System

Abstract

It is an object of the system according to the embodiment to provide a proposal of a drop-in spot or a voice interaction that utilizes real-time information in accordance with an individual need of a user.SOLUTION: A system according to an embodiment includes a user information input portion, a vehicle information input portion, a real-time information association portion, a side trip proposal portion, and a voice interaction portion. The user information input unit inputs user information. The vehicle information input unit inputs vehicle information. The real-time information association unit associates real-time information. The drop-in spot proposal unit proposes a drop-in spot on the basis of the information inputted from the user information inputting unit and the vehicle information inputting unit. The voice interaction unit performs voice interaction on the basis of the information linked by the real-time information linking 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 do not adequately suggest detour spots that meet the individual needs of users or provide voice dialogue that utilizes real-time information, so there is room for improvement.

[0005] The system according to the embodiment aims to provide a voice dialogue that utilizes real-time information and suggestions for detour spots that meet the individual needs of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information input unit, a vehicle information input unit, a real-time information linking unit, a detour suggestion unit, and a voice dialogue unit. The user information input unit inputs user information. The vehicle information input unit inputs vehicle information. The real-time information linking unit links real-time information. The detour suggestion unit suggests detour spots based on the information input by the user information input unit and the vehicle information input unit. The voice dialogue unit performs voice dialogue based on the information linked by the real-time information linking unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide suggestions for detour spots according to the individual needs of the user and voice dialogue utilizing real-time information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The Total Navi service according to an embodiment of the present invention is a system that provides voice car navigation, AI generation, congestion information, and parking information in real time. This system suggests "fun detours" to users by inputting information such as past routes, stopover spots, their own profile, and things they want to do on the way to their destination. As a result, the Total Navi service provides users with a comfortable drive, allowing them to enjoy the drive itself rather than just reaching their destination.

[0029] The Total Navi service according to the embodiment includes a user information input unit, a vehicle information input unit, a real-time information linking unit, a detour suggestion unit, and a voice dialogue unit. The user information input unit inputs user information. For example, the user can input information such as past routes, stopover spots, their own profile, and things they want to do on the way to their destination. The user information input unit allows the generation AI to analyze the user's input information and learn specific patterns and preferences. The vehicle information input unit inputs vehicle information. For example, the user can input information such as the model and size of their car and their driving history. The vehicle information input unit allows the generation AI to analyze the vehicle information and generate an optimal route taking into account road width and driving difficulty. The real-time information linking unit links real-time information. For example, the generation AI can link parking information and road traffic information for the destination in real time and update it as needed. The detour suggestion unit suggests detour spots based on the information input by the user information input unit and the vehicle information input unit. For example, the generation AI can suggest parks where children can have fun or cafes where people can take a break based on conditions such as "when there is a family of four with a five-year-old child and the trip is likely to take more than three hours." The voice dialogue unit performs voice dialogue based on information linked by the real-time information linking unit. For example, if a user asks, "Where is the next rest spot?", the generation AI can respond with, "The next rest spot is a park that you will arrive at in 10 minutes." As a result, the Total Navi service according to the embodiment provides users with a comfortable drive, allowing them to not only reach their destination but also enjoy the drive itself.

[0030] The user information input unit analyzes the user's past driving history and learns specific patterns and preferences, allowing it to suggest more personalized detour spots. For example, the user information input unit analyzes the user's past driving history and learns frequently visited places and preferred routes. For example, the unit identifies cafes and parks that the user frequently visits and suggests new detour spots based on that. The user information input unit also extracts the user's preferences from the driving history and finds specific patterns. For example, if the user likes natural scenery, the unit can suggest scenic routes and natural parks. The user information input unit also analyzes the user's preferred time zones and days of the week based on the past driving history and suggests detour spots based on that information. For example, the unit can prioritize suggestions of spots that are often visited on weekends. This allows it to suggest personalized detour spots based on the user's past driving history.

[0031] The user information input unit can link with the user's social media account and suggest detour spots that the user might be interested in based on the content of posts and places that the user has "liked." The user information input unit, for example, links with the user's social media account and analyzes the content of posts and places that the user has "liked." For example, detour spots can be suggested based on restaurants and tourist attractions that the user has "liked." The user information input unit can also analyze the content of social media posts to identify the user's interests and concerns. For example, related spots can be suggested based on themes and hashtags that the user frequently posts about. The user information input unit can also suggest events and festivals that the user might be interested in based on the user's social media activity history. For example, similar events can be suggested based on information about events the user has attended. In this way, spots that the user might be interested in can be suggested based on the user's social media account.

[0032] The user information input unit can input user information using gestures and eye tracking in addition to voice, thereby providing a more intuitive interface. The user information input unit, for example, builds a system in which user information is input using gestures and eye tracking in addition to voice. For example, the system recognizes the location where the user is pointing and inputs that information. The user information input unit also uses eye tracking technology to identify the location or object the user is looking at and suggests detour spots based on that information. For example, if the user is looking at a sign, information about that location can be provided. The user information input unit also uses gesture recognition technology to analyze the user's hand movements and facial expressions, thereby providing an intuitive interface. For example, the user can input a specific command by waving their hand. This allows user information to be input using gestures and eye tracking in addition to voice, thereby providing an intuitive interface.

[0033] The user information input unit works in conjunction with the user's health data to suggest detour spots based on the user's health condition. The user information input unit, for example, monitors the user's health data (heart rate and stress level) in real time and suggests detour spots based on that data. For example, if the heart rate is high, it suggests places where you can relax. The user information input unit also analyzes the health data and builds a system that suggests spots based on the user's health condition. For example, if the stress level is high, it can suggest relaxation facilities. The user information input unit also works in conjunction with the user's health data to automatically suggest detour spots based on the user's health condition. For example, if the heart rate is low, it can suggest active sports facilities. In this way, it is possible to suggest detour spots based on the user's health data.

[0034] The vehicle information input unit can analyze the vehicle's fuel efficiency data and suggest a fuel-efficient route. The vehicle information input unit, for example, analyzes the vehicle's fuel efficiency data in real time and builds a system that suggests a fuel-efficient route. For example, it prioritizes routes on flat roads and with few traffic lights. The vehicle information input unit also suggests optimal speeds and acceleration patterns based on the fuel efficiency data. For example, it can navigate the vehicle to travel at the most fuel-efficient speed. The vehicle information input unit also analyzes the vehicle's fuel efficiency data and automatically selects a fuel-efficient route. For example, it can suggest a route that avoids steep slopes and traffic jams that worsen fuel efficiency. In this way, it is possible to suggest a fuel-efficient route based on the vehicle's fuel efficiency data.

[0035] The vehicle information input unit can suggest a route that puts less strain on the vehicle based on the vehicle's maintenance history. The vehicle information input unit, for example, analyzes the vehicle's maintenance history and builds a system that suggests routes that put less strain on the vehicle. For example, if there have been engine problems in the past, a route that avoids steep slopes can be suggested. The vehicle information input unit also selects the optimal route according to the vehicle's condition based on the maintenance history. For example, if the tires are worn out, it can prioritize roads with good pavement. The vehicle information input unit also takes the vehicle's maintenance history into consideration and automatically selects a route that puts less strain on the vehicle. For example, if the brakes are worn out, it can suggest a route with fewer traffic lights. In this way, it is possible to suggest routes that put less strain on the vehicle based on the vehicle's maintenance history.

[0036] The vehicle information input unit can automatically input vehicle information using an on-board camera or sensor, thereby reducing the user's workload. The vehicle information input unit, for example, uses an on-board camera or sensor to build a system that automatically collects vehicle information. For example, the vehicle's exterior and interior conditions are photographed with a camera and the data is analyzed. The vehicle information input unit also uses sensors to monitor the vehicle's condition in real time and propose the optimal route based on that information. For example, the tire pressure and engine temperature can be measured with sensors. The vehicle information input unit can also develop a system that automatically inputs vehicle information using an on-board camera or sensor. For example, the vehicle's mileage and remaining fuel level can be automatically detected and registered in a database. This allows the vehicle information to be automatically input using an on-board camera or sensor, reducing the user's workload.

[0037] The vehicle information input unit stores vehicle driving data in the cloud and shares it with other users, thereby generating an optimal route based on more data. The vehicle information input unit, for example, builds a system for storing vehicle driving data in the cloud and sharing it with other users. For example, it uploads mileage and fuel efficiency data to the cloud. The vehicle information input unit also analyzes the driving data stored in the cloud and generates an optimal route. For example, it can propose a route that avoids congestion based on data from other users. The vehicle information input unit also develops a system for sharing driving data with other users and generating an optimal route based on more data. For example, it can propose a fuel-efficient route based on data from users of the same vehicle model. In this way, it is possible to generate an optimal route by storing driving data in the cloud and sharing it with other users.

[0038] The real-time information linking unit can evaluate the safety of routes based on information provided in real time and prioritize the suggestion of safe routes. The real-time information linking unit, for example, analyzes traffic information provided in real time and builds a system for evaluating the safety of routes. For example, it suggests safe routes based on accident and construction information. The real-time information linking unit also evaluates the safety of routes based on real-time information and prioritizes the suggestion of safe routes. For example, it can select a route with many bright street lights when driving at night. The real-time information linking unit can also analyze weather information provided in real time and suggest a route that avoids bad weather. For example, if heavy rain or snow is forecast, it can select a route that will be less affected by the weather. This makes it possible to prioritize the suggestion of safe routes based on real-time information.

[0039] The real-time information linking unit can analyze real-time information and propose an optimal departure time based on predicted traffic conditions. The real-time information linking unit, for example, analyzes traffic information provided in real time and builds a system that proposes an optimal departure time based on predicted traffic conditions. For example, it proposes a departure time that avoids time periods when congestion is predicted. The real-time information linking unit also predicts traffic conditions based on real-time information and proposes an optimal departure time. For example, it can select a time period with low traffic volume and adjust the departure time. The real-time information linking unit also analyzes event information provided in real time and proposes an optimal departure time to avoid congestion during events. For example, it can propose a departure time that avoids time periods before and after large-scale events. In this way, it is possible to propose an optimal departure time based on real-time information.

[0040] The real-time information collaboration unit can also collaborate with other modes of transportation to propose combined modes of transportation. For example, the real-time information collaboration unit builds a system that proposes combined modes of transportation by collaborating with other modes of transportation (public transportation or shared ride services) based on real-time information. For example, it proposes the optimal transfer route based on train and bus operation information. The real-time information collaboration unit also analyzes real-time information from public transportation and shared ride services to propose the optimal mode of transportation. For example, it can propose an alternative bus route based on train delay information. The real-time information collaboration unit also strengthens collaboration with other modes of transportation to develop a system that proposes combined modes of transportation. For example, it can check the availability of shared ride services in real time and propose the optimal mode of transportation. This makes it possible to collaborate with other modes of transportation to propose combined modes of transportation.

[0041] The real-time information linking unit can visually display real-time information using AR technology, allowing the user to intuitively understand it. The real-time information linking unit, for example, builds a system that visually displays real-time information using AR technology. For example, real-time traffic information and parking information are overlaid on a smartphone or an in-vehicle display. The real-time information linking unit also uses AR technology to visually display real-time information, allowing the user to intuitively understand it. For example, congested roads and available parking spaces can be displayed using AR. The real-time information linking unit also develops a system that displays real-time information using AR technology, allowing the user to intuitively grasp the information. For example, the route to the destination can be displayed using AR, allowing the user to visually confirm it. This makes it possible to visually display real-time information using AR technology, allowing the user to intuitively understand it.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The user information input unit can work in conjunction with the user's health data to suggest detour spots based on the user's health condition. For example, it can monitor the user's heart rate and stress level in real time and suggest places where the user can relax based on that data. It can also build a system that analyzes the health data and suggests spots based on the user's health condition. For example, if the stress level is high, it can suggest relaxation facilities. It can also work in conjunction with the health data to automatically suggest detour spots based on the user's health condition. For example, if the heart rate is low, it can suggest active sports facilities. This makes it possible to suggest detour spots based on the user's health data.

[0044] The user information input unit can input user information using gestures and eye tracking in addition to voice, providing a more intuitive interface. For example, it can recognize the location where the user is pointing and input that information. It can also use eye tracking technology to identify the location or object the user is looking at and suggest detour spots based on that information. For example, if the user is looking at a sign, it can provide information about that location. It can also use gesture recognition technology to analyze the user's hand movements and facial expressions, providing an intuitive interface. For example, the user can input specific commands by waving their hand. This makes it possible to input user information using gestures and eye tracking in addition to voice, providing an intuitive interface.

[0045] The vehicle information input unit can analyze the vehicle's fuel economy data and suggest fuel-efficient routes. For example, it can analyze the vehicle's fuel economy data in real time and prioritize routes with flat roads and few traffic lights. It can also suggest optimal speeds and acceleration patterns based on the fuel economy data. For example, it can navigate to drive at the most fuel-efficient speed. It can also analyze the fuel economy data and automatically select fuel-efficient routes. For example, it can suggest routes that avoid steep slopes and traffic jams that worsen fuel economy. This makes it possible to suggest fuel-efficient routes based on the vehicle's fuel economy data.

[0046] The vehicle information input unit can suggest routes that put less strain on the vehicle based on the vehicle's maintenance history. For example, it can analyze the vehicle's maintenance history and, if there have been engine problems in the past, suggest a route that avoids steep slopes. It can also select the optimal route based on the vehicle's condition based on the maintenance history. For example, if the tires are worn out, it can prioritize roads with good pavement. Furthermore, it can automatically select a route that puts less strain on the vehicle by taking the maintenance history into consideration. For example, if the brakes are worn out, it can suggest a route with fewer traffic lights. This makes it possible to suggest routes that put less strain on the vehicle based on the vehicle's maintenance history.

[0047] The vehicle information input unit can automatically input vehicle information using on-board cameras and sensors, reducing the user's workload. For example, a system can be built that automatically collects vehicle information using on-board cameras and sensors. For example, a camera can capture images of the vehicle's exterior and interior conditions and analyze the data. Sensors can also be used to monitor the vehicle's condition in real time and suggest the optimal route based on that information. For example, sensors can measure tire pressure and engine temperature. Furthermore, a system can be developed that automatically inputs vehicle information using on-board cameras and sensors. For example, the vehicle's mileage and remaining fuel level can be automatically detected and registered in a database. This makes it possible to automatically input vehicle information using on-board cameras and sensors, reducing the user's workload.

[0048] The real-time information linking unit can also link with other modes of transportation to propose combined modes of transportation. For example, a system can be built that links with other modes of transportation (public transportation and shared ride services) based on real-time information to propose combined modes of transportation. For example, it can propose optimal transfer routes based on train and bus operation information. It can also analyze real-time information from public transportation and shared ride services to propose optimal modes of transportation. For example, it can propose alternative bus routes based on train delay information. Furthermore, a system that strengthens linking with other modes of transportation will be developed to propose combined modes of transportation. For example, it can check the availability of shared ride services in real time and propose the optimal mode of transportation. This makes it possible to link with other modes of transportation to propose combined modes of transportation.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The user information input unit inputs user information. For example, the user can input information such as past routes, stopover spots, their profile, and what they want to do on the way to their destination. The user information input unit also allows the generation AI to analyze the user's input information and learn specific patterns and preferences. Step 2: The vehicle information input unit inputs vehicle information. For example, the user can input information such as the vehicle model, size, and driving history. The vehicle information input unit also allows the generation AI to analyze the vehicle information and generate an optimal route that takes into account road width and driving difficulty. Step 3: The real-time information linking unit links real-time information. For example, the generation AI can link destination parking information and road traffic information in real time and update them as needed. Step 4: The detour suggestion unit suggests detour spots based on the information entered by the user information input unit and the vehicle information input unit. For example, the generation AI can suggest parks where children can have fun or cafes where people can take a break based on conditions such as "when there is a family of four with a five-year-old child and it looks like it will take more than three hours." Step 5: The voice dialogue unit conducts a voice dialogue based on the information linked by the real-time information linking unit. For example, if a user asks, "Where is the next rest spot?", the generation AI can respond with, "The next rest spot is a park that you will arrive at in 10 minutes."

[0051] (Example 2) The Total Navi service according to an embodiment of the present invention is a system that provides voice car navigation, AI generation, congestion information, and parking information in real time. This system suggests "fun detours" to users by inputting information such as past routes, stopover spots, their own profile, and things they want to do on the way to their destination. As a result, the Total Navi service provides users with a comfortable drive, allowing them to enjoy the drive itself rather than just reaching their destination.

[0052] The Total Navi service according to the embodiment includes a user information input unit, a vehicle information input unit, a real-time information linking unit, a detour suggestion unit, and a voice dialogue unit. The user information input unit inputs user information. For example, the user can input information such as past routes, stopover spots, their own profile, and things they want to do on the way to their destination. The user information input unit allows the generation AI to analyze the user's input information and learn specific patterns and preferences. The vehicle information input unit inputs vehicle information. For example, the user can input information such as the model and size of their car and their driving history. The vehicle information input unit allows the generation AI to analyze the vehicle information and generate an optimal route taking into account road width and driving difficulty. The real-time information linking unit links real-time information. For example, the generation AI can link parking information and road traffic information for the destination in real time and update it as needed. The detour suggestion unit suggests detour spots based on the information input by the user information input unit and the vehicle information input unit. For example, the generation AI can suggest parks where children can have fun or cafes where people can take a break based on conditions such as "when there is a family of four with a five-year-old child and the trip is likely to take more than three hours." The voice dialogue unit performs voice dialogue based on information linked by the real-time information linking unit. For example, if a user asks, "Where is the next rest spot?", the generation AI can respond with, "The next rest spot is a park that you will arrive at in 10 minutes." As a result, the Total Navi service according to the embodiment provides users with a comfortable drive, allowing them to not only reach their destination but also enjoy the drive itself.

[0053] The user information input unit analyzes the user's past driving history and learns specific patterns and preferences, allowing it to suggest more personalized detour spots. For example, the user information input unit analyzes the user's past driving history and learns frequently visited places and preferred routes. For example, the unit identifies cafes and parks that the user frequently visits and suggests new detour spots based on that. The user information input unit also extracts the user's preferences from the driving history and finds specific patterns. For example, if the user likes natural scenery, the unit can suggest scenic routes and natural parks. The user information input unit also analyzes the user's preferred time zones and days of the week based on the past driving history and suggests detour spots based on that information. For example, the unit can prioritize suggestions of spots that are often visited on weekends. This allows it to suggest personalized detour spots based on the user's past driving history.

[0054] The user information input unit can link with the user's social media account and suggest detour spots that the user might be interested in based on the content of posts and places that the user has "liked." The user information input unit, for example, links with the user's social media account and analyzes the content of posts and places that the user has "liked." For example, detour spots can be suggested based on restaurants and tourist attractions that the user has "liked." The user information input unit can also analyze the content of social media posts to identify the user's interests and concerns. For example, related spots can be suggested based on themes and hashtags that the user frequently posts about. The user information input unit can also suggest events and festivals that the user might be interested in based on the user's social media activity history. For example, similar events can be suggested based on information about events the user has attended. In this way, spots that the user might be interested in can be suggested based on the user's social media account.

[0055] The user information input unit can use the emotion estimation function to analyze the user's current emotional state and suggest relaxing or exciting detour spots. The user information input unit, for example, uses the emotion estimation function to analyze the user's current emotional state. For example, if the user is feeling stressed, it can suggest relaxing hot springs or parks. The user information input unit can also monitor the user's emotional state in real time and suggest detour spots based on the results. For example, if the user is excited, it can suggest an exciting amusement park. The user information input unit can also build a system that suggests spots according to the user's emotional state based on the emotion estimation data. For example, if the user is tired, it can suggest a cafe or rest spot where the user can relax. In this way, it is possible to suggest relaxing or exciting spots based on the user's emotional state.

[0056] The user information input unit can input user information using gestures and eye tracking in addition to voice, thereby providing a more intuitive interface. The user information input unit, for example, builds a system in which user information is input using gestures and eye tracking in addition to voice. For example, the system recognizes the location where the user is pointing and inputs that information. The user information input unit also uses eye tracking technology to identify the location or object the user is looking at and suggests detour spots based on that information. For example, if the user is looking at a sign, information about that location can be provided. The user information input unit also uses gesture recognition technology to analyze the user's hand movements and facial expressions, thereby providing an intuitive interface. For example, the user can input a specific command by waving their hand. This allows user information to be input using gestures and eye tracking in addition to voice, thereby providing an intuitive interface.

[0057] The user information input unit works in conjunction with the user's health data to suggest detour spots based on the user's health condition. The user information input unit, for example, monitors the user's health data (heart rate and stress level) in real time and suggests detour spots based on that data. For example, if the heart rate is high, it suggests places where you can relax. The user information input unit also analyzes the health data and builds a system that suggests spots based on the user's health condition. For example, if the stress level is high, it can suggest relaxation facilities. The user information input unit also works in conjunction with the user's health data to automatically suggest detour spots based on the user's health condition. For example, if the heart rate is low, it can suggest active sports facilities. In this way, it is possible to suggest detour spots based on the user's health data.

[0058] The user information input unit uses the emotion estimation function to analyze the emotion of the user when entering information in real time, and can provide an interface that elicits positive emotions. The user information input unit, for example, uses the emotion estimation function to analyze the emotion of the user when entering information in real time. For example, if the user is feeling stressed, an interface that allows the user to relax is provided. The user information input unit also analyzes the user's emotional state and provides an interface that elicits positive emotions. For example, animations and sounds that make the user smile can be used. The user information input unit also builds a system that dynamically adjusts the interface according to the user's emotional state based on the emotion estimation data. For example, if the user is tired, a simple and intuitive interface can be provided. This makes it possible to analyze the emotion of the user when entering information in real time, and provide an interface that elicits positive emotions.

[0059] The vehicle information input unit can analyze the vehicle's fuel efficiency data and suggest a fuel-efficient route. The vehicle information input unit, for example, analyzes the vehicle's fuel efficiency data in real time and builds a system that suggests a fuel-efficient route. For example, it prioritizes routes on flat roads and with few traffic lights. The vehicle information input unit also suggests optimal speeds and acceleration patterns based on the fuel efficiency data. For example, it can navigate the vehicle to travel at the most fuel-efficient speed. The vehicle information input unit also analyzes the vehicle's fuel efficiency data and automatically selects a fuel-efficient route. For example, it can suggest a route that avoids steep slopes and traffic jams that worsen fuel efficiency. In this way, it is possible to suggest a fuel-efficient route based on the vehicle's fuel efficiency data.

[0060] The vehicle information input unit can suggest a route that puts less strain on the vehicle based on the vehicle's maintenance history. The vehicle information input unit, for example, analyzes the vehicle's maintenance history and builds a system that suggests routes that put less strain on the vehicle. For example, if there have been engine problems in the past, a route that avoids steep slopes can be suggested. The vehicle information input unit also selects the optimal route according to the vehicle's condition based on the maintenance history. For example, if the tires are worn out, it can prioritize roads with good pavement. The vehicle information input unit also takes the vehicle's maintenance history into consideration and automatically selects a route that puts less strain on the vehicle. For example, if the brakes are worn out, it can suggest a route with fewer traffic lights. In this way, it is possible to suggest routes that put less strain on the vehicle based on the vehicle's maintenance history.

[0061] The vehicle information input unit can use the emotion estimation function to analyze the driver's stress level and suggest a route that reduces stress. The vehicle information input unit, for example, uses the emotion estimation function to analyze the driver's stress level in real time. For example, if stress is high, it can suggest a route that avoids congestion. The vehicle information input unit also builds a system that selects a route that reduces stress based on the driver's stress level. For example, it can suggest a scenic route or relaxing music. The vehicle information input unit also automatically selects the optimal route according to the driver's stress level based on the emotion estimation data. For example, if stress is low, it can suggest the shortest route. This makes it possible to suggest a route that reduces stress based on the driver's stress level.

[0062] The vehicle information input unit can automatically input vehicle information using an on-board camera or sensor, thereby reducing the user's workload. The vehicle information input unit, for example, uses an on-board camera or sensor to build a system that automatically collects vehicle information. For example, the vehicle's exterior and interior conditions are photographed with a camera and the data is analyzed. The vehicle information input unit also uses sensors to monitor the vehicle's condition in real time and propose the optimal route based on that information. For example, the tire pressure and engine temperature can be measured with sensors. The vehicle information input unit can also develop a system that automatically inputs vehicle information using an on-board camera or sensor. For example, the vehicle's mileage and remaining fuel level can be automatically detected and registered in a database. This allows the vehicle information to be automatically input using an on-board camera or sensor, reducing the user's workload.

[0063] The vehicle information input unit stores vehicle driving data in the cloud and shares it with other users, thereby generating an optimal route based on more data. The vehicle information input unit, for example, builds a system for storing vehicle driving data in the cloud and sharing it with other users. For example, it uploads mileage and fuel efficiency data to the cloud. The vehicle information input unit also analyzes the driving data stored in the cloud and generates an optimal route. For example, it can propose a route that avoids congestion based on data from other users. The vehicle information input unit also develops a system for sharing driving data with other users and generating an optimal route based on more data. For example, it can propose a fuel-efficient route based on data from users of the same vehicle model. In this way, it is possible to generate an optimal route by storing driving data in the cloud and sharing it with other users.

[0064] The vehicle information input unit can use the emotion estimation function to analyze the driver's emotional state in real time and suggest a relaxing route. The vehicle information input unit, for example, uses the emotion estimation function to analyze the driver's emotional state in real time. For example, if the driver is nervous, the vehicle information input unit suggests a relaxing route. The vehicle information input unit also builds a system that selects a relaxing route based on the driver's emotional state. For example, it can suggest a scenic route or a route that plays music. The vehicle information input unit also automatically selects the optimal route according to the driver's emotional state based on the emotion estimation data. For example, if the driver is tired, it can suggest a route that includes a rest spot. This makes it possible to suggest a relaxing route based on the driver's emotional state.

[0065] The real-time information linking unit can evaluate the safety of routes based on information provided in real time and prioritize the suggestion of safe routes. The real-time information linking unit, for example, analyzes traffic information provided in real time and builds a system for evaluating the safety of routes. For example, it suggests safe routes based on accident and construction information. The real-time information linking unit also evaluates the safety of routes based on real-time information and prioritizes the suggestion of safe routes. For example, it can select a route with many bright street lights when driving at night. The real-time information linking unit can also analyze weather information provided in real time and suggest a route that avoids bad weather. For example, if heavy rain or snow is forecast, it can select a route that will be less affected by the weather. This makes it possible to prioritize the suggestion of safe routes based on real-time information.

[0066] The real-time information linking unit can analyze real-time information and propose an optimal departure time based on predicted traffic conditions. The real-time information linking unit, for example, analyzes traffic information provided in real time and builds a system that proposes an optimal departure time based on predicted traffic conditions. For example, it proposes a departure time that avoids time periods when congestion is predicted. The real-time information linking unit also predicts traffic conditions based on real-time information and proposes an optimal departure time. For example, it can select a time period with low traffic volume and adjust the departure time. The real-time information linking unit also analyzes event information provided in real time and proposes an optimal departure time to avoid congestion during events. For example, it can propose a departure time that avoids time periods before and after large-scale events. In this way, it is possible to propose an optimal departure time based on real-time information.

[0067] The real-time information linking unit can use the emotion estimation function to analyze the user's emotional state and provide real-time information for reducing stress. The real-time information linking unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provide information for reducing stress. For example, it can suggest relaxing music or a scenic route. The real-time information linking unit also builds a system that provides real-time information for reducing stress based on the user's emotional state. For example, it can suggest detour routes or rest spots to avoid crowds. The real-time information linking unit also provides real-time information according to the user's emotional state based on the emotion estimation data. For example, if the user is tired, it can provide information on nearby rest spots or cafes. This makes it possible to provide real-time information for reducing stress based on the user's emotional state.

[0068] The real-time information collaboration unit can also collaborate with other modes of transportation to propose combined modes of transportation. For example, the real-time information collaboration unit builds a system that proposes combined modes of transportation by collaborating with other modes of transportation (public transportation or shared ride services) based on real-time information. For example, it proposes the optimal transfer route based on train and bus operation information. The real-time information collaboration unit also analyzes real-time information from public transportation and shared ride services to propose the optimal mode of transportation. For example, it can propose an alternative bus route based on train delay information. The real-time information collaboration unit also strengthens collaboration with other modes of transportation to develop a system that proposes combined modes of transportation. For example, it can check the availability of shared ride services in real time and propose the optimal mode of transportation. This makes it possible to collaborate with other modes of transportation to propose combined modes of transportation.

[0069] The real-time information linking unit can visually display real-time information using AR technology, allowing the user to intuitively understand it. The real-time information linking unit, for example, builds a system that visually displays real-time information using AR technology. For example, real-time traffic information and parking information are overlaid on a smartphone or an in-vehicle display. The real-time information linking unit also uses AR technology to visually display real-time information, allowing the user to intuitively understand it. For example, congested roads and available parking spaces can be displayed using AR. The real-time information linking unit also develops a system that displays real-time information using AR technology, allowing the user to intuitively grasp the information. For example, the route to the destination can be displayed using AR, allowing the user to visually confirm it. This makes it possible to visually display real-time information using AR technology, allowing the user to intuitively understand it.

[0070] The real-time information linking unit can use the emotion estimation function to analyze the user's emotional response to real-time information and provide information that elicits positive emotions preferentially. The real-time information linking unit, for example, uses the emotion estimation function to analyze the user's emotional response to real-time information. For example, it preferentially provides information that indicates a positive emotion to the user. The real-time information linking unit also builds a system that provides real-time information that elicits positive emotions based on the user's emotional response. For example, it can suggest music that helps the user relax or a scenic route. The real-time information linking unit also provides real-time information according to the user's emotional response based on the emotion estimation data. For example, if the user is feeling stressed, it can preferentially provide information that helps the user relax. This makes it possible to preferentially provide information that elicits positive emotions based on the user's emotional response to real-time information.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The user information input unit can work in conjunction with the user's health data to suggest detour spots based on the user's health condition. For example, it can monitor the user's heart rate and stress level in real time and suggest places where the user can relax based on that data. It can also build a system that analyzes the health data and suggests spots based on the user's health condition. For example, if the stress level is high, it can suggest relaxation facilities. It can also work in conjunction with the health data to automatically suggest detour spots based on the user's health condition. For example, if the heart rate is low, it can suggest active sports facilities. This makes it possible to suggest detour spots based on the user's health data.

[0073] The user information input unit can input user information using gestures and eye tracking in addition to voice, providing a more intuitive interface. For example, it can recognize the location where the user is pointing and input that information. It can also use eye tracking technology to identify the location or object the user is looking at and suggest detour spots based on that information. For example, if the user is looking at a sign, it can provide information about that location. It can also use gesture recognition technology to analyze the user's hand movements and facial expressions, providing an intuitive interface. For example, the user can input specific commands by waving their hand. This makes it possible to input user information using gestures and eye tracking in addition to voice, providing an intuitive interface.

[0074] The vehicle information input unit can analyze the vehicle's fuel economy data and suggest fuel-efficient routes. For example, it can analyze the vehicle's fuel economy data in real time and prioritize routes with flat roads and few traffic lights. It can also suggest optimal speeds and acceleration patterns based on the fuel economy data. For example, it can navigate to drive at the most fuel-efficient speed. It can also analyze the fuel economy data and automatically select fuel-efficient routes. For example, it can suggest routes that avoid steep slopes and traffic jams that worsen fuel economy. This makes it possible to suggest fuel-efficient routes based on the vehicle's fuel economy data.

[0075] The vehicle information input unit can suggest routes that put less strain on the vehicle based on the vehicle's maintenance history. For example, it can analyze the vehicle's maintenance history and, if there have been engine problems in the past, suggest a route that avoids steep slopes. It can also select the optimal route based on the vehicle's condition based on the maintenance history. For example, if the tires are worn out, it can prioritize roads with good pavement. Furthermore, it can automatically select a route that puts less strain on the vehicle by taking the maintenance history into consideration. For example, if the brakes are worn out, it can suggest a route with fewer traffic lights. This makes it possible to suggest routes that put less strain on the vehicle based on the vehicle's maintenance history.

[0076] The vehicle information input unit can automatically input vehicle information using on-board cameras and sensors, reducing the user's workload. For example, a system can be built that automatically collects vehicle information using on-board cameras and sensors. For example, a camera can capture images of the vehicle's exterior and interior conditions and analyze the data. Sensors can also be used to monitor the vehicle's condition in real time and suggest the optimal route based on that information. For example, sensors can measure tire pressure and engine temperature. Furthermore, a system can be developed that automatically inputs vehicle information using on-board cameras and sensors. For example, the vehicle's mileage and remaining fuel level can be automatically detected and registered in a database. This makes it possible to automatically input vehicle information using on-board cameras and sensors, reducing the user's workload.

[0077] The user information input unit can use the emotion estimation function to analyze the user's current emotional state and suggest relaxing or exciting detour spots. For example, the emotion estimation function is used to analyze the user's current emotional state. For example, if the user is feeling stressed, a relaxing hot spring or park can be suggested. The user's emotional state can also be monitored in real time and a detour spot can be suggested based on the results. For example, if the user is excited, an exciting amusement park can be suggested. Furthermore, a system can be constructed that suggests spots according to the user's emotional state based on the emotion estimation data. For example, if the user is tired, a relaxing cafe or rest spot can be suggested. This makes it possible to suggest relaxing or exciting spots based on the user's emotional state.

[0078] The user information input unit can use the emotion estimation function to analyze the emotion of the user when entering information in real time and provide an interface that elicits positive emotions. For example, the emotion estimation function is used to analyze the emotion of the user when entering information in real time. For example, if the user is feeling stressed, an interface that helps the user relax is provided. Furthermore, an interface that analyzes the user's emotional state and elicits positive emotions is provided. For example, animations and sounds that make the user smile can be used. Furthermore, a system is constructed that dynamically adjusts the interface according to the user's emotional state based on the emotion estimation data. For example, if the user is tired, a simple and intuitive interface can be provided. This makes it possible to analyze the emotion of the user when entering information in real time and provide an interface that elicits positive emotions.

[0079] The vehicle information input unit can use the emotion estimation function to analyze the driver's stress level and suggest a route that reduces stress. For example, the emotion estimation function can be used to analyze the driver's stress level in real time. For example, if stress is high, a route that avoids congestion can be suggested. In addition, a system can be built to select a route that reduces stress based on the driver's stress level. For example, a scenic route or relaxing music can be suggested. Furthermore, based on the emotion estimation data, the optimal route according to the driver's stress level can be automatically selected. For example, if stress is low, the shortest route can be suggested. This makes it possible to suggest a route that reduces stress based on the driver's stress level.

[0080] The real-time information linking unit can use the emotion estimation function to analyze the user's emotional state and provide real-time information for reducing stress. For example, the emotion estimation function can be used to analyze the user's emotional state in real time and suggest relaxing music or scenic routes. Furthermore, a system can be constructed that provides real-time information for reducing stress based on the user's emotional state. For example, it can suggest detour routes or rest spots to avoid crowds. Furthermore, based on the emotion estimation data, real-time information according to the user's emotional state can be provided. For example, if the user is tired, it can provide information on nearby rest spots or cafes. In this way, real-time information for reducing stress can be provided based on the user's emotional state.

[0081] The real-time information linking unit can also link with other modes of transportation to propose combined modes of transportation. For example, a system can be built that links with other modes of transportation (public transportation and shared ride services) based on real-time information to propose combined modes of transportation. For example, it can propose optimal transfer routes based on train and bus operation information. It can also analyze real-time information from public transportation and shared ride services to propose optimal modes of transportation. For example, it can propose alternative bus routes based on train delay information. Furthermore, a system that strengthens linking with other modes of transportation will be developed to propose combined modes of transportation. For example, it can check the availability of shared ride services in real time and propose the optimal mode of transportation. This makes it possible to link with other modes of transportation to propose combined modes of transportation.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The user information input unit inputs user information. For example, the user can input information such as past routes, stopover spots, their profile, and what they want to do on the way to their destination. The user information input unit also allows the generation AI to analyze the user's input information and learn specific patterns and preferences. Step 2: The vehicle information input unit inputs vehicle information. For example, the user can input information such as the vehicle model, size, and driving history. The vehicle information input unit also allows the generation AI to analyze the vehicle information and generate an optimal route that takes into account road width and driving difficulty. Step 3: The real-time information linking unit links real-time information. For example, the generation AI can link destination parking information and road traffic information in real time and update them as needed. Step 4: The detour suggestion unit suggests detour spots based on the information entered by the user information input unit and the vehicle information input unit. For example, the generation AI can suggest parks where children can have fun or cafes where people can take a break based on conditions such as "when there is a family of four with a five-year-old child and it looks like it will take more than three hours." Step 5: The voice dialogue unit conducts a voice dialogue based on the information linked by the real-time information linking unit. For example, if a user asks, "Where is the next rest spot?", the generation AI can respond with, "The next rest spot is a park that you will arrive at in 10 minutes."

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 user information input unit for inputting user information; a vehicle information input unit for inputting vehicle information; a real-time information sharing unit that shares real-time information; a detour suggestion unit that suggests detour spots based on the information input by the user information input unit and the vehicle information input unit; a voice dialogue unit that performs voice dialogue based on the information linked by the real-time information linking unit; A system characterized by:

2. The user information input unit The user information can be input using gestures and eye tracking in addition to voice, providing a more intuitive interface.

2. The system of claim 1.

3. The vehicle information input unit Analyzing vehicle fuel consumption data and proposing fuel-efficient routes 2. The system of claim 1.

4. The real-time information linking unit Based on the information provided in real time, the safety of the route is evaluated and the safe route is preferentially proposed.

2. The system of claim 1.

5. The user information input unit Analyzing the user's current emotional state and suggesting relaxing or exciting detour spots 2. The system of claim 1.

6. The vehicle information input unit Analyzes driver stress levels and suggests routes that reduce stress 2. The system of claim 1.

7. The real-time information linking unit Analyzing the user's emotional state and providing real-time information to reduce stress 2. The system of claim 1.

8. The real-time information linking unit Analyzing the user's emotional response to the real-time information and providing the information that elicits positive emotions preferentially 2. The system of claim 1.

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