System

Abstract

An object of a system according to an embodiment is to appropriately propose a route to a facility where right-turn entry is prohibited and estimate a dangerous place.SOLUTION: A system according to an embodiment includes a driving route learning unit, a right-turn entry prohibited route proposal unit, and a risk estimation unit. The driving route learning unit learns driving history data of a user. The right-turn parking prohibited route suggestion unit suggests a route to a right-turn parking prohibited facility based on the driving history data of the user learned by the driving route learning unit. The danger estimation unit estimates a dangerous place based on the route proposed by the right-turn entry prohibited route proposal 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 routes to facilities where right turns are prohibited or estimate dangerous areas, so there is room for improvement.

[0005] The system according to the embodiment aims to appropriately propose routes to facilities where right turns are prohibited and to appropriately estimate dangerous areas. [Means for solving the problem]

[0006] The system according to the embodiment includes a driving route learning unit, a no-right-turn-entry route proposing unit, and a risk estimation unit. The driving route learning unit learns driving history data of a user. The no-right-turn-entry route proposing unit proposes a route to a facility where right turns are prohibited, based on the driving history data of the user learned by the driving route learning unit. The risk estimation unit estimates risk locations based on the route proposed by the no-right-turn-entry route proposing unit. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately propose routes to facilities where right turns are prohibited and estimate dangerous locations. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A car navigation system according to an embodiment of the present invention is a system that learns a user's driving route, and when a route to a facility where right turns are prohibited is proposed, suggests a route that can be reached by turning left. This allows the car navigation system to learn a user's driving route, and when a route to a facility where right turns are prohibited is proposed, suggests a route that can be reached by turning left. Furthermore, by expanding the range of risk estimation, it is possible to aim to propose routes that eliminate all risky driving.

[0029] A car navigation system according to an embodiment includes a driving route learning unit, a no-right-turn-entry route proposing unit, and a risk estimation unit. The driving route learning unit learns a user's driving history data. For example, the driving route learning unit collects the user's past driving history data and identifies frequently used roads and roads to avoid. The driving route learning unit also analyzes the data using a generation AI to understand the user's driving patterns and preferences. The no-right-turn-entry route proposing unit proposes a route to a facility where right-turn entry is prohibited based on the user's driving history data learned by the driving route learning unit. For example, if the destination is a parking lot where right-turn entry is prohibited, the no-right-turn-entry route proposing unit calculates a route that allows left turn entry and proposes it to the user. The risk estimation unit estimates dangerous areas based on the route proposed by the no-right-turn-entry route proposing unit. For example, the risk estimation unit analyzes traffic accident data and road condition data to identify dangerous intersections and sharp curves. The risk estimation unit also calculates a route that avoids these dangerous areas and proposes it to the user. As a result, the car navigation system according to the embodiment can learn the user's driving history data, suggest routes to facilities where right turns are prohibited, and estimate dangerous areas, thereby assisting in safe driving.

[0030] The driving route learning unit can collect biometric information, including the user's heart rate and stress level, in addition to the user's driving history data, and analyze the user's psychological state while driving. The driving route learning unit, for example, collects biometric information, such as the user's heart rate and stress level, along with the user's driving history data, and analyzes the user's psychological state while driving. For example, it identifies points where the user's heart rate rises sharply and suggests a route that avoids those points. The driving route learning unit also analyzes fluctuations in stress levels and suggests routes that avoid routes that increase stress. In this way, analyzing the user's psychological state while driving makes it possible to suggest safer routes.

[0031] The driving route learning unit can analyze the user's driving history data by time period and learn changes in driving patterns during specific time periods. The driving route learning unit, for example, analyzes the user's driving history data by time period and learns changes in driving patterns during specific time periods. For example, it analyzes the differences in driving patterns between commuting hours and holidays. The driving route learning unit also proposes routes suitable for specific time periods based on the driving patterns by time period. In this way, by learning changes in driving patterns during specific time periods, it becomes possible to propose more appropriate routes.

[0032] The driving route learning unit can collect passenger feedback in addition to the user's driving history data and evaluate driving comfort. The driving route learning unit, for example, collects passenger feedback along with the user's driving history data and evaluates driving comfort. For example, it preferentially suggests routes that passengers find comfortable. Furthermore, the driving route learning unit evaluates the smoothness of driving and the quietness inside the vehicle based on the passenger feedback and suggests a comfortable route. In this way, by evaluating driving comfort, it becomes possible to suggest a more comfortable route.

[0033] The driving route learning unit can compare the user's driving history data with other users and identify common driving patterns. The driving route learning unit, for example, compares the user's driving history data with other users and identifies common driving patterns. For example, it analyzes the driving patterns of users living in the same area. The driving route learning unit also proposes efficient routes based on the common driving patterns. In this way, by identifying common driving patterns, it becomes possible to propose more efficient routes.

[0034] The no right-turn entry route proposal unit can propose an optimal route by collecting information on traffic regulations for specific time periods in real time in addition to facilities where right turns are prohibited. The no right-turn entry route proposal unit, for example, collects information on traffic regulations for specific time periods in real time in addition to facilities where right turns are prohibited, and proposes an optimal route. For example, it proposes a route that takes into account time periods when traffic regulations are lifted. The no right-turn entry route proposal unit also collects information on traffic congestion and road construction, and calculates an optimal route based on this information. This makes it possible to propose a more appropriate route by taking into account traffic regulation information for specific time periods.

[0035] The no right-turn route proposal unit can check the availability of nearby parking lots in real time and propose the most suitable parking lot when proposing a route to a facility where right turns are prohibited. For example, the no right-turn route proposal unit checks the availability of nearby parking lots in real time and proposes the most suitable parking lot when proposing a route to a facility where right turns are prohibited. For example, it gives priority to proposing routes with many available parking lots. In addition, the no right-turn route proposal unit considers the reservation status and capacity of parking lots to propose the most suitable parking lot. This makes it possible to propose a more suitable parking lot by considering the availability of nearby parking lots.

[0036] The no right-turn route proposing unit can take into account specific traffic regulations when proposing a route to a specific facility, in addition to facilities where right turns are prohibited. The no right-turn route proposing unit, for example, takes into account specific traffic regulations when proposing a route to a specific facility (hospital, school, etc.) in addition to facilities where right turns are prohibited. For example, it proposes a route that takes into account traffic regulations during school commute times. In addition, the no right-turn route proposing unit takes into account routes reserved for emergency vehicles at hospitals and proposes the optimal route. In this way, by taking traffic regulations into account when proposing a route to a specific facility, it becomes possible to propose a more appropriate route.

[0037] The no right turn route proposing unit can propose a route that combines the use of public transportation when proposing a route to a facility where right turns are prohibited. For example, the no right turn route proposing unit proposes a route that combines the use of public transportation when proposing a route to a facility where right turns are prohibited. For example, it proposes a route from the nearest station or bus stop. In addition, the no right turn route proposing unit checks the operation status of public transportation in real time and calculates the optimal route. This makes it possible to propose a more efficient route by combining the use of public transportation.

[0038] The risk estimation unit collects weather data and road construction information in real time in addition to traffic accident data, and can identify dangerous areas more accurately. The risk estimation unit collects weather data and road construction information in real time in addition to traffic accident data, and can identify dangerous areas more accurately. For example, the risk estimation unit suggests a route that avoids slippery roads in rainy weather. The risk estimation unit also suggests a route that avoids construction areas based on the road construction information. In this way, by taking weather data and road construction information into consideration, it becomes possible to identify dangerous areas more accurately.

[0039] The risk estimation unit can collect driving data of other vehicles and analyze surrounding traffic conditions to widen the range of risk estimation. The risk estimation unit, for example, collects driving data of other vehicles and analyzes surrounding traffic conditions to widen the range of risk estimation. For example, it identifies dangerous locations based on data of vehicles that have braked suddenly. The risk estimation unit also analyzes the speed and direction of travel of other vehicles to identify dangerous intersections. In this way, by collecting driving data of other vehicles, it becomes possible to widen the range of risk estimation.

[0040] The risk estimation unit can analyze the movements of pedestrians and bicycles in real time and identify dangerous areas in order to expand the range of risk estimation. The risk estimation unit, for example, analyzes the movements of pedestrians and bicycles in real time to build a system that identifies dangerous areas. For example, it can suggest routes that avoid intersections with many pedestrians. The risk estimation unit also analyzes the direction and speed of bicycles to identify dangerous intersections. This makes it possible to more accurately identify dangerous areas by analyzing the movements of pedestrians and bicycles.

[0041] In order to expand the range of risk estimation, the risk estimation unit can use drones to monitor traffic conditions from the sky in real time and identify dangerous areas. The risk estimation unit, for example, uses drones to monitor traffic conditions from the sky in real time and builds a system to identify dangerous areas. For example, traffic congestion and accidents are monitored from the sky. The risk estimation unit also analyzes drone video data to identify dangerous intersections and sharp curves. This makes it possible to use drones to monitor traffic conditions over a wide area and identify dangerous areas.

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

[0043] The car navigation system further includes a voice recognition unit. The voice recognition unit can analyze the user's voice commands and reflect them in route suggestions. For example, if the user vocally instructs the system that "I want to avoid traffic jams," the voice recognition unit can analyze the instruction and suggest a route that avoids traffic jams. In addition, when the user vocally inputs a specific destination, the voice recognition unit can calculate and suggest the optimal route to that destination. This allows for more intuitive and rapid route suggestions by using voice commands.

[0044] The driving route learning unit can further analyze the user's driving style and support eco-driving. For example, it can analyze the frequency of sudden acceleration and sudden braking and suggest routes that reduce these. The driving route learning unit can also prioritize suggesting routes with good fuel efficiency. In this way, by analyzing the user's driving style, it is possible to improve fuel efficiency and reduce environmental impact.

[0045] The driving route learning unit can also collect gaze data while the user is driving and detect a decline in attention. For example, if the user's gaze does not stay within a certain range, it determines that the user's attention is declining and suggests taking a break. The driving route learning unit can also highlight areas requiring attention based on the gaze data. This makes it possible to maintain attention while driving and support safe driving by using gaze data.

[0046] The driving route learning unit can further analyze the user's level of fatigue while driving and suggest rest stops. For example, it can detect fatigue caused by long driving hours and suggest appropriate rest stops. The driving route learning unit can also suggest routes that shorten driving time according to the level of fatigue. This makes it possible to suggest routes that take the user's health into consideration by analyzing the level of fatigue.

[0047] The driving route learning unit can also collect body temperature data of the user while driving and maintain a comfortable in-car environment. For example, if the user's body temperature rises, the air conditioning settings can be adjusted to maintain a comfortable temperature. The driving route learning unit can also suggest appropriate rest stops based on the body temperature data. In this way, by using the body temperature data, a comfortable in-car environment can be maintained and comfort during driving can be improved.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The driving route learning unit learns the user's driving history data. For example, the driving route learning unit collects the user's past driving history data and identifies roads that are frequently used and roads that should be avoided. The driving route learning unit also analyzes the data using generative AI to understand the user's driving patterns and preferences. Step 2: The no-right-turn route suggestion unit suggests a route to a facility where right turns are prohibited, based on the user's driving history data learned by the driving route learning unit. For example, if the destination is a parking lot where right turns are prohibited, the no-right-turn route suggestion unit calculates a route that allows left turns to enter the facility and suggests it to the user. Step 3: The risk estimation unit estimates dangerous locations based on the route proposed by the no-right-turn-entry route proposal unit. For example, the risk estimation unit analyzes traffic accident data and road condition data to identify dangerous intersections and sharp curves. The risk estimation unit also calculates a route that avoids these dangerous locations and proposes it to the user.

[0050] (Example 2) A car navigation system according to an embodiment of the present invention is a system that learns a user's driving route, and when a route to a facility where right turns are prohibited is proposed, suggests a route that can be reached by turning left. This allows the car navigation system to learn a user's driving route, and when a route to a facility where right turns are prohibited is proposed, suggests a route that can be reached by turning left. Furthermore, by expanding the range of risk estimation, it is possible to aim to propose routes that eliminate all risky driving.

[0051] A car navigation system according to an embodiment includes a driving route learning unit, a no-right-turn-entry route proposing unit, and a risk estimation unit. The driving route learning unit learns a user's driving history data. For example, the driving route learning unit collects the user's past driving history data and identifies frequently used roads and roads to avoid. The driving route learning unit also analyzes the data using a generation AI to understand the user's driving patterns and preferences. The no-right-turn-entry route proposing unit proposes a route to a facility where right-turn entry is prohibited based on the user's driving history data learned by the driving route learning unit. For example, if the destination is a parking lot where right-turn entry is prohibited, the no-right-turn-entry route proposing unit calculates a route that allows left turn entry and proposes it to the user. The risk estimation unit estimates dangerous areas based on the route proposed by the no-right-turn-entry route proposing unit. For example, the risk estimation unit analyzes traffic accident data and road condition data to identify dangerous intersections and sharp curves. The risk estimation unit also calculates a route that avoids these dangerous areas and proposes it to the user. As a result, the car navigation system according to the embodiment can learn the user's driving history data, suggest routes to facilities where right turns are prohibited, and estimate dangerous areas, thereby assisting in safe driving.

[0052] The driving route learning unit can collect biometric information, including the user's heart rate and stress level, in addition to the user's driving history data, and analyze the user's psychological state while driving. The driving route learning unit, for example, collects biometric information, such as the user's heart rate and stress level, along with the user's driving history data, and analyzes the user's psychological state while driving. For example, it identifies points where the user's heart rate rises sharply and suggests a route that avoids those points. The driving route learning unit also analyzes fluctuations in stress levels and suggests routes that avoid routes that increase stress. In this way, analyzing the user's psychological state while driving makes it possible to suggest safer routes.

[0053] The driving route learning unit can analyze the user's driving history data by time period and learn changes in driving patterns during specific time periods. The driving route learning unit, for example, analyzes the user's driving history data by time period and learns changes in driving patterns during specific time periods. For example, it analyzes the differences in driving patterns between commuting hours and holidays. The driving route learning unit also proposes routes suitable for specific time periods based on the driving patterns by time period. In this way, by learning changes in driving patterns during specific time periods, it becomes possible to propose more appropriate routes.

[0054] The driving route learning unit can use the emotion estimation function to estimate the user's emotions while driving in real time and propose a route based on the emotions. The driving route learning unit, for example, uses the emotion estimation function to estimate the user's emotions while driving in real time and propose a route based on the emotions. For example, it makes a proposal to avoid routes that increase stress. The driving route learning unit also analyzes the user's emotions and preferentially proposes routes that are relaxing. In this way, by proposing a route based on the user's emotions, stress while driving can be reduced.

[0055] The driving route learning unit can collect passenger feedback in addition to the user's driving history data and evaluate driving comfort. The driving route learning unit, for example, collects passenger feedback along with the user's driving history data and evaluates driving comfort. For example, it preferentially suggests routes that passengers find comfortable. Furthermore, the driving route learning unit evaluates the smoothness of driving and the quietness inside the vehicle based on the passenger feedback and suggests a comfortable route. In this way, by evaluating driving comfort, it becomes possible to suggest a more comfortable route.

[0056] The driving route learning unit can compare the user's driving history data with other users and identify common driving patterns. The driving route learning unit, for example, compares the user's driving history data with other users and identifies common driving patterns. For example, it analyzes the driving patterns of users living in the same area. The driving route learning unit also proposes efficient routes based on the common driving patterns. In this way, by identifying common driving patterns, it becomes possible to propose more efficient routes.

[0057] The driving route learning unit can use the emotion estimation function to analyze the emotional response of the user when selecting a specific route and suggest a route that elicits positive emotions. For example, the driving route learning unit can use the emotion estimation function to analyze the emotional response of the user when selecting a specific route and suggest a route that elicits positive emotions. For example, the driving route learning unit preferentially suggests routes that allow the user to relax. Furthermore, the driving route learning unit suggests routes that elicit joy and a sense of security based on the user's emotional response. This makes it possible to improve the user's driving experience by suggesting routes that elicit positive emotions.

[0058] The no right-turn entry route proposal unit can propose an optimal route by collecting information on traffic regulations for specific time periods in real time in addition to facilities where right turns are prohibited. The no right-turn entry route proposal unit, for example, collects information on traffic regulations for specific time periods in real time in addition to facilities where right turns are prohibited, and proposes an optimal route. For example, it proposes a route that takes into account time periods when traffic regulations are lifted. The no right-turn entry route proposal unit also collects information on traffic congestion and road construction, and calculates an optimal route based on this information. This makes it possible to propose a more appropriate route by taking into account traffic regulation information for specific time periods.

[0059] The no right-turn route proposal unit can check the availability of nearby parking lots in real time and propose the most suitable parking lot when proposing a route to a facility where right turns are prohibited. For example, the no right-turn route proposal unit checks the availability of nearby parking lots in real time and proposes the most suitable parking lot when proposing a route to a facility where right turns are prohibited. For example, it gives priority to proposing routes with many available parking lots. In addition, the no right-turn route proposal unit considers the reservation status and capacity of parking lots to propose the most suitable parking lot. This makes it possible to propose a more suitable parking lot by considering the availability of nearby parking lots.

[0060] The no right-turn route proposing unit can use the emotion estimation function to estimate the user's emotion when proposing a route to a facility where right turns are prohibited, and propose a route that reduces stress. The no right-turn route proposing unit, for example, uses the emotion estimation function to estimate the user's emotion when proposing a route to a facility where right turns are prohibited, and proposes a route that reduces stress. For example, it preferentially proposes routes that cause less stress. In addition, the no right-turn route proposing unit analyzes the user's emotion and proposes a route that allows the user to relax. This makes it possible to propose a route that reduces stress by taking the user's emotion into consideration.

[0061] The no right-turn route proposing unit can take into account specific traffic regulations when proposing a route to a specific facility, in addition to facilities where right turns are prohibited. The no right-turn route proposing unit, for example, takes into account specific traffic regulations when proposing a route to a specific facility (hospital, school, etc.) in addition to facilities where right turns are prohibited. For example, it proposes a route that takes into account traffic regulations during school commute times. In addition, the no right-turn route proposing unit takes into account routes reserved for emergency vehicles at hospitals and proposes the optimal route. In this way, by taking traffic regulations into account when proposing a route to a specific facility, it becomes possible to propose a more appropriate route.

[0062] The no right turn route proposing unit can propose a route that combines the use of public transportation when proposing a route to a facility where right turns are prohibited. For example, the no right turn route proposing unit proposes a route that combines the use of public transportation when proposing a route to a facility where right turns are prohibited. For example, it proposes a route from the nearest station or bus stop. In addition, the no right turn route proposing unit checks the operation status of public transportation in real time and calculates the optimal route. This makes it possible to propose a more efficient route by combining the use of public transportation.

[0063] The risk estimation unit collects weather data and road construction information in real time in addition to traffic accident data, and can identify dangerous areas more accurately. The risk estimation unit collects weather data and road construction information in real time in addition to traffic accident data, and can identify dangerous areas more accurately. For example, the risk estimation unit suggests a route that avoids slippery roads in rainy weather. The risk estimation unit also suggests a route that avoids construction areas based on the road construction information. In this way, by taking weather data and road construction information into consideration, it becomes possible to identify dangerous areas more accurately.

[0064] The risk estimation unit can collect driving data of other vehicles and analyze surrounding traffic conditions to widen the range of risk estimation. The risk estimation unit, for example, collects driving data of other vehicles and analyzes surrounding traffic conditions to widen the range of risk estimation. For example, it identifies dangerous locations based on data of vehicles that have braked suddenly. The risk estimation unit also analyzes the speed and direction of travel of other vehicles to identify dangerous intersections. In this way, by collecting driving data of other vehicles, it becomes possible to widen the range of risk estimation.

[0065] The risk estimation unit can use the emotion estimation function to estimate the user's emotion when passing through a dangerous spot and suggest a route that reduces stress. The risk estimation unit, for example, uses the emotion estimation function to estimate the user's emotion when passing through a dangerous spot and suggest a route that reduces stress. For example, it suggests a route that avoids dangerous spots that increase stress. The risk estimation unit also analyzes the user's emotion and suggests a route that allows the user to relax. This makes it possible to suggest a route that reduces stress by taking the user's emotion into consideration.

[0066] The risk estimation unit can analyze the movements of pedestrians and bicycles in real time and identify dangerous areas in order to expand the range of risk estimation. The risk estimation unit, for example, analyzes the movements of pedestrians and bicycles in real time to build a system that identifies dangerous areas. For example, it can suggest routes that avoid intersections with many pedestrians. The risk estimation unit also analyzes the direction and speed of bicycles to identify dangerous intersections. This makes it possible to more accurately identify dangerous areas by analyzing the movements of pedestrians and bicycles.

[0067] In order to expand the range of risk estimation, the risk estimation unit can use drones to monitor traffic conditions from the sky in real time and identify dangerous areas. The risk estimation unit, for example, uses drones to monitor traffic conditions from the sky in real time and builds a system to identify dangerous areas. For example, traffic congestion and accidents are monitored from the sky. The risk estimation unit also analyzes drone video data to identify dangerous intersections and sharp curves. This makes it possible to use drones to monitor traffic conditions over a wide area and identify dangerous areas.

[0068] The risk estimation unit can use the emotion estimation function to monitor the user's emotions in real time when passing through dangerous areas and continuously adjust the optimal route. The risk estimation unit, for example, uses the emotion estimation function to monitor the user's emotions in real time when passing through dangerous areas and continuously adjust the optimal route. For example, it makes suggestions to avoid dangerous areas that increase stress. The risk estimation unit also builds a system that analyzes changes in the user's emotions and adjusts the route in real time. This makes it possible to reduce stress while driving by monitoring the user's emotions in real time and continuously adjusting the optimal route.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The car navigation system further includes a voice recognition unit. The voice recognition unit can analyze the user's voice commands and reflect them in route suggestions. For example, if the user vocally instructs the system that "I want to avoid traffic jams," the voice recognition unit can analyze the instruction and suggest a route that avoids traffic jams. In addition, when the user vocally inputs a specific destination, the voice recognition unit can calculate and suggest the optimal route to that destination. This allows for more intuitive and rapid route suggestions by using voice commands.

[0071] The driving route learning unit can further analyze the user's driving style and support eco-driving. For example, it can analyze the frequency of sudden acceleration and sudden braking and suggest routes that reduce these. The driving route learning unit can also prioritize suggesting routes with good fuel efficiency. In this way, by analyzing the user's driving style, it is possible to improve fuel efficiency and reduce environmental impact.

[0072] The driving route learning unit can further analyze the user's music preferences while driving and suggest relaxing music. For example, if the user likes a particular genre of music, music of that genre is played. The driving route learning unit can also suggest music that matches the user's mood while driving. In this way, analyzing the user's musical preferences can enhance the relaxing effect while driving.

[0073] The driving route learning unit can also collect gaze data while the user is driving and detect a decline in attention. For example, if the user's gaze does not stay within a certain range, it determines that the user's attention is declining and suggests taking a break. The driving route learning unit can also highlight areas requiring attention based on the gaze data. This makes it possible to maintain attention while driving and support safe driving by using gaze data.

[0074] The driving route learning unit can also analyze facial expression data of the user while driving and detect changes in emotions. For example, if the user smiles, it determines that the user is relaxed and suggests a route that maintains that state. Furthermore, if the user looks anxious, the driving route learning unit can identify the cause and suggest a route that avoids the anxiety. This makes it possible to suggest routes based on the user's emotions by using facial expression data.

[0075] The driving route learning unit can further analyze the user's level of fatigue while driving and suggest rest stops. For example, it can detect fatigue caused by long driving hours and suggest appropriate rest stops. The driving route learning unit can also suggest routes that shorten driving time according to the level of fatigue. This makes it possible to suggest routes that take the user's health into consideration by analyzing the level of fatigue.

[0076] The driving route learning unit can further estimate the user's emotions while driving and provide entertainment to reduce stress. For example, if the user is feeling stressed, it can provide relaxing videos or music. The driving route learning unit can also suggest interactive content such as games or quizzes depending on the user's emotions. This can reduce stress while driving by providing entertainment based on emotions.

[0077] The driving route learning unit can also collect body temperature data of the user while driving and maintain a comfortable in-car environment. For example, if the user's body temperature rises, the air conditioning settings can be adjusted to maintain a comfortable temperature. The driving route learning unit can also suggest appropriate rest stops based on the body temperature data. In this way, by using the body temperature data, a comfortable in-car environment can be maintained and comfort during driving can be improved.

[0078] The driving route learning unit can further estimate the user's emotions while driving and provide driving assistance based on the emotions. For example, if the user is nervous, the driving assistance function is enhanced to reduce the burden of driving. Also, if the user is relaxed, the driving route learning unit can minimize the driving assistance function, allowing the user to enjoy free driving. This makes it possible to improve the user's driving experience by providing driving assistance based on emotions.

[0079] The driving route learning unit can further estimate the user's emotions while driving and provide driving assistance based on the emotions. For example, if the user is nervous, the driving assistance function is enhanced to reduce the burden of driving. Also, if the user is relaxed, the driving route learning unit can minimize the driving assistance function, allowing the user to enjoy free driving. This makes it possible to improve the user's driving experience by providing driving assistance based on emotions.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The driving route learning unit learns the user's driving history data. For example, the driving route learning unit collects the user's past driving history data and identifies roads that are frequently used and roads that should be avoided. The driving route learning unit also analyzes the data using generative AI to understand the user's driving patterns and preferences. Step 2: The no-right-turn route suggestion unit suggests a route to a facility where right turns are prohibited, based on the user's driving history data learned by the driving route learning unit. For example, if the destination is a parking lot where right turns are prohibited, the no-right-turn route suggestion unit calculates a route that allows left turns to enter the facility and suggests it to the user. Step 3: The risk estimation unit estimates dangerous locations based on the route proposed by the no-right-turn-entry route proposal unit. For example, the risk estimation unit analyzes traffic accident data and road condition data to identify dangerous intersections and sharp curves. The risk estimation unit also calculates a route that avoids these dangerous locations and proposes it to the user.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] 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]

[0149] 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 route learning unit that learns driving history data of a user; a right-turn entry prohibited route proposing unit that proposes a route to a facility where right turns are prohibited based on the driving history data of the user learned by the driving route learning unit; a risk estimation unit that estimates dangerous locations based on the route proposed by the right-turn entry prohibition route proposal unit. A system characterized by:

2. The driving route learning unit The user's emotions while driving are estimated in real time, and a route is proposed based on the emotions.

2. The system of claim 1.

3. The right turn entry prohibited route proposing unit In addition to the facilities where right turns are prohibited, traffic regulation information for specific time periods is collected in real time, and the optimal route is proposed.

2. The system of claim 1.

4. The risk estimation unit In addition to traffic accident data, weather data and road construction information will be collected in real time to more accurately identify these dangerous areas.

2. The system of claim 1.

5. The driving route learning unit Analyzing the emotional response of the user when selecting a specific route and suggesting the route that elicits positive emotions 2. The system of claim 1.

6. The right turn entry prohibited route proposing unit When proposing a route to the facility where right turns are prohibited, the emotion of the user is estimated and the route that reduces stress is proposed.

2. The system of claim 1.

7. The risk estimation unit Estimating the user's feelings when passing through the dangerous spot and proposing the route that reduces stress 2. The system of claim 1.

8. The risk estimation unit The user's emotions are monitored in real time as the user passes through the dangerous spots, and the optimal route is continuously adjusted.

2. The system of claim 1.

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