System

Abstract

An object of a system according to an embodiment is to propose an individualized optimal route based on driving data of a driver.SOLUTION: A system according to an embodiment includes a driving data collection unit, a driving pattern learning unit, and a route suggestion unit. The driving data collection unit collects driving data of a driver. The driving pattern learning unit learns a driving pattern based on the driving data collected by the driving data collection unit. The route proposal unit proposes an optimal route to the driver based on the driving pattern learned by the driving pattern learning 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 propose optimal routes that take into account the driving skills and personalities of individual drivers, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal route that is personalized based on the driver's driving data. [Means for solving the problem]

[0006] The system according to the embodiment includes a driving data collection unit, a driving pattern learning unit, and a route proposing unit. The driving data collection unit collects driving data of a driver. The driving pattern learning unit learns driving patterns based on the driving data collected by the driving data collection unit. The route proposing unit proposes an optimal route to the driver based on the driving patterns learned by the driving pattern learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal route that is personalized based on the driver's driving data. [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 next-generation navigation system according to an embodiment of the present invention is a system that combines generative AI with a car navigation system. This system employs a vast amount of traffic data updated in real time and AI that learns from the driver's past driving patterns. This allows the next-generation navigation system to analyze the driving skills and personality of each individual driver and provide optimal route guidance at the lane level based on that analysis.

[0029] A next-generation navigation system according to an embodiment includes a driving data collection unit, a driving pattern learning unit, and a route suggestion unit. The driving data collection unit collects driving data of the driver. For example, the driving data collection unit collects data such as speed, braking frequency, and steering operation using sensors installed in the vehicle. The driving data collection unit can also acquire GPS data from the driver's smartphone. For example, the driving data collection unit collects driving data through the driver's smartphone app. The driving pattern learning unit learns driving patterns based on the driving data collected by the driving data collection unit. For example, the driving pattern learning unit analyzes the driver's driving style using a generative AI. The driving pattern learning unit can also learn driving patterns taking into account the driver's driving habits and driving environment. For example, the driving pattern learning unit learns driving patterns based on the driver's mileage and average speed. The route suggestion unit suggests an optimal route for the driver based on the driving pattern learned by the driving pattern learning unit. For example, if the driver dislikes lane changes, the route suggestion unit suggests a route with fewer lane changes. Furthermore, when a driver actively changes lanes, the route suggestion unit can also advise the driver on the timing of lane changes to safely and quickly reach the destination. For example, the route suggestion unit suggests an optimal route based on real-time traffic data. As a result, the next-generation navigation system according to the embodiment can suggest an optimal route based on the driver's driving data, enabling individually customized navigation.

[0030] The driving data collection unit can collect biometric data of the driver and learn driving patterns that take into account the driver's stress level and concentration level. For example, the driving data collection unit measures the driver's heart rate and galvanic skin response using sensors installed in the vehicle to collect the driver's biometric data. For example, the driving data collection unit measures the driver's heart rate in real time using a heart rate sensor. The driving data collection unit can also measure the driver's galvanic skin response using a galvanic skin response sensor. For example, the driving data collection unit estimates the driver's stress level based on changes in the galvanic skin response. The driving data collection unit can also measure the driver's respiratory rate and analyze the driver's concentration level. For example, the driving data collection unit measures the driver's respiratory rate using a respiratory sensor to evaluate the driver's concentration level. This allows the system to learn the driver's driving patterns based on the driver's biometric data, thereby enabling more individually customized route suggestions.

[0031] The driving data collection unit can analyze the voice commands and utterances of the driver while driving and learn patterns that reflect the driver's driving style and preferences. The driving data collection unit, for example, builds a system that collects and analyzes the voice commands and utterances of the driver while driving. For example, the driving data collection unit converts the driver's utterances into text using voice recognition technology and learns patterns that reflect the driver's driving style and preferences. The driving data collection unit can also analyze the driver's utterances using natural language processing technology. For example, the driving data collection unit extracts keywords from the driver's utterances and analyzes the driving style. The driving data collection unit can also analyze the driver's voice commands and learn the driver's driving style and preferences. For example, the driving data collection unit analyzes the frequency and type of the driver's voice commands to understand the driver's driving style. This allows the system to learn driving patterns based on the driver's voice commands and utterances, thereby enabling more individually customized route suggestions.

[0032] The driving pattern learning unit can apply the driver's driving patterns to other means of transportation and learn comprehensive travel patterns. The driving pattern learning unit, for example, collects bicycle and walking data and learns comprehensive travel patterns in order to apply the driver's driving patterns to other means of transportation. For example, the driving pattern learning unit collects bicycle travel data and integrates it with the driving patterns. The driving pattern learning unit can also collect walking travel data and integrate it with the driving patterns. For example, the driving pattern learning unit learns driving patterns based on walking travel data. The driving pattern learning unit can also collect public transportation usage data and integrate it with the driving patterns. For example, the driving pattern learning unit learns driving patterns based on bus and train usage data. This makes it possible to apply the driver's driving patterns to other means of transportation and provide comprehensive travel guidance.

[0033] The driving pattern learning unit can share driving patterns of family and friends and integrate data of multiple drivers to propose an optimal route. For example, in order to share driving patterns of family and friends, the driving pattern learning unit stores driving data in the cloud and builds a system that integrates data of multiple drivers. For example, the driving pattern learning unit integrates driving data of all family members and proposes an optimal route. The driving pattern learning unit can also integrate driving data of friends and learn driving patterns. For example, the driving pattern learning unit learns driving patterns based on driving data of friends. The driving pattern learning unit can also integrate data of multiple drivers and learn driving patterns. For example, the driving pattern learning unit learns driving patterns based on driving data of family and friends. In this way, sharing driving patterns of family and friends enables more accurate route proposals.

[0034] The route suggestion unit can analyze the driver's past route selection history, identify preferred routes and routes to avoid, and customize the route. The route suggestion unit, for example, collects the driver's past route selection history and builds a system that identifies preferred routes and routes to avoid. For example, the route suggestion unit analyzes past route data and identifies the driver's preferences and routes to avoid. The route suggestion unit can also suggest preferred routes based on the driver's route selection history. For example, the route suggestion unit suggests preferred routes based on frequently selected routes. The route suggestion unit can also identify routes that the driver wants to avoid and make suggestions to avoid those routes. For example, the route suggestion unit makes suggestions to avoid routes with heavy traffic or dangerous routes. This makes it possible to suggest more individually customized routes by identifying the driver's preferences and routes to avoid based on the past route selection history.

[0035] The route suggestion unit can suggest a relaxing route by taking into consideration the driver's music and radio selections while driving. The route suggestion unit, for example, collects the driver's music and radio selections while driving and builds a system that suggests a relaxing route. For example, the route suggestion unit analyzes music data from driving and suggests a relaxing route. The route suggestion unit can also suggest a relaxing route based on the driver's radio selections. For example, the route suggestion unit suggests a relaxing route based on the driver's preferred music genre or radio program. The route suggestion unit can also suggest a route that reduces stress while driving by taking into consideration the driver's music and radio selections. For example, the route suggestion unit suggests a scenic route while playing music that the driver finds relaxing. In this way, it is possible to suggest a route that helps the driver relax by taking into consideration the driver's music and radio selections while driving.

[0036] The route suggestion unit can apply the driver's customized route guidance to other means of transportation to provide comprehensive travel guidance. For example, the route suggestion unit collects data on public transportation and bicycles to build a system that provides comprehensive travel guidance in order to apply the driver's customized route guidance to other means of transportation. For example, the route suggestion unit integrates public transportation timetables and bicycle route data. The route suggestion unit can also apply the driver's customized route guidance to other means of transportation based on the driver's customized route guidance. For example, the route suggestion unit proposes public transportation and bicycle routes taking into account the driver's preferences and driving style. The route suggestion unit can also provide comprehensive travel guidance based on the driver's customized route guidance. For example, the route suggestion unit proposes the optimal means of transportation based on the driver's driving data. This enables comprehensive travel guidance to be provided by applying the driver's customized route guidance to other means of transportation.

[0037] The route suggestion unit can also apply the customized route guidance to travel and sightseeing planning and suggest optimal sightseeing routes. For example, the route suggestion unit collects data on tourist destinations and builds a system that suggests optimal sightseeing routes in order to apply the customized route guidance to travel and sightseeing planning. For example, the route suggestion unit integrates information on popular spots and events at tourist destinations. The route suggestion unit can also plan trips and sightseeing based on the driver's customized route guidance. For example, the route suggestion unit proposes a sightseeing route taking into account the driver's preferences and interests. The route suggestion unit can also propose an optimal sightseeing route based on the driver's customized route guidance. For example, the route suggestion unit proposes a route that goes around tourist attractions based on the driver's driving data. This allows the customized route guidance to be applied to travel and sightseeing planning, enabling a more fulfilling sightseeing experience.

[0038] The route suggestion unit can integrate real-time weather data and suggest an optimal route depending on the weather. The route suggestion unit, for example, collects real-time weather data and builds a system that suggests an optimal route depending on the weather. For example, the route suggestion unit collects weather data such as temperature, precipitation, and wind speed, and suggests a route depending on the weather conditions. The route suggestion unit can also suggest an optimal route to the driver based on the real-time weather data. For example, the route suggestion unit suggests a route that avoids slippery roads in rainy weather. The route suggestion unit can also provide route guidance to ensure the driver's safety based on the weather data. For example, the route suggestion unit suggests a route that is less affected by wind in strong winds. This allows for a safer and more comfortable driving experience by suggesting an optimal route depending on the weather based on real-time weather data.

[0039] The route suggestion unit can utilize real-time parking information to suggest a route that takes into account the availability of parking lots around the destination. The route suggestion unit, for example, collects real-time parking information and builds a system that suggests a route that takes into account the availability of parking lots around the destination. For example, the route suggestion unit displays the availability of parking lots in real time and suggests the optimal parking lot. The route suggestion unit can also suggest the optimal parking lot to the driver based on parking fee information. For example, the route suggestion unit displays the parking fee information in real time and suggests a cost-effective parking lot. The route suggestion unit can also suggest a parking lot around the destination based on the availability of parking lots. For example, the route suggestion unit suggests the optimal parking lot by taking into account the distance from the destination. In this way, the stress of parking can be reduced by suggesting a route that takes into account the availability of parking lots around the destination based on real-time parking information.

[0040] The route suggestion unit can link real-time traffic data with traffic systems of other cities or countries to provide international route guidance. The route suggestion unit, for example, links real-time traffic data with traffic systems of other cities or countries to build a system that provides international route guidance. For example, the route suggestion unit integrates international traffic data to propose an optimal route. The route suggestion unit can also link with traffic systems of other cities or countries to propose an optimal route to a driver. For example, the route suggestion unit provides international route guidance based on traffic data of other cities or countries. The route suggestion unit can also propose an optimal route to a driver based on international traffic data. For example, the route suggestion unit provides route guidance across borders. In this way, international route guidance becomes possible by linking real-time traffic data with traffic systems of other cities or countries.

[0041] The route proposal unit can apply real-time traffic data to logistics and delivery operations to propose an optimal delivery route. The route proposal unit, for example, applies real-time traffic data to logistics and delivery operations to build a system that proposes an optimal delivery route. For example, the route proposal unit proposes an optimal delivery route based on traffic data. The route proposal unit can also propose an optimal delivery route based on logistics and delivery operation data. For example, the route proposal unit proposes a time-efficient delivery route based on delivery operation data. The route proposal unit can also propose a cost-efficient delivery route based on real-time traffic data. For example, the route proposal unit proposes a fuel-efficient delivery route based on traffic data. The route proposal unit can also propose an optimal delivery route based on logistics and delivery operation data. For example, the route proposal unit proposes a route that efficiently travels around multiple delivery destinations based on delivery operation data. In this way, by applying real-time traffic data to logistics and delivery operations, it is possible to propose an optimal delivery route.

[0042] The route suggestion unit can integrate data from other car navigation systems in real time to provide the most reliable information. For example, the route suggestion unit integrates data from other car navigation systems in real time to build a system that provides the most reliable information. For example, the route suggestion unit integrates data from multiple car navigation systems to provide highly reliable information. The route suggestion unit can also propose an optimal route to a driver based on data from other car navigation systems. For example, the route suggestion unit proposes an optimal route based on data from other car navigation systems. The route suggestion unit can also provide real-time traffic information based on data from other car navigation systems. For example, the route suggestion unit provides congestion information and accident information based on data from other car navigation systems. In this way, the most reliable information can be provided by integrating data from other car navigation systems in real time.

[0043] The route suggestion unit can share the driver's past driving data by cooperating with other car navigation systems, thereby realizing more accurate route guidance. The route suggestion unit, for example, builds a system for sharing the driver's past driving data by cooperating with other car navigation systems. For example, the route suggestion unit integrates data from multiple car navigation systems to provide highly accurate route guidance. The route suggestion unit can also propose an optimal route based on the driver's past driving data by cooperating with other car navigation systems. For example, the route suggestion unit proposes an optimal route based on data from other car navigation systems. The route suggestion unit can also provide real-time traffic information based on the driver's past driving data by cooperating with other car navigation systems. For example, the route suggestion unit provides congestion information and accident information based on data from other car navigation systems. In this way, by cooperating with other car navigation systems, the driver's past driving data can be shared, thereby enabling more accurate route guidance.

[0044] The route suggestion unit can extend cooperation with other car navigation systems to include public transportation and bicycle sharing systems to provide comprehensive travel guidance. The route suggestion unit, for example, extends cooperation with other car navigation systems to include public transportation and bicycle sharing systems to build a system that provides comprehensive travel guidance. For example, the route suggestion unit integrates data on public transportation timetables and bicycle sharing services. The route suggestion unit can also integrate data on public transportation and bicycle sharing systems based on cooperation with other car navigation systems. For example, the route suggestion unit provides optimal travel guidance based on data on public transportation timetables and bicycle sharing services. The route suggestion unit can also provide comprehensive travel guidance based on cooperation with other car navigation systems. For example, the route suggestion unit provides route guidance that combines multiple means of transportation. As a result, comprehensive travel guidance can be provided by extending cooperation with other car navigation systems to include public transportation and bicycle sharing systems.

[0045] The route suggestion unit can integrate collaboration with other car navigation systems with the infrastructure of a smart city to optimize traffic throughout the city. For example, the route suggestion unit integrates collaboration with other car navigation systems with the infrastructure of a smart city to build a system for optimizing traffic throughout the city. For example, the route suggestion unit integrates traffic data from the smart city to propose an optimal route. The route suggestion unit can also integrate with the infrastructure of a smart city based on collaboration with other car navigation systems. For example, the route suggestion unit collaborates with the traffic signal system and parking management system of the smart city to propose an optimal route. The route suggestion unit can also optimize traffic throughout the city based on collaboration with other car navigation systems. For example, the route suggestion unit provides route guidance to disperse traffic volume and alleviate congestion. As a result, integrating collaboration with other car navigation systems with the infrastructure of a smart city makes it possible to optimize traffic throughout the city.

[0046] The route suggestion unit can play music or podcasts that the driver likes, providing a relaxing environment. The route suggestion unit, for example, builds a system that cooperates with an in-vehicle audio system to play music or podcasts that the driver likes. For example, the route suggestion unit plays relaxing content based on the driver's music playlist or podcast history. The route suggestion unit can also provide a relaxing environment based on the driver's favorite music or podcasts. For example, the route suggestion unit plays music genres or podcast episodes that the driver likes. The route suggestion unit can also provide an environment that reduces stress while driving based on the driver's favorite music or podcasts. For example, the route suggestion unit can suggest a scenic route while playing relaxing music for the driver. In this way, a relaxing environment can be provided by playing music or podcasts that the driver likes.

[0047] The route suggestion unit can apply the stress-free driving experience to other modes of transportation to improve the overall travel experience. For example, the route suggestion unit collects data on public transportation and bicycles to build a system that improves the overall travel experience in order to apply the stress-free driving experience to other modes of transportation. For example, the route suggestion unit integrates public transportation timetables and bicycle route data. The route suggestion unit can also apply the stress-free driving experience to other modes of transportation based on the stress-free driving experience. For example, the route suggestion unit proposes public transportation and bicycle routes taking into account the driver's preferences and driving style. The route suggestion unit can also provide a comprehensive travel experience based on the stress-free driving experience. For example, the route suggestion unit proposes the optimal mode of transportation based on the driver's driving data. In this way, the stress-free driving experience can be applied to other modes of transportation to improve the overall travel experience.

[0048] The route suggestion unit can apply the stress-free driving experience to travel and sightseeing planning and suggest relaxing sightseeing routes. For example, in order to apply the stress-free driving experience to travel and sightseeing planning, the route suggestion unit collects data on tourist destinations and builds a system that suggests relaxing sightseeing routes. For example, the route suggestion unit integrates information on popular spots and events in tourist destinations. The route suggestion unit can also plan trips and sightseeing based on the stress-free driving experience. For example, the route suggestion unit proposes sightseeing routes taking into account the driver's preferences and interests. The route suggestion unit can also propose optimal sightseeing routes based on the stress-free driving experience. For example, the route suggestion unit proposes a route that goes around tourist attractions based on the driver's driving data. In this way, applying the stress-free driving experience to travel and sightseeing planning can enable a more fulfilling sightseeing experience.

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

[0050] The driving data collection unit collects gaze data of the driver while driving and can analyze driving patterns in more detail based on the gaze movements. For example, the driving data collection unit analyzes the driver's gaze movements using an eye-tracking camera installed in the vehicle. The driving data collection unit can also evaluate the driver's attention and concentration level from the gaze movements. For example, the driving data collection unit estimates the driver's attention by analyzing the gaze fixation time and gaze movement patterns. The driving data collection unit can also analyze the relationship between gaze movements and driving behavior to learn driving patterns. For example, the driving data collection unit learns driving patterns by analyzing gaze movements and the timing of braking and steering operations. This enables more accurate route suggestions by analyzing driving patterns in detail based on the driver's gaze data.

[0051] The driving data collection unit can collect the music and radio selections made by the driver while driving and learn patterns that reflect the driver's driving style and preferences. For example, the driving data collection unit analyzes music data played while driving to learn the driver's driving style and preferences. The driving data collection unit can also learn the driver's driving style and preferences based on the driver's radio selections. For example, the driving data collection unit analyzes the driver's driving style based on the driver's preferred music genres and radio programs. The driving data collection unit can also learn driving patterns to reduce stress while driving, taking into account the driver's music and radio selections. For example, the driving data collection unit learns the driver's driving patterns when playing relaxing music. This allows the system to learn driving patterns based on the driver's music and radio selections, thereby enabling more individually customized route suggestions.

[0052] The driving pattern learning unit can apply the driver's driving patterns to other means of transportation and learn comprehensive travel patterns. For example, the driving pattern learning unit collects data on bicycles and walking and learns comprehensive travel patterns. For example, the driving pattern learning unit collects bicycle travel data and integrates it with driving patterns. The driving pattern learning unit can also collect walking travel data and integrate it with driving patterns. For example, the driving pattern learning unit learns driving patterns based on walking travel data. The driving pattern learning unit can also collect public transportation usage data and integrate it with driving patterns. For example, the driving pattern learning unit learns driving patterns based on bus and train usage data. In this way, comprehensive travel guidance can be provided by applying the driver's driving patterns to other means of transportation.

[0053] The driving pattern learning unit can share driving patterns of family and friends and integrate data of multiple drivers to propose an optimal route. For example, in order to share driving patterns of family and friends, the driving pattern learning unit stores driving data in the cloud and builds a system that integrates data of multiple drivers. For example, the driving pattern learning unit integrates driving data of all family members and proposes an optimal route. The driving pattern learning unit can also integrate driving data of friends and learn driving patterns. For example, the driving pattern learning unit learns driving patterns based on the driving data of friends. The driving pattern learning unit can also integrate data of multiple drivers and learn driving patterns. For example, the driving pattern learning unit learns driving patterns based on the driving data of family and friends. In this way, sharing driving patterns of family and friends enables more accurate route proposals.

[0054] The route suggestion unit can analyze the driver's past route selection history, identify preferred routes and routes to avoid, and customize the route. For example, the route suggestion unit collects the driver's past route selection history and builds a system that identifies preferred routes and routes to avoid. For example, the route suggestion unit analyzes past route data and identifies the driver's preferences and routes to avoid. The route suggestion unit can also suggest preferred routes based on the driver's route selection history. For example, the route suggestion unit suggests preferred routes based on frequently selected routes. The route suggestion unit can also identify routes that the driver wants to avoid and make suggestions to avoid those routes. For example, the route suggestion unit makes suggestions to avoid routes with heavy traffic or dangerous routes. This makes it possible to identify the driver's preferences and routes to avoid based on the past route selection history, thereby making it possible to suggest more individually customized routes.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The driving data collection unit collects the driver's driving data. For example, it uses sensors installed in the vehicle to collect data such as speed, frequency of braking, and steering operation. It can also obtain GPS data from the driver's smartphone. This also includes collecting driving data through the driver's smartphone app. Step 2: The driving pattern learning unit learns driving patterns based on the driving data collected by the driving data collection unit. For example, the generation AI is used to analyze the driver's driving style and learn driving patterns taking into account the driver's driving habits and driving environment. This also includes learning driving patterns based on the driver's driving distance and average speed. Step 3: The route suggestion unit suggests the optimal route to the driver based on the driving patterns learned by the driving pattern learning unit. For example, if the driver dislikes lane changes, it can suggest a route with fewer lane changes. If the driver changes lanes aggressively, it can teach the timing of lane changes to reach the destination safely and quickly. This also includes suggesting the optimal route based on real-time traffic data.

[0057] (Example 2) The next-generation navigation system according to an embodiment of the present invention is a system that combines generative AI with a car navigation system. This system employs a vast amount of traffic data updated in real time and AI that learns from the driver's past driving patterns. This allows the next-generation navigation system to analyze the driving skills and personality of each individual driver and provide optimal route guidance at the lane level based on that analysis.

[0058] A next-generation navigation system according to an embodiment includes a driving data collection unit, a driving pattern learning unit, and a route suggestion unit. The driving data collection unit collects driving data of the driver. For example, the driving data collection unit collects data such as speed, braking frequency, and steering operation using sensors installed in the vehicle. The driving data collection unit can also acquire GPS data from the driver's smartphone. For example, the driving data collection unit collects driving data through the driver's smartphone app. The driving pattern learning unit learns driving patterns based on the driving data collected by the driving data collection unit. For example, the driving pattern learning unit analyzes the driver's driving style using a generative AI. The driving pattern learning unit can also learn driving patterns taking into account the driver's driving habits and driving environment. For example, the driving pattern learning unit learns driving patterns based on the driver's mileage and average speed. The route suggestion unit suggests an optimal route for the driver based on the driving pattern learned by the driving pattern learning unit. For example, if the driver dislikes lane changes, the route suggestion unit suggests a route with fewer lane changes. Furthermore, when a driver actively changes lanes, the route suggestion unit can also advise the driver on the timing of lane changes to safely and quickly reach the destination. For example, the route suggestion unit suggests an optimal route based on real-time traffic data. As a result, the next-generation navigation system according to the embodiment can suggest an optimal route based on the driver's driving data, enabling individually customized navigation.

[0059] The driving data collection unit can collect emotional data of the driver and analyze driving patterns in more detail based on emotional fluctuations. For example, to collect the emotional data of the driver, the driving data collection unit analyzes facial expressions and voice tone using a camera or sensor installed in the vehicle. For example, the driving data collection unit analyzes the driver's emotions using facial expression recognition technology. The driving data collection unit can also analyze the driver's emotions using voice analysis technology. For example, the driving data collection unit analyzes the tone and speed of the driver's voice to infer emotions. The driving data collection unit can also collect heart rate and electrodermal activity using a sensor to analyze emotional fluctuations. For example, the driving data collection unit infers the driver's emotions based on heart rate fluctuations. This enables more accurate route suggestions by analyzing driving patterns in detail based on the driver's emotional data.

[0060] The driving data collection unit can collect biometric data of the driver and learn driving patterns that take into account the driver's stress level and concentration level. For example, the driving data collection unit measures the driver's heart rate and galvanic skin response using sensors installed in the vehicle to collect the driver's biometric data. For example, the driving data collection unit measures the driver's heart rate in real time using a heart rate sensor. The driving data collection unit can also measure the driver's galvanic skin response using a galvanic skin response sensor. For example, the driving data collection unit estimates the driver's stress level based on changes in the galvanic skin response. The driving data collection unit can also measure the driver's respiratory rate and analyze the driver's concentration level. For example, the driving data collection unit measures the driver's respiratory rate using a respiratory sensor to evaluate the driver's concentration level. This allows the system to learn the driver's driving patterns based on the driver's biometric data, thereby enabling more individually customized route suggestions.

[0061] The driving data collection unit can analyze the voice commands and utterances of the driver while driving and learn patterns that reflect the driver's driving style and preferences. The driving data collection unit, for example, builds a system that collects and analyzes the voice commands and utterances of the driver while driving. For example, the driving data collection unit converts the driver's utterances into text using voice recognition technology and learns patterns that reflect the driver's driving style and preferences. The driving data collection unit can also analyze the driver's utterances using natural language processing technology. For example, the driving data collection unit extracts keywords from the driver's utterances and analyzes the driving style. The driving data collection unit can also analyze the driver's voice commands and learn the driver's driving style and preferences. For example, the driving data collection unit analyzes the frequency and type of the driver's voice commands to understand the driver's driving style. This allows the system to learn driving patterns based on the driver's voice commands and utterances, thereby enabling more individually customized route suggestions.

[0062] The driving pattern learning unit can apply the driver's driving patterns to other means of transportation and learn comprehensive travel patterns. The driving pattern learning unit, for example, collects bicycle and walking data and learns comprehensive travel patterns in order to apply the driver's driving patterns to other means of transportation. For example, the driving pattern learning unit collects bicycle travel data and integrates it with the driving patterns. The driving pattern learning unit can also collect walking travel data and integrate it with the driving patterns. For example, the driving pattern learning unit learns driving patterns based on walking travel data. The driving pattern learning unit can also collect public transportation usage data and integrate it with the driving patterns. For example, the driving pattern learning unit learns driving patterns based on bus and train usage data. This makes it possible to apply the driver's driving patterns to other means of transportation and provide comprehensive travel guidance.

[0063] The driving pattern learning unit can share driving patterns of family and friends and integrate data of multiple drivers to propose an optimal route. For example, in order to share driving patterns of family and friends, the driving pattern learning unit stores driving data in the cloud and builds a system that integrates data of multiple drivers. For example, the driving pattern learning unit integrates driving data of all family members and proposes an optimal route. The driving pattern learning unit can also integrate driving data of friends and learn driving patterns. For example, the driving pattern learning unit learns driving patterns based on driving data of friends. The driving pattern learning unit can also integrate data of multiple drivers and learn driving patterns. For example, the driving pattern learning unit learns driving patterns based on driving data of family and friends. In this way, sharing driving patterns of family and friends enables more accurate route proposals.

[0064] The driving pattern learning unit uses the emotion estimation function to learn driving patterns based on the driver's emotions and provide route guidance according to the emotions. The driving pattern learning unit, for example, uses the emotion estimation function to build a system that learns driving patterns based on the driver's emotions. For example, the driving pattern learning unit collects emotional data while driving and learns driving patterns according to the emotions. The driving pattern learning unit can also use the emotion estimation function to provide route guidance based on the driver's emotions. For example, the driving pattern learning unit suggests a relaxing route based on the driver's emotional data. The driving pattern learning unit can also use the emotion estimation function to provide driving advice according to the driver's emotions. For example, the driving pattern learning unit provides driving advice to reduce stress based on the driver's emotional data. In this way, using the emotion estimation function makes it possible to provide route guidance according to the driver's emotions.

[0065] The route suggestion unit can analyze the driver's past route selection history, identify preferred routes and routes to avoid, and customize the route. The route suggestion unit, for example, collects the driver's past route selection history and builds a system that identifies preferred routes and routes to avoid. For example, the route suggestion unit analyzes past route data and identifies the driver's preferences and routes to avoid. The route suggestion unit can also suggest preferred routes based on the driver's route selection history. For example, the route suggestion unit suggests preferred routes based on frequently selected routes. The route suggestion unit can also identify routes that the driver wants to avoid and make suggestions to avoid those routes. For example, the route suggestion unit makes suggestions to avoid routes with heavy traffic or dangerous routes. This makes it possible to suggest more individually customized routes by identifying the driver's preferences and routes to avoid based on the past route selection history.

[0066] The route suggestion unit can suggest a relaxing route by taking into consideration the driver's music and radio selections while driving. The route suggestion unit, for example, collects the driver's music and radio selections while driving and builds a system that suggests a relaxing route. For example, the route suggestion unit analyzes music data from driving and suggests a relaxing route. The route suggestion unit can also suggest a relaxing route based on the driver's radio selections. For example, the route suggestion unit suggests a relaxing route based on the driver's preferred music genre or radio program. The route suggestion unit can also suggest a route that reduces stress while driving by taking into consideration the driver's music and radio selections. For example, the route suggestion unit suggests a scenic route while playing music that the driver finds relaxing. In this way, it is possible to suggest a route that helps the driver relax by taking into consideration the driver's music and radio selections while driving.

[0067] The route suggestion unit can suggest a low-stress route or a scenic route based on the driver's emotional data. The route suggestion unit, for example, collects the driver's emotional data and builds a system that suggests a low-stress route or a scenic route. For example, the route suggestion unit analyzes the emotional data while driving and suggests a low-stress route. The route suggestion unit can also suggest a scenic route based on the driver's emotional data. For example, the route suggestion unit suggests a scenic route that will allow the driver to relax. The route suggestion unit can also provide route guidance to reduce stress based on the driver's emotional data. For example, the route suggestion unit suggests a route with less traffic based on the driver's emotional data. This enables a more comfortable driving experience by suggesting a low-stress route or a scenic route based on the driver's emotional data.

[0068] The route suggestion unit can apply the driver's customized route guidance to other means of transportation to provide comprehensive travel guidance. For example, the route suggestion unit collects data on public transportation and bicycles to build a system that provides comprehensive travel guidance in order to apply the driver's customized route guidance to other means of transportation. For example, the route suggestion unit integrates public transportation timetables and bicycle route data. The route suggestion unit can also apply the driver's customized route guidance to other means of transportation based on the driver's customized route guidance. For example, the route suggestion unit proposes public transportation and bicycle routes taking into account the driver's preferences and driving style. The route suggestion unit can also provide comprehensive travel guidance based on the driver's customized route guidance. For example, the route suggestion unit proposes the optimal means of transportation based on the driver's driving data. This enables comprehensive travel guidance to be provided by applying the driver's customized route guidance to other means of transportation.

[0069] The route suggestion unit can also apply the customized route guidance to travel and sightseeing planning and suggest optimal sightseeing routes. For example, the route suggestion unit collects data on tourist destinations and builds a system that suggests optimal sightseeing routes in order to apply the customized route guidance to travel and sightseeing planning. For example, the route suggestion unit integrates information on popular spots and events at tourist destinations. The route suggestion unit can also plan trips and sightseeing based on the driver's customized route guidance. For example, the route suggestion unit proposes a sightseeing route taking into account the driver's preferences and interests. The route suggestion unit can also propose an optimal sightseeing route based on the driver's customized route guidance. For example, the route suggestion unit proposes a route that goes around tourist attractions based on the driver's driving data. This allows the customized route guidance to be applied to travel and sightseeing planning, enabling a more fulfilling sightseeing experience.

[0070] The route suggestion unit can use the emotion estimation function to provide route guidance based on the driver's emotions and suggest a route that elicits positive emotions. The route suggestion unit, for example, uses the emotion estimation function to build a system that provides route guidance based on the driver's emotions. For example, the route suggestion unit collects emotion data while driving and suggests a route that elicits positive emotions. The route suggestion unit can also use the emotion estimation function to provide route guidance based on the driver's emotions. For example, the route suggestion unit suggests a relaxing route based on the driver's emotion data. The route suggestion unit can also use the emotion estimation function to provide driving advice based on the driver's emotions. For example, the route suggestion unit provides driving advice to elicit positive emotions based on the driver's emotion data. In this way, using the emotion estimation function makes it possible to provide route guidance that is based on the driver's emotions and elicit positive emotions.

[0071] The route suggestion unit can integrate real-time weather data and suggest an optimal route depending on the weather. The route suggestion unit, for example, collects real-time weather data and builds a system that suggests an optimal route depending on the weather. For example, the route suggestion unit collects weather data such as temperature, precipitation, and wind speed, and suggests a route depending on the weather conditions. The route suggestion unit can also suggest an optimal route to the driver based on the real-time weather data. For example, the route suggestion unit suggests a route that avoids slippery roads in rainy weather. The route suggestion unit can also provide route guidance to ensure the driver's safety based on the weather data. For example, the route suggestion unit suggests a route that is less affected by wind in strong winds. This allows for a safer and more comfortable driving experience by suggesting an optimal route depending on the weather based on real-time weather data.

[0072] The route suggestion unit can utilize real-time parking information to suggest a route that takes into account the availability of parking lots around the destination. The route suggestion unit, for example, collects real-time parking information and builds a system that suggests a route that takes into account the availability of parking lots around the destination. For example, the route suggestion unit displays the availability of parking lots in real time and suggests the optimal parking lot. The route suggestion unit can also suggest the optimal parking lot to the driver based on parking fee information. For example, the route suggestion unit displays the parking fee information in real time and suggests a cost-effective parking lot. The route suggestion unit can also suggest a parking lot around the destination based on the availability of parking lots. For example, the route suggestion unit suggests the optimal parking lot by taking into account the distance from the destination. In this way, the stress of parking can be reduced by suggesting a route that takes into account the availability of parking lots around the destination based on real-time parking information.

[0073] The route suggestion unit can optimize a route according to the driver's stress level based on real-time emotional data. The route suggestion unit, for example, collects real-time emotional data and builds a system that optimizes a route according to the driver's stress level. For example, the route suggestion unit analyzes emotional data while driving and suggests a low-stress route. The route suggestion unit can also provide route guidance to reduce stress based on the driver's emotional data. For example, the route suggestion unit suggests a route with low traffic volume based on the driver's emotional data. The route suggestion unit can also suggest a relaxing route based on the driver's emotional data. For example, the route suggestion unit suggests a scenic route based on the driver's emotional data. This makes it possible to optimize a route according to the driver's stress level based on real-time emotional data, thereby enabling a driving experience with reduced stress.

[0074] The route suggestion unit can link real-time traffic data with traffic systems of other cities or countries to provide international route guidance. The route suggestion unit, for example, links real-time traffic data with traffic systems of other cities or countries to build a system that provides international route guidance. For example, the route suggestion unit integrates international traffic data to propose an optimal route. The route suggestion unit can also link with traffic systems of other cities or countries to propose an optimal route to a driver. For example, the route suggestion unit provides international route guidance based on traffic data of other cities or countries. The route suggestion unit can also propose an optimal route to a driver based on international traffic data. For example, the route suggestion unit provides route guidance across borders. In this way, international route guidance becomes possible by linking real-time traffic data with traffic systems of other cities or countries.

[0075] The route proposal unit can apply real-time traffic data to logistics and delivery operations to propose an optimal delivery route. The route proposal unit, for example, applies real-time traffic data to logistics and delivery operations to build a system that proposes an optimal delivery route. For example, the route proposal unit proposes an optimal delivery route based on traffic data. The route proposal unit can also propose an optimal delivery route based on logistics and delivery operation data. For example, the route proposal unit proposes a time-efficient delivery route based on delivery operation data. The route proposal unit can also propose a cost-efficient delivery route based on real-time traffic data. For example, the route proposal unit proposes a fuel-efficient delivery route based on traffic data. The route proposal unit can also propose an optimal delivery route based on logistics and delivery operation data. For example, the route proposal unit proposes a route that efficiently travels around multiple delivery destinations based on delivery operation data. In this way, by applying real-time traffic data to logistics and delivery operations, it is possible to propose an optimal delivery route.

[0076] The route suggestion unit can use the emotion estimation function to provide real-time traffic information based on the driver's emotions and suggest a route that reduces stress. The route suggestion unit, for example, uses the emotion estimation function to build a system that provides real-time traffic information based on the driver's emotions. For example, the route suggestion unit collects emotion data while driving and suggests a route that reduces stress. The route suggestion unit can also use the emotion estimation function to provide real-time traffic information based on the driver's emotions. For example, the route suggestion unit suggests a route with less traffic volume based on the driver's emotion data. The route suggestion unit can also use the emotion estimation function to provide driving advice based on the driver's emotions. For example, the route suggestion unit provides driving advice to reduce stress based on the driver's emotion data. In this way, by using the emotion estimation function, it is possible to provide real-time traffic information based on the driver's emotions and suggest a route that reduces stress.

[0077] The route suggestion unit can integrate data from other car navigation systems in real time to provide the most reliable information. For example, the route suggestion unit integrates data from other car navigation systems in real time to build a system that provides the most reliable information. For example, the route suggestion unit integrates data from multiple car navigation systems to provide highly reliable information. The route suggestion unit can also propose an optimal route to a driver based on data from other car navigation systems. For example, the route suggestion unit proposes an optimal route based on data from other car navigation systems. The route suggestion unit can also provide real-time traffic information based on data from other car navigation systems. For example, the route suggestion unit provides congestion information and accident information based on data from other car navigation systems. In this way, the most reliable information can be provided by integrating data from other car navigation systems in real time.

[0078] The route suggestion unit can share the driver's past driving data by cooperating with other car navigation systems, thereby realizing more accurate route guidance. The route suggestion unit, for example, builds a system for sharing the driver's past driving data by cooperating with other car navigation systems. For example, the route suggestion unit integrates data from multiple car navigation systems to provide highly accurate route guidance. The route suggestion unit can also propose an optimal route based on the driver's past driving data by cooperating with other car navigation systems. For example, the route suggestion unit proposes an optimal route based on data from other car navigation systems. The route suggestion unit can also provide real-time traffic information based on the driver's past driving data by cooperating with other car navigation systems. For example, the route suggestion unit provides congestion information and accident information based on data from other car navigation systems. In this way, by cooperating with other car navigation systems, the driver's past driving data can be shared, thereby enabling more accurate route guidance.

[0079] The route suggestion unit can share driver's emotional data through cooperation with other car navigation systems and provide route guidance based on the driver's emotions. The route suggestion unit, for example, builds a system for sharing driver's emotional data through cooperation with other car navigation systems. For example, the route suggestion unit integrates emotional data from multiple car navigation systems and provides emotion-based route guidance. The route suggestion unit can also propose an optimal route based on the driver's emotional data through cooperation with other car navigation systems. For example, the route suggestion unit proposes an optimal route based on the emotional data from other car navigation systems. The route suggestion unit can also provide real-time traffic information based on the driver's emotional data through cooperation with other car navigation systems. For example, the route suggestion unit provides traffic congestion information and accident information based on the emotional data from other car navigation systems. In this way, emotion-based route guidance is possible by sharing the driver's emotional data through cooperation with other car navigation systems.

[0080] The route suggestion unit can extend cooperation with other car navigation systems to include public transportation and bicycle sharing systems to provide comprehensive travel guidance. The route suggestion unit, for example, extends cooperation with other car navigation systems to include public transportation and bicycle sharing systems to build a system that provides comprehensive travel guidance. For example, the route suggestion unit integrates data on public transportation timetables and bicycle sharing services. The route suggestion unit can also integrate data on public transportation and bicycle sharing systems based on cooperation with other car navigation systems. For example, the route suggestion unit provides optimal travel guidance based on data on public transportation timetables and bicycle sharing services. The route suggestion unit can also provide comprehensive travel guidance based on cooperation with other car navigation systems. For example, the route suggestion unit provides route guidance that combines multiple means of transportation. As a result, comprehensive travel guidance can be provided by extending cooperation with other car navigation systems to include public transportation and bicycle sharing systems.

[0081] The route suggestion unit can integrate collaboration with other car navigation systems with the infrastructure of a smart city to optimize traffic throughout the city. For example, the route suggestion unit integrates collaboration with other car navigation systems with the infrastructure of a smart city to build a system for optimizing traffic throughout the city. For example, the route suggestion unit integrates traffic data from the smart city to propose an optimal route. The route suggestion unit can also integrate with the infrastructure of a smart city based on collaboration with other car navigation systems. For example, the route suggestion unit collaborates with the traffic signal system and parking management system of the smart city to propose an optimal route. The route suggestion unit can also optimize traffic throughout the city based on collaboration with other car navigation systems. For example, the route suggestion unit provides route guidance to disperse traffic volume and alleviate congestion. As a result, integrating collaboration with other car navigation systems with the infrastructure of a smart city makes it possible to optimize traffic throughout the city.

[0082] The route suggestion unit can use the emotion estimation function to provide comprehensive travel guidance based on the driver's emotions in cooperation with other car navigation systems. The route suggestion unit, for example, uses the emotion estimation function to build a system that provides comprehensive travel guidance based on the driver's emotions in cooperation with other car navigation systems. For example, the route suggestion unit collects emotion data during driving and provides travel guidance based on the emotions. The route suggestion unit can also use the emotion estimation function to propose an optimal route based on the driver's emotion data in cooperation with other car navigation systems. For example, the route suggestion unit proposes an optimal route based on emotion data from other car navigation systems. The route suggestion unit can also use the emotion estimation function to provide real-time traffic information based on the driver's emotion data in cooperation with other car navigation systems. For example, the route suggestion unit provides traffic congestion information and accident information based on emotion data from other car navigation systems. In this way, the emotion estimation function makes it possible to provide comprehensive travel guidance based on the driver's emotions.

[0083] The route suggestion unit can monitor the driver's stress level in real time and suggest a route that allows the driver to relax when stress levels increase. For example, the route suggestion unit measures the driver's heart rate and galvanic skin response using sensors installed in the vehicle to monitor the driver's stress level in real time. For example, the route suggestion unit measures the driver's heart rate in real time using a heart rate sensor. The route suggestion unit can also measure the driver's galvanic skin response using a galvanic skin response sensor. For example, the route suggestion unit estimates the driver's stress level based on changes in the galvanic skin response. The route suggestion unit can also suggest a relaxing route based on the driver's stress level. For example, the route suggestion unit suggests a scenic route or a route with little traffic when the driver's stress level increases. The route suggestion unit can also adjust the driving environment based on the driver's stress level. For example, the route suggestion unit adjusts the temperature or music in the vehicle when the driver's stress level increases. In this way, by monitoring the driver's stress level in real time, it is possible to suggest a relaxing route when stress levels increase.

[0084] The route suggestion unit can play music or podcasts that the driver likes, providing a relaxing environment. The route suggestion unit, for example, builds a system that cooperates with an in-vehicle audio system to play music or podcasts that the driver likes. For example, the route suggestion unit plays relaxing content based on the driver's music playlist or podcast history. The route suggestion unit can also provide a relaxing environment based on the driver's favorite music or podcasts. For example, the route suggestion unit plays music genres or podcast episodes that the driver likes. The route suggestion unit can also provide an environment that reduces stress while driving based on the driver's favorite music or podcasts. For example, the route suggestion unit can suggest a scenic route while playing relaxing music for the driver. In this way, a relaxing environment can be provided by playing music or podcasts that the driver likes.

[0085] The route suggestion unit can provide driving advice to reduce stress in real time based on the driver's emotional data. The route suggestion unit, for example, collects the driver's emotional data and builds a system that provides driving advice to reduce stress in real time. For example, the route suggestion unit analyzes the emotional data while driving and provides driving advice to help the driver relax. The route suggestion unit can also provide driving advice to reduce stress based on the driver's emotional data. For example, the route suggestion unit provides guidance on improving driving posture and breathing techniques based on the driver's emotional data. The route suggestion unit can also adjust the driving environment based on the driver's emotional data. For example, the route suggestion unit adjusts the temperature and music in the car based on the driver's emotional data. In this way, driving advice to reduce stress can be provided in real time based on the driver's emotional data, enabling a more comfortable driving experience.

[0086] The route suggestion unit can apply the stress-free driving experience to other modes of transportation to improve the overall travel experience. For example, the route suggestion unit collects data on public transportation and bicycles to build a system that improves the overall travel experience in order to apply the stress-free driving experience to other modes of transportation. For example, the route suggestion unit integrates public transportation timetables and bicycle route data. The route suggestion unit can also apply the stress-free driving experience to other modes of transportation based on the stress-free driving experience. For example, the route suggestion unit proposes public transportation and bicycle routes taking into account the driver's preferences and driving style. The route suggestion unit can also provide a comprehensive travel experience based on the stress-free driving experience. For example, the route suggestion unit proposes the optimal mode of transportation based on the driver's driving data. In this way, the stress-free driving experience can be applied to other modes of transportation to improve the overall travel experience.

[0087] The route suggestion unit can apply the stress-free driving experience to travel and sightseeing planning and suggest relaxing sightseeing routes. For example, in order to apply the stress-free driving experience to travel and sightseeing planning, the route suggestion unit collects data on tourist destinations and builds a system that suggests relaxing sightseeing routes. For example, the route suggestion unit integrates information on popular spots and events in tourist destinations. The route suggestion unit can also plan trips and sightseeing based on the stress-free driving experience. For example, the route suggestion unit proposes sightseeing routes taking into account the driver's preferences and interests. The route suggestion unit can also propose optimal sightseeing routes based on the stress-free driving experience. For example, the route suggestion unit proposes a route that goes around tourist attractions based on the driver's driving data. In this way, applying the stress-free driving experience to travel and sightseeing planning can enable a more fulfilling sightseeing experience.

[0088] The route suggestion unit can use the emotion estimation function to provide a stress-free driving experience based on the driver's emotions and suggest a route that elicits positive emotions. The route suggestion unit, for example, uses the emotion estimation function to build a system that provides a stress-free driving experience based on the driver's emotions. For example, the route suggestion unit collects emotion data during driving and suggests a route that elicits positive emotions. The route suggestion unit can also use the emotion estimation function to provide a stress-free driving experience based on the driver's emotions. For example, the route suggestion unit suggests a relaxing route based on the driver's emotion data. The route suggestion unit can also use the emotion estimation function to provide driving advice based on the driver's emotions. For example, the route suggestion unit provides driving advice to elicit positive emotions based on the driver's emotion data. In this way, by using the emotion estimation function, a stress-free driving experience based on the driver's emotions is possible and positive emotions can be elicited.

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

[0090] The driving data collection unit collects gaze data of the driver while driving and can analyze driving patterns in more detail based on the gaze movements. For example, the driving data collection unit analyzes the driver's gaze movements using an eye-tracking camera installed in the vehicle. The driving data collection unit can also evaluate the driver's attention and concentration level from the gaze movements. For example, the driving data collection unit estimates the driver's attention by analyzing the gaze fixation time and gaze movement patterns. The driving data collection unit can also analyze the relationship between gaze movements and driving behavior to learn driving patterns. For example, the driving data collection unit learns driving patterns by analyzing gaze movements and the timing of braking and steering operations. This enables more accurate route suggestions by analyzing driving patterns in detail based on the driver's gaze data.

[0091] The driving data collection unit can collect the music and radio selections made by the driver while driving and learn patterns that reflect the driver's driving style and preferences. For example, the driving data collection unit analyzes music data played while driving to learn the driver's driving style and preferences. The driving data collection unit can also learn the driver's driving style and preferences based on the driver's radio selections. For example, the driving data collection unit analyzes the driver's driving style based on the driver's preferred music genres and radio programs. The driving data collection unit can also learn driving patterns to reduce stress while driving, taking into account the driver's music and radio selections. For example, the driving data collection unit learns the driver's driving patterns when playing relaxing music. This allows the system to learn driving patterns based on the driver's music and radio selections, thereby enabling more individually customized route suggestions.

[0092] The driving pattern learning unit can apply the driver's driving patterns to other means of transportation and learn comprehensive travel patterns. For example, the driving pattern learning unit collects data on bicycles and walking and learns comprehensive travel patterns. For example, the driving pattern learning unit collects bicycle travel data and integrates it with driving patterns. The driving pattern learning unit can also collect walking travel data and integrate it with driving patterns. For example, the driving pattern learning unit learns driving patterns based on walking travel data. The driving pattern learning unit can also collect public transportation usage data and integrate it with driving patterns. For example, the driving pattern learning unit learns driving patterns based on bus and train usage data. In this way, comprehensive travel guidance can be provided by applying the driver's driving patterns to other means of transportation.

[0093] The driving pattern learning unit can share driving patterns of family and friends and integrate data of multiple drivers to propose an optimal route. For example, in order to share driving patterns of family and friends, the driving pattern learning unit stores driving data in the cloud and builds a system that integrates data of multiple drivers. For example, the driving pattern learning unit integrates driving data of all family members and proposes an optimal route. The driving pattern learning unit can also integrate driving data of friends and learn driving patterns. For example, the driving pattern learning unit learns driving patterns based on the driving data of friends. The driving pattern learning unit can also integrate data of multiple drivers and learn driving patterns. For example, the driving pattern learning unit learns driving patterns based on the driving data of family and friends. In this way, sharing driving patterns of family and friends enables more accurate route proposals.

[0094] The route suggestion unit can analyze the driver's past route selection history, identify preferred routes and routes to avoid, and customize the route. For example, the route suggestion unit collects the driver's past route selection history and builds a system that identifies preferred routes and routes to avoid. For example, the route suggestion unit analyzes past route data and identifies the driver's preferences and routes to avoid. The route suggestion unit can also suggest preferred routes based on the driver's route selection history. For example, the route suggestion unit suggests preferred routes based on frequently selected routes. The route suggestion unit can also identify routes that the driver wants to avoid and make suggestions to avoid those routes. For example, the route suggestion unit makes suggestions to avoid routes with heavy traffic or dangerous routes. This makes it possible to identify the driver's preferences and routes to avoid based on the past route selection history, thereby making it possible to suggest more individually customized routes.

[0095] The route suggestion unit can suggest a low-stress route or a scenic route based on the driver's emotional data. For example, the route suggestion unit collects the driver's emotional data and builds a system that suggests a low-stress route or a scenic route. For example, the route suggestion unit analyzes the emotional data while driving and suggests a low-stress route. The route suggestion unit can also suggest a scenic route based on the driver's emotional data. For example, the route suggestion unit suggests a scenic route that will allow the driver to relax. The route suggestion unit can also provide route guidance to reduce stress based on the driver's emotional data. For example, the route suggestion unit suggests a route with less traffic based on the driver's emotional data. This enables a more comfortable driving experience by suggesting a low-stress route or a scenic route based on the driver's emotional data.

[0096] The route suggestion unit can use the emotion estimation function to provide route guidance based on the driver's emotions and suggest a route that elicits positive emotions. For example, the route suggestion unit uses the emotion estimation function to build a system that provides route guidance based on the driver's emotions. For example, the route suggestion unit collects emotion data while driving and suggests a route that elicits positive emotions. The route suggestion unit can also use the emotion estimation function to provide route guidance based on the driver's emotions. For example, the route suggestion unit suggests a relaxing route based on the driver's emotion data. The route suggestion unit can also use the emotion estimation function to provide driving advice based on the driver's emotions. For example, the route suggestion unit provides driving advice to elicit positive emotions based on the driver's emotion data. In this way, using the emotion estimation function makes it possible to provide route guidance that is based on the driver's emotions and elicit positive emotions.

[0097] The route suggestion unit can use the emotion estimation function to provide real-time traffic information based on the driver's emotions and suggest a route that reduces stress. For example, the route suggestion unit uses the emotion estimation function to build a system that provides real-time traffic information based on the driver's emotions. For example, the route suggestion unit collects emotion data while driving and suggests a route that reduces stress. The route suggestion unit can also use the emotion estimation function to provide real-time traffic information based on the driver's emotions. For example, the route suggestion unit suggests a route with less traffic volume based on the driver's emotion data. The route suggestion unit can also use the emotion estimation function to provide driving advice based on the driver's emotions. For example, the route suggestion unit provides driving advice to reduce stress based on the driver's emotion data. In this way, by using the emotion estimation function, it is possible to provide real-time traffic information based on the driver's emotions and suggest a route that reduces stress.

[0098] The route suggestion unit can use the emotion estimation function to provide a stress-free driving experience based on the driver's emotions and suggest a route that elicits positive emotions. For example, the route suggestion unit uses the emotion estimation function to build a system that provides a stress-free driving experience based on the driver's emotions. For example, the route suggestion unit collects emotion data during driving and suggests a route that elicits positive emotions. The route suggestion unit can also use the emotion estimation function to provide a stress-free driving experience based on the driver's emotions. For example, the route suggestion unit suggests a relaxing route based on the driver's emotion data. The route suggestion unit can also use the emotion estimation function to provide driving advice based on the driver's emotions. For example, the route suggestion unit provides driving advice to elicit positive emotions based on the driver's emotion data. In this way, by using the emotion estimation function, a stress-free driving experience based on the driver's emotions is possible and positive emotions can be elicited.

[0099] The route suggestion unit can use the emotion estimation function to provide driving advice based on the driver's emotions in real time. For example, the route suggestion unit collects emotional data of the driver and builds a system that provides emotionally-based driving advice in real time. For example, the route suggestion unit analyzes the emotional data while driving and provides driving advice that helps the driver relax. The route suggestion unit can also provide driving advice to reduce stress based on the emotional data of the driver. For example, the route suggestion unit provides guidance on improving driving posture and breathing techniques based on the emotional data of the driver. The route suggestion unit can also adjust the driving environment based on the emotional data of the driver. For example, the route suggestion unit adjusts the temperature and music in the car based on the emotional data of the driver. This allows for a more comfortable driving experience by providing driving advice to reduce stress in real time based on the emotional data of the driver.

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

[0101] Step 1: The driving data collection unit collects the driver's driving data. For example, it uses sensors installed in the vehicle to collect data such as speed, frequency of braking, and steering operation. It can also obtain GPS data from the driver's smartphone. This also includes collecting driving data through the driver's smartphone app. Step 2: The driving pattern learning unit learns driving patterns based on the driving data collected by the driving data collection unit. For example, the generation AI is used to analyze the driver's driving style and learn driving patterns taking into account the driver's driving habits and driving environment. This also includes learning driving patterns based on the driver's driving distance and average speed. Step 3: The route suggestion unit suggests the optimal route to the driver based on the driving patterns learned by the driving pattern learning unit. For example, if the driver dislikes lane changes, it can suggest a route with fewer lane changes. If the driver changes lanes aggressively, it can teach the timing of lane changes to reach the destination safely and quickly. This also includes suggesting the optimal route based on real-time traffic data.

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

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

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

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

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0136] 7, 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.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0153] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0154] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0155] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0159] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0160] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0161] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0162] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0163] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0164] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0166] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0167] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

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

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a driving data collection unit that collects driving data of a driver; a driving pattern learning unit that learns driving patterns based on the driving data collected by the driving data collecting unit; a route suggestion unit that suggests an optimal route to the driver based on the driving pattern learned by the driving pattern learning unit. A system characterized by:

2. The driving data collection unit Collects driver biometric data and learns driving patterns that take stress levels and concentration into account 2. The system of claim 1.

3. The driving pattern learning unit Applying a driver's driving patterns to other modes of transportation to learn overall travel patterns 2. The system of claim 1.

4. The route suggestion unit Analyze a driver's past route choices to identify and customize preferred and avoided routes 2. The system of claim 1.

5. The route suggestion unit Integrates real-time weather data to suggest optimal routes depending on the weather 2. The system of claim 1.

6. The route suggestion unit Integrates data from other car navigation systems in real time to provide the most reliable information 2. The system of claim 1.

7. The route suggestion unit By linking with other car navigation systems, it provides comprehensive travel guidance based on the driver's emotions.

2. The system of claim 1.

8. The driving data collection unit Collecting driver emotion data and further analyzing driving patterns based on emotional fluctuations 2. The system of claim 1.

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