such as "I want to take a road with a good view" and propose the optimal route. The route generation unit can also accurately analyze the user's input using natural language processing technology. For example, it can use morphological analysis and grammatical analysis to accurately understand the user's intention. This allows it to accurately understand even ambiguous instructions from the user and propose the optimal route.

[0064] The route generation unit can generate multiple route candidates based on the user's input and provide the user with options. The route generation unit, for example, builds a system that generates multiple route candidates based on the user's input and provides the user with options. For example, it can propose a scenic route, the shortest route, a route that includes tourist spots, etc. The route generation unit can also generate multiple route candidates according to the user's preferences and conditions. For example, if the user inputs "I want to take a mountain path," it will propose multiple mountain path routes. This allows the user to be provided with multiple route candidates and increase their options.

[0065] The route generation unit can use the emotion estimation function to analyze the emotion of the user when inputting information and propose a route that corresponds to that emotion. The route generation unit, for example, uses the emotion estimation function to analyze the emotion of the user when inputting information. For example, it analyzes the user's facial expression and voice and proposes a route that corresponds to that emotion. The route generation unit can also propose an optimal route based on the user's emotion. For example, if the user feels like relaxing, it proposes a quiet route. In this way, it is possible to analyze the emotion of the user when inputting information and propose a route that corresponds to that emotion.

[0066] The route generation unit can also suggest nearby event information based on the stop-off spots input by the user. The route generation unit, for example, builds a system that also suggests nearby event information based on the stop-off spots input by the user. For example, if a user inputs "I want to go to an art museum," events held in the vicinity will also be suggested. The route generation unit can also collect and suggest nearby event information based on the user's stop-off spots. For example, a local event calendar can be used to provide the user with the most suitable event information. This makes it possible to suggest nearby event information based on the stop-off spots input by the user.

[0067] The route generation unit can also suggest other routes that include similar roads based on the roads the user wants to enjoy input. The route generation unit, for example, builds a system that suggests other routes that include similar roads based on the roads the user wants to enjoy input. For example, if the user inputs "I want to take a mountain road," routes that include other mountain roads are also suggested. The route generation unit can also suggest other routes that include similar roads based on the user's preferences. For example, if the user inputs "I want to enjoy roads along the sea," other seaside routes are also suggested. In this way, other routes that include similar roads can be suggested based on the roads the user wants to enjoy.

[0068] The route generation unit can use the emotion estimation function to analyze the user's expectations for the stop-off spots input by the user and propose a route that meets those expectations. The route generation unit, for example, uses the emotion estimation function to analyze the user's expectations for the stop-off spots input by the user. For example, the route generation unit analyzes the user's facial expressions and voice and proposes a route that includes spots that are highly anticipated. The route generation unit can also propose an optimal route based on the user's expectations. For example, if the user feels that "this spot is fun," the route generation unit proposes a route that includes that spot. In this way, the user's expectations for the stop-off spots input by the user can be analyzed and a route that meets those expectations can be proposed.

[0069] The integration unit can also take real-time weather information into consideration when integrating map information and landscape information. For example, the integration unit builds a system that takes real-time weather information into consideration when integrating map information and landscape information. For example, it can suggest a safe route when it is raining and a scenic route when it is sunny. The integration unit can also suggest an optimal route based on weather information. For example, if it is snowing, it can suggest a route where you can enjoy the snowy scenery. This makes it possible to take real-time weather information into consideration when integrating map information and landscape information.

[0070] The integration unit can propose a route that offers the most beautiful scenery at a specific time of day based on the scenery information. The integration unit, for example, builds a system that proposes a route that offers the most beautiful scenery at a specific time of day based on the scenery information. For example, it proposes a route where you can see a sunset in the evening. The integration unit can also propose the optimal scenic route depending on the time of day. For example, it proposes a route where you can see the sunrise in the morning and a route where you can enjoy the night view in the evening. In this way, it is possible to propose a route where you can see the most beautiful scenery at a specific time of day.

[0071] The integration unit can use the emotion estimation function to analyze the emotional information of past drivers and suggest routes that include scenery that elicits positive emotions. For example, the integration unit uses the emotion estimation function to analyze the emotional information of past drivers and build a system that suggests routes that include scenery that elicits positive emotions. For example, it suggests routes that other drivers have rated as "great scenery." The integration unit can also suggest optimal scenic routes based on past emotional information. For example, it suggests routes that past drivers have felt were "pleasant." This makes it possible to analyze the emotional information of past drivers and suggest routes that include scenery that elicit positive emotions.

[0072] The integration unit can integrate information on local culture and history in addition to map information and landscape information to suggest cultural routes. The integration unit, for example, integrates information on local culture and history in addition to map information and landscape information to build a system that suggests cultural routes. For example, it suggests routes that include historical buildings and cultural heritage sites. The integration unit can also suggest optimal routes based on information on culture and history. For example, it suggests routes that include local festivals and events. This makes it possible to integrate information on local culture and history in addition to map information and landscape information to suggest cultural routes.

[0073] The integration unit can propose routes that allow users to enjoy different scenery for each season based on the scenery information. The integration unit, for example, builds a system that proposes routes that allow users to enjoy different scenery for each season based on the scenery information. For example, it proposes a route where you can see rows of cherry blossoms in spring and autumn leaves in autumn. The integration unit can also propose optimal routes taking into account the scenery of each season. For example, it proposes a seaside route in summer and a route where you can enjoy snowy scenery in winter. This makes it possible to propose routes that allow users to enjoy different scenery for each season.

[0074] The integration unit can use the emotion estimation function to analyze the emotion a user feels toward a specific landscape and suggest a route that corresponds to that emotion. For example, the integration unit uses the emotion estimation function to build a system that analyzes the emotion a user feels toward a specific landscape and suggests a route that corresponds to that emotion. For example, if a user feels that "this view is wonderful," the integration unit can suggest a route that includes that landscape. The integration unit can also suggest an optimal scenic route based on the user's emotion. For example, if a user feels that "I want to relax," the integration unit can suggest a route with a quiet landscape. In this way, the emotion a user feels toward a specific landscape can be analyzed and a route that corresponds to that emotion can be suggested.

[0075] The emotion information utilization unit can filter spots based on the user's interests when utilizing a database of tourist spots. For example, the emotion information utilization unit builds a system that filters spots based on the user's interests when the generation AI utilizes a database of tourist spots. For example, if a user inputs "I'm interested in historical places," the emotion information utilization unit will suggest historical tourist spots. The emotion information utilization unit can also suggest optimal tourist spots based on the user's interests. For example, if a user inputs "I like nature," the emotion information utilization unit will suggest spots with natural landscapes. This makes it possible to filter tourist spots based on the user's interests.

[0076] The emotion information utilization unit can suggest new spots that the user has not visited based on a database of tourist spots. The emotion information utilization unit, for example, builds a system that suggests new spots that the user has not visited based on the database of tourist spots. For example, if the user inputs "I'm looking for a new place," the emotion information utilization unit can suggest tourist spots that have not been visited. The emotion information utilization unit can also suggest new spots based on the user's past visit history. For example, it can suggest tourist spots in areas that the user has not visited before. This makes it possible to suggest new tourist spots that the user has not visited.

[0077] The emotion information utilization unit can use the emotion estimation function to analyze the emotion information of past drivers and suggest tourist spots that elicit positive emotions. The emotion information utilization unit, for example, uses the emotion estimation function to analyze the emotion information of past drivers and build a system that suggests tourist spots that elicit positive emotions. For example, it suggests tourist spots that other drivers have rated as "great spots." The emotion information utilization unit can also suggest optimal tourist spots based on past emotion information. For example, it suggests spots that past drivers have felt were "fun places." In this way, it is possible to analyze the emotion information of past drivers and suggest tourist spots that elicit positive emotions.

[0078] The emotion information utilization unit can integrate local gourmet information in addition to a database of tourist spots and suggest routes for enjoying meals. The emotion information utilization unit, for example, builds a system that integrates local gourmet information in addition to a database of tourist spots and suggests routes for enjoying meals. For example, it suggests a route that combines tourist spots and local restaurants. The emotion information utilization unit can also suggest optimal gourmet spots according to the user's preferences. For example, if the user inputs "I want to enjoy local specialties," it will suggest restaurants where you can enjoy those specialties. In this way, it is possible to integrate local gourmet information in addition to a database of tourist spots and suggest routes for enjoying meals.

[0079] The emotion information utilization unit can suggest routes based on a specific theme to the user based on a database of tourist spots. The emotion information utilization unit, for example, builds a system that suggests routes based on a specific theme to the user based on a database of tourist spots. For example, it suggests a route that goes around historical tourist spots or a route that allows users to enjoy natural scenery. The emotion information utilization unit can also suggest optimal themed routes based on the user's interests and concerns. For example, if the user inputs "I'm interested in art," it suggests a route that goes around art galleries and museums. This allows the user to suggest routes based on a specific theme.

[0080] The emotion information utilization unit can use the emotion estimation function to analyze the expectations the user has for a specific tourist spot and propose a route that meets those expectations. The emotion information utilization unit, for example, uses the emotion estimation function to analyze the expectations the user has for a specific tourist spot. For example, it analyzes the user's facial expressions and voice and proposes a route that includes spots that are highly anticipated. The emotion information utilization unit can also propose an optimal route based on the user's expectations. For example, if the user feels that "this spot is fun," it proposes a route that includes that spot. In this way, it is possible to analyze the expectations the user has for a specific tourist spot and propose a route that meets those expectations.

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

[0082] The route generation unit can also suggest routes that suit the user's driving style. For example, it can suggest winding roads with many curves to a user who prefers aggressive driving, and suggest straight roads with beautiful scenery to a user who prefers relaxed driving. The route generation unit can also learn the user's driving style and reflect this in the next route suggestion. For example, it can analyze past driving data and optimize the route based on the user's preferred driving style. This makes it possible to suggest the optimal route that suits the user's driving style.

[0083] The route generation unit can also propose routes that take into account the user's health condition. For example, for a user who finds it difficult to drive for long periods of time, it can propose a route with many rest stops, and for a user who is confident in their physical strength, it can propose a long-distance drive. The route generation unit can also collect the user's health data and reflect it in the next route proposal. For example, it can analyze data from a wearable device and optimize the route based on the user's health condition. This makes it possible to propose the optimal route according to the user's health condition.

[0084] The route generation unit can use the emotion estimation function to analyze the stress the user feels while driving and suggest a route that reduces stress. For example, it can analyze the user's facial expressions and voice, and if the user's stress level is high, suggest a route with less traffic. The route generation unit can also use the emotion estimation function to analyze the user's stress level in real time and suggest a route that reduces stress. For example, if the user feels that "this road is crowded and stressful," it can suggest a different route. This makes it possible to analyze the stress the user feels while driving and suggest a route that reduces stress.

[0085] The route generation unit can suggest routes based on specific themes based on the user's hobbies and interests. For example, it can suggest a route that visits historical sites to a history buff, and a route that allows nature lovers to enjoy natural scenery. The route generation unit can also learn the user's hobbies and interests and reflect this in the next route suggestion. For example, it can analyze past visit history and optimize the route based on the user's favorite themes. This makes it possible to suggest the optimal route based on the user's hobbies and interests.

[0086] The route generation unit can use the emotion estimation function to analyze the level of excitement the user feels while driving and suggest a route that will increase the excitement. For example, the emotion estimation function can analyze the user's facial expressions and voice, and if the level of excitement is high, a route with a lot of activity can be suggested. The route generation unit can also use the emotion estimation function to analyze the user's excitement level in real time and suggest a route that will increase the excitement. For example, if the user feels that "this road is fun," the route can be optimized based on that information. This makes it possible to analyze the level of excitement the user feels while driving and suggest a route that will increase the excitement.

[0087] The route generation unit can also propose a route that takes into consideration whether the user is accompanied by a pet. For example, it can propose a route with many spots where pets can rest, or a route that includes pet-friendly facilities. The route generation unit can also propose an optimal route based on the type and characteristics of the user's pet. For example, it can propose a route that has a dog run for a user who is accompanied by a dog. This makes it possible to propose an optimal route that takes into consideration whether the user is accompanied by a pet.

[0088] The route generation unit can use the emotion estimation function to analyze the sense of security the user feels while driving and suggest a route that will increase the sense of security. For example, it can analyze the user's facial expressions and voice, and if the sense of security is low, it can suggest a safe route. The route generation unit can also use the emotion estimation function to analyze the user's sense of security in real time and suggest a route that will increase the sense of security. For example, if the user feels uneasy about this road, it can suggest a different route. This makes it possible to analyze the sense of security the user feels while driving and suggest a route that will increase the sense of security.

[0089] The route generation unit can also suggest fuel-efficient routes based on the user's awareness of eco-driving. For example, it can suggest roads with good fuel efficiency or routes that encourage eco-driving. The route generation unit can also learn the user's awareness of eco-driving and reflect this in the next route proposal. For example, it can analyze past driving data and optimize the route based on the user's preferred eco-driving. This makes it possible to suggest the optimal route based on the user's awareness of eco-driving.

[0090] The route generation unit can use the emotion estimation function to analyze the user's sense of fatigue while driving and suggest a route that reduces fatigue. For example, the emotion estimation function can analyze the user's facial expressions and voice, and if the user feels highly fatigued, suggest a route with many rest stops. The route generation unit can also use the emotion estimation function to analyze the user's sense of fatigue in real time and suggest a route that reduces fatigue. For example, if the user feels that "this road is tiring," a different route can be suggested. This makes it possible to analyze the user's sense of fatigue while driving and suggest a route that reduces fatigue.

[0091] The route generation unit can also suggest routes where users can enjoy music based on their musical preferences while driving. For example, it can suggest routes that include locations where music festivals are held, or spots where users can enjoy music while driving. The route generation unit can also learn the user's musical preferences and reflect them in the next route proposal. For example, it can analyze data on music events that the user has attended in the past and optimize the route based on the user's preferred music. This allows it to suggest the optimal route based on the user's musical preferences.

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

[0093] Step 1: The route generation unit generates a route based on the user's preferences and vehicle type. For example, if the user inputs, "I want to take a scenic mountain road," the route generation unit analyzes the instruction and suggests a route that includes a mountain road. The route generation unit can also select the optimal route depending on the user's vehicle type. For example, it suggests an off-road route for an SUV and a winding road for a sports car. Step 2: The integration unit integrates map information and landscape information based on the route generated by the route generation unit. For example, in addition to generating a route from map information, the integration unit also optimizes the route by taking into account scenic locations and tourist attractions. The integration unit can also utilize a database of tourist attractions to suggest routes that are attractive to the user. Step 3: The emotion information utilization unit utilizes the driver's emotion information based on the information integrated by the integration unit. For example, based on information that other users have rated the road as "pleasant," the unit can suggest a similar route. The emotion information utilization unit can also use a database of past driver emotion information to suggest the optimal route for the user. Step 4: The navigation unit performs navigation based on the information utilized by the emotion information utilization unit. For example, it provides specific instructions such as "Turn right at the next intersection." The navigation unit can also optimize the route by taking real-time traffic information into account. Step 5: The suggestion unit suggests stop-off spots based on the route navigated by the navigation unit. For example, it may suggest, "There's a beautiful lake nearby. Why don't you stop by?" In addition to the stop-off spots specified by the user, the suggestion unit can also suggest recommended spots in the surrounding area.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (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.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 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 route generation unit that generates a route based on a user's preferences and vehicle type; an integration unit that integrates map information and landscape information based on the route generated by the route generation unit; an emotion information utilization unit that utilizes emotion information of a driver based on the information integrated by the integration unit; a navigation unit that performs navigation based on the information utilized by the emotion information utilization unit; a suggestion unit that suggests stop-off spots based on the route navigated by the navigation unit. A system characterized by:

2. The route generation unit Collecting real-time feedback from the user and dynamically optimizing the route as the user drives 2. The system of claim 1.

3. The route generation unit Based on the user's preferences, the system proposes the best route for each season.

2. The system of claim 1.

4. The integration unit When integrating the map information and the landscape information, real-time weather information is also taken into consideration.

2. The system of claim 1.

5. The emotion information utilization unit When utilizing a database of tourist spots, the spots are filtered based on the user's interests.

2. The system of claim 1.

6. The route generation unit Using emotion estimation function, the emotions felt by the user while driving are analyzed in real time, and a route that elicits positive emotions is suggested.

2. The system of claim 1.

7. The integration unit Using emotion estimation function, the system analyzes the driver's past emotional information and suggests routes that include scenery that elicits positive emotions.

2. The system of claim 1.

8. The emotion information utilization unit Using an emotion estimation function, the emotional information of the driver in the past is analyzed, and tourist spots that elicit positive emotions are suggested.

2. The system of claim 1.

Images