System

Abstract

An object of a system according to an embodiment is to propose an optimal route in real time while avoiding traffic congestion.SOLUTION: A system according to an embodiment includes a current location acquisition unit, a destination acquisition unit, a traffic situation analysis unit, and a route generation unit. The current location acquisition unit acquires a current location of a user. The destination acquisition unit acquires a destination of a user. The traffic situation analysis unit analyzes a current traffic situation. The route generation unit generates an optimal route on the basis of the data acquired and analyzed by the current position acquisition unit, the destination acquisition unit, and the traffic situation analysis 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 in real time that avoid traffic congestion, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal route in real time that avoids traffic congestion. [Means for solving the problem]

[0006] The system according to the embodiment includes a current location acquisition unit, a destination acquisition unit, a traffic condition analysis unit, and a route generation unit. The current location acquisition unit acquires the user's current location. The destination acquisition unit acquires the user's destination. The traffic condition analysis unit analyzes current traffic conditions. The route generation unit generates an optimal route based on data acquired and analyzed by the current location acquisition unit, destination acquisition unit, and traffic condition analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal route in real time while avoiding traffic congestion. [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 navigation app according to an embodiment of the present invention is a system that proposes optimal routes in real time for various driving scenarios, such as a user's commute, business deliveries, weekend drives, etc. This allows the navigation app to avoid traffic congestion, save time, and make driving more comfortable.

[0029] A navigation application according to an embodiment includes a current location acquisition unit, a destination acquisition unit, a traffic condition analysis unit, and a route generation unit. The current location acquisition unit acquires the user's current location. For example, the current location acquisition unit acquires the current location using GPS. The current location can also be acquired using Wi-Fi or a cell tower. The current location acquisition unit can also acquire current location information directly from the user's device. The destination acquisition unit acquires the user's destination. For example, the destination acquisition unit acquires the destination entered by the user into the application. The destination can also be acquired from calendar information. The destination can also be estimated from past traffic history. The traffic condition analysis unit analyzes current traffic conditions. For example, the traffic condition analysis unit analyzes real-time traffic data. The traffic condition analysis unit can also analyze past traffic data. The traffic condition analysis unit can also analyze traffic conditions using sensor data. The route generation unit generates an optimal route based on data acquired and analyzed by the current location acquisition unit, destination acquisition unit, and traffic condition analysis unit. For example, the route generation unit generates a route with the shortest distance. The route generation unit can also generate a route with the shortest time. The route generation unit can also generate a route with the least traffic. This allows the navigation application to propose an optimal route in real time based on the user's current location, destination, and traffic conditions.

[0030] The route generation unit can analyze the user's past driving history and propose a route optimized for the user's driving style. For example, the route generation unit collects the user's past driving history and analyzes data such as driving speed and frequency of stops. For example, for a user who is in a hurry, the route generation unit proposes a route that prioritizes highways. The route generation unit also analyzes past driving patterns to understand the user's driving style. For example, the route generation unit analyzes the frequency of sudden acceleration and sudden braking. The route generation unit also proposes an optimal route based on the driving style. For example, for a user who prefers relaxed driving, the route generation unit proposes a scenic route. This makes it possible to propose a route optimized for the user's driving style.

[0031] The route generation unit can predict the arrival time at the destination and dynamically adjust the optimal route based on the arrival time. For example, the route generation unit inputs the user's destination and current location, and the generation AI predicts the arrival time. For example, based on the arrival time at the destination, it proposes the optimal departure time and route. The route generation unit also predicts the arrival time taking into account traffic conditions and road congestion. For example, it analyzes the timing of traffic lights and road congestion. The route generation unit also dynamically adjusts the optimal route based on the arrival time. For example, it proposes an alternative route to avoid traffic jams. This allows the optimal route to be dynamically adjusted based on the arrival time.

[0032] The route generation unit can also propose optimal routes for bicycles, walking, and other means of transportation. The route generation unit develops a dedicated route proposal algorithm to accommodate bicycles and walking as modes of transportation, for example. For example, the route generation unit proposes routes that take into account bicycle-only roads and pedestrian-only roads. The route generation unit can also propose routes that use public transportation. For example, the route generation unit proposes routes that take into account the operating conditions of buses and trains. The route generation unit also proposes optimal routes depending on the user's mode of transportation. For example, the route generation unit proposes a route that avoids slopes when traveling by bicycle. This makes it possible to propose optimal routes for other modes of transportation, such as bicycles and walking.

[0033] The route generation unit can refer to the user's calendar information and propose a route that matches the schedule. The route generation unit, for example, refers to the user's calendar information and proposes an optimal route that matches the schedule. For example, the departure time is adjusted to match the start time of a meeting. The route generation unit also obtains a destination from the calendar information and proposes an optimal route. For example, the location of an event registered in the calendar is set as the destination. The route generation unit also dynamically adjusts the route that matches the schedule based on the calendar information. For example, the route is recalculated in response to changes in the schedule. This makes it possible to propose a route that matches the user's schedule.

[0034] The route generation unit can learn from past traffic data, predict future traffic congestion, and propose routes to avoid it. For example, the route generation unit collects past traffic data, and the generation AI develops an algorithm to predict future traffic congestion. For example, it predicts congestion that is likely to occur at certain times of the day or on certain days of the week. The route generation unit also analyzes real-time traffic data to predict future traffic congestion. For example, it predicts the occurrence of congestion based on current traffic conditions. The route generation unit also proposes routes to avoid future traffic congestion. For example, it proposes alternative routes to avoid congestion. This makes it possible to predict future traffic congestion and propose routes to avoid it.

[0035] The route generation unit can analyze the trends of users of other navigation apps in real time and propose the optimal route. For example, the route generation unit collects the trends of users of other navigation apps in real time, and the generation AI proposes the optimal route. For example, it prioritizes proposing routes that other users avoid. The route generation unit also analyzes data from other navigation apps and proposes the optimal route. For example, it analyzes the movement patterns of other users. The route generation unit also proposes the optimal route based on the trends of users of other navigation apps. For example, it proposes an alternative route to avoid congested routes. This makes it possible to propose the optimal route based on the trends of users of other navigation apps.

[0036] The route generation unit can analyze the operation status of public transportation and propose a route using public transportation. For example, the route generation unit collects the operation status of public transportation in real time, and the generation AI proposes the optimal route. For example, it proposes a route that takes into account the operation status of trains and buses. The route generation unit also analyzes data on public transportation and proposes the optimal route. For example, it analyzes operation schedules and delay information. The route generation unit also proposes a route using public transportation. For example, it proposes a route that involves changing buses and trains. This makes it possible to propose the optimal route using public transportation.

[0037] The route generation unit can analyze fuel efficiency data of the user's vehicle and propose a fuel-efficient route. For example, the route generation unit collects fuel efficiency data of the user's vehicle, and the generation AI proposes a fuel-efficient route. For example, it selects a route that can be driven at a fuel-efficient speed. The route generation unit also analyzes the fuel efficiency data and proposes the optimal route. For example, it proposes a route that prioritizes roads with good fuel efficiency. The route generation unit also proposes a route that takes fuel efficiency into consideration. For example, it proposes a route that avoids steep slopes. This makes it possible to propose a fuel-efficient route.

[0038] The route generation unit can analyze the user's past travel data and learn and propose time-efficient routes. The route generation unit, for example, collects the user's past travel data, and the generation AI learns the most time-efficient route. For example, it analyzes past travel times and routes. The route generation unit also proposes the optimal route based on past travel data. For example, it proposes the most time-efficient route among routes used in the past. The route generation unit also proposes routes that take time efficiency into consideration. For example, it proposes routes with less traffic volume or fewer wait times at traffic lights. This makes it possible to propose the most time-efficient route based on the user's past travel data.

[0039] The route generation unit can analyze traffic light timing in real time and propose a route that minimizes waiting time at traffic lights. For example, the route generation unit collects traffic light timing in real time, and the generation AI proposes a route that minimizes waiting time at traffic lights. For example, it selects a route that takes into account the timing of traffic light changes. The route generation unit also analyzes traffic light data and proposes the optimal route. For example, it proposes a route with minimal waiting time at traffic lights. The route generation unit also dynamically adjusts the route based on real-time traffic light data. For example, it recalculates the route to match the timing of traffic lights. This makes it possible to propose a route that minimizes waiting time at traffic lights.

[0040] The route generation unit can analyze the availability of parking spaces at the user's destination and propose the shortest route to the parking space. For example, the route generation unit collects information on the availability of parking spaces around the user's destination in real time, and the generation AI proposes the optimal route to the parking space. For example, it gives priority to vacant parking spaces. The route generation unit also analyzes parking space data and proposes the optimal route. For example, it selects a route based on the availability of parking spaces. The route generation unit also monitors the availability of parking spaces in real time and proposes the optimal route. For example, it recalculates the route depending on the availability of parking spaces. This makes it possible to propose the shortest route based on the availability of parking spaces.

[0041] The route generation unit can predict waiting times at the user's destination and propose routes that minimize waiting times. For example, the route generation unit collects waiting times at the user's destination in real time, and the generation AI proposes the optimal route. For example, it selects a time period with low waiting times. The route generation unit also analyzes waiting time data and proposes the optimal route. For example, it proposes a route with low waiting times. The route generation unit also predicts waiting times and dynamically adjusts the route based on that. For example, it recalculates the route to match time periods with low waiting times. This makes it possible to propose routes that minimize waiting times.

[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 navigation system may further include a health monitoring unit that monitors the user's health condition and suggests an optimal route based on the health condition. For example, the system may monitor the user's heart rate and blood pressure, and if the user's health condition worsens, suggest a route that passes through the nearest hospital or rest area. The health monitoring unit may also analyze the user's exercise volume and suggest routes that recommend walking or cycling to help the user get more exercise. Furthermore, the health monitoring unit may suggest routes that reduce stress based on the user's health data. For example, the system may suggest routes with plenty of natural scenery or routes that pass through parks. This allows the system to suggest optimal routes based on the user's health condition.

[0044] The navigation system may also be equipped with a maintenance monitoring unit that monitors the maintenance status of the user's vehicle and suggests a route that passes through the nearest repair shop if maintenance is required. For example, if an oil change or tire pressure check is required, the system suggests a route that passes through the nearest gas station or repair shop. The maintenance monitoring unit may also analyze the vehicle's breakdown risk and suggest a safe route if the risk of breakdown is high. The maintenance monitoring unit may also analyze the vehicle's fuel efficiency and suggest a route that improves fuel efficiency. For example, the system may suggest a route that avoids steep slopes or a route that allows driving at a constant speed. This makes it possible to suggest the optimal route according to the vehicle's maintenance status.

[0045] The navigation system may further include an energy efficiency unit for minimizing energy consumption while the user is driving. For example, the energy efficiency unit may analyze fuel consumption data of the user's vehicle and suggest a fuel-efficient route. The energy efficiency unit may also analyze the user's driving style and provide driving advice for minimizing energy consumption. Furthermore, the energy efficiency unit may suggest an optimal route based on the energy consumption data of the user's vehicle. For example, the energy efficiency unit may suggest a route that avoids steep slopes or a route that allows driving at a constant speed. In this way, a route for minimizing energy consumption while the user is driving can be suggested.

[0046] The navigation system may further include an environmental protection unit for minimizing the environmental impact of the user's driving. For example, the environmental protection unit may analyze exhaust gas data of the user's vehicle and suggest a route with a low environmental impact. The environmental protection unit may also analyze the user's driving style and provide driving advice for minimizing the environmental impact. Furthermore, the environmental protection unit may suggest an optimal route based on the environmental impact data of the user's vehicle. For example, the environmental protection unit may suggest a route that prioritizes roads with low exhaust gases or a route that allows driving at a certain speed. In this way, a route that minimizes the environmental impact of the user's driving can be suggested.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The current location acquisition unit acquires the user's current location. For example, the current location is acquired using GPS. The current location can also be acquired using Wi-Fi or a cell tower. Furthermore, the current location information can also be acquired directly from the user's device. Step 2: The destination acquisition unit acquires the user's destination. For example, it acquires the destination entered by the user into the app. It can also acquire the destination from calendar information. It can also estimate the destination from past history. Step 3: The traffic situation analysis unit analyzes the current traffic situation. For example, it analyzes real-time traffic data. It can also analyze past traffic data. It can also analyze traffic situations using sensor data. Step 4: The route generation unit generates an optimal route based on the data acquired and analyzed by the current location acquisition unit, destination acquisition unit, and traffic condition analysis unit. For example, it generates a route with the shortest distance. It can also generate a route with the shortest time. It can also generate a route with less traffic.

[0049] (Example 2) A navigation app according to an embodiment of the present invention is a system that proposes optimal routes in real time for various driving scenarios, such as a user's commute, business deliveries, weekend drives, etc. This allows the navigation app to avoid traffic congestion, save time, and make driving more comfortable.

[0050] A navigation application according to an embodiment includes a current location acquisition unit, a destination acquisition unit, a traffic condition analysis unit, and a route generation unit. The current location acquisition unit acquires the user's current location. For example, the current location acquisition unit acquires the current location using GPS. The current location can also be acquired using Wi-Fi or a cell tower. The current location acquisition unit can also acquire current location information directly from the user's device. The destination acquisition unit acquires the user's destination. For example, the destination acquisition unit acquires the destination entered by the user into the application. The destination can also be acquired from calendar information. The destination can also be estimated from past traffic history. The traffic condition analysis unit analyzes current traffic conditions. For example, the traffic condition analysis unit analyzes real-time traffic data. The traffic condition analysis unit can also analyze past traffic data. The traffic condition analysis unit can also analyze traffic conditions using sensor data. The route generation unit generates an optimal route based on data acquired and analyzed by the current location acquisition unit, destination acquisition unit, and traffic condition analysis unit. For example, the route generation unit generates a route with the shortest distance. The route generation unit can also generate a route with the shortest time. The route generation unit can also generate a route with the least traffic. This allows the navigation application to propose an optimal route in real time based on the user's current location, destination, and traffic conditions.

[0051] The route generation unit can analyze the user's past driving history and propose a route optimized for the user's driving style. For example, the route generation unit collects the user's past driving history and analyzes data such as driving speed and frequency of stops. For example, for a user who is in a hurry, the route generation unit proposes a route that prioritizes highways. The route generation unit also analyzes past driving patterns to understand the user's driving style. For example, the route generation unit analyzes the frequency of sudden acceleration and sudden braking. The route generation unit also proposes an optimal route based on the driving style. For example, for a user who prefers relaxed driving, the route generation unit proposes a scenic route. This makes it possible to propose a route optimized for the user's driving style.

[0052] The route generation unit can predict the arrival time at the destination and dynamically adjust the optimal route based on the arrival time. For example, the route generation unit inputs the user's destination and current location, and the generation AI predicts the arrival time. For example, based on the arrival time at the destination, it proposes the optimal departure time and route. The route generation unit also predicts the arrival time taking into account traffic conditions and road congestion. For example, it analyzes the timing of traffic lights and road congestion. The route generation unit also dynamically adjusts the optimal route based on the arrival time. For example, it proposes an alternative route to avoid traffic jams. This allows the optimal route to be dynamically adjusted based on the arrival time.

[0053] The route generation unit can use the emotion estimation function to analyze the user's current emotional state and suggest a route to reduce stress. The route generation unit, for example, analyzes the user's facial expressions and voice to estimate the user's current emotional state. For example, if the user is feeling stressed, the route generation unit suggests a route that will help the user relax. The route generation unit also analyzes the user's biometric data to estimate the user's emotional state. For example, the route generation unit analyzes the user's heart rate and electrodermal activity. The route generation unit also suggests a route to reduce stress based on the user's emotional state. For example, the route generation unit suggests a route with less traffic or a scenic view. This makes it possible to suggest a route to reduce the user's stress.

[0054] The route generation unit can also propose optimal routes for bicycles, walking, and other means of transportation. The route generation unit develops a dedicated route proposal algorithm to accommodate bicycles and walking as modes of transportation, for example. For example, the route generation unit proposes routes that take into account bicycle-only roads and pedestrian-only roads. The route generation unit can also propose routes that use public transportation. For example, the route generation unit proposes routes that take into account the operating conditions of buses and trains. The route generation unit also proposes optimal routes depending on the user's mode of transportation. For example, the route generation unit proposes a route that avoids slopes when traveling by bicycle. This makes it possible to propose optimal routes for other modes of transportation, such as bicycles and walking.

[0055] The route generation unit can refer to the user's calendar information and propose a route that matches the schedule. The route generation unit, for example, refers to the user's calendar information and proposes an optimal route that matches the schedule. For example, the departure time is adjusted to match the start time of a meeting. The route generation unit also obtains a destination from the calendar information and proposes an optimal route. For example, the location of an event registered in the calendar is set as the destination. The route generation unit also dynamically adjusts the route that matches the schedule based on the calendar information. For example, the route is recalculated in response to changes in the schedule. This makes it possible to propose a route that matches the user's schedule.

[0056] The route generation unit can use the emotion estimation function to suggest a scenic route that will allow the user to relax. The route generation unit, for example, uses the emotion estimation function to suggest a scenic route that will allow the user to relax. For example, a route through a park or along the sea is selected. The route generation unit also analyzes the emotional state of the user and suggests a relaxing route. For example, if the user is feeling stressed, a route with many natural landscapes is suggested. The route generation unit also sets criteria for scenic routes and suggests a route based on those criteria. For example, a route that takes tourist spots and natural landscapes into consideration is suggested. This makes it possible to suggest a scenic route that will allow the user to relax.

[0057] The route generation unit can learn from past traffic data, predict future traffic congestion, and propose routes to avoid it. For example, the route generation unit collects past traffic data, and the generation AI develops an algorithm to predict future traffic congestion. For example, it predicts congestion that is likely to occur at certain times of the day or on certain days of the week. The route generation unit also analyzes real-time traffic data to predict future traffic congestion. For example, it predicts the occurrence of congestion based on current traffic conditions. The route generation unit also proposes routes to avoid future traffic congestion. For example, it proposes alternative routes to avoid congestion. This makes it possible to predict future traffic congestion and propose routes to avoid it.

[0058] The route generation unit can analyze the trends of users of other navigation apps in real time and propose the optimal route. For example, the route generation unit collects the trends of users of other navigation apps in real time, and the generation AI proposes the optimal route. For example, it prioritizes proposing routes that other users avoid. The route generation unit also analyzes data from other navigation apps and proposes the optimal route. For example, it analyzes the movement patterns of other users. The route generation unit also proposes the optimal route based on the trends of users of other navigation apps. For example, it proposes an alternative route to avoid congested routes. This makes it possible to propose the optimal route based on the trends of users of other navigation apps.

[0059] The route generation unit can use the emotion estimation function to suggest a route for reducing the stress the user feels due to traffic congestion. The route generation unit, for example, uses the emotion estimation function to suggest a route for reducing the stress the user feels due to traffic congestion. For example, it preferentially suggests a route that avoids traffic congestion. The route generation unit also analyzes the emotional state of the user and suggests a route for reducing stress. For example, if the user is feeling stressed, it suggests a route with less traffic. The route generation unit also monitors the emotional state of the user in real time using the emotion estimation function and suggests a route for reducing stress. For example, it suggests an alternative route to avoid traffic congestion. In this way, it is possible to suggest a route for reducing the stress the user feels due to traffic congestion.

[0060] The route generation unit can analyze the operation status of public transportation and propose a route using public transportation. For example, the route generation unit collects the operation status of public transportation in real time, and the generation AI proposes the optimal route. For example, it proposes a route that takes into account the operation status of trains and buses. The route generation unit also analyzes data on public transportation and proposes the optimal route. For example, it analyzes operation schedules and delay information. The route generation unit also proposes a route using public transportation. For example, it proposes a route that involves changing buses and trains. This makes it possible to propose the optimal route using public transportation.

[0061] The route generation unit can analyze fuel efficiency data of the user's vehicle and propose a fuel-efficient route. For example, the route generation unit collects fuel efficiency data of the user's vehicle, and the generation AI proposes a fuel-efficient route. For example, it selects a route that can be driven at a fuel-efficient speed. The route generation unit also analyzes the fuel efficiency data and proposes the optimal route. For example, it proposes a route that prioritizes roads with good fuel efficiency. The route generation unit also proposes a route that takes fuel efficiency into consideration. For example, it proposes a route that avoids steep slopes. This makes it possible to propose a fuel-efficient route.

[0062] The route generation unit can use the emotion estimation function to propose a route that passes through events and tourist spots that the user can enjoy. For example, the route generation unit uses the emotion estimation function to propose a route that passes through events and tourist spots that the user can enjoy. For example, it selects tourist spots that match the user's interests. The route generation unit also analyzes the user's emotional state and proposes an enjoyable route. For example, it uses the emotion estimation function to analyze the user's interests and preferences. The route generation unit also collects information on events and tourist spots and proposes a route based on that information. For example, it proposes a route that takes into account event information such as concerts and festivals. This makes it possible to propose a route that passes through events and tourist spots that the user can enjoy.

[0063] The route generation unit can analyze the user's past travel data and learn and propose time-efficient routes. The route generation unit, for example, collects the user's past travel data, and the generation AI learns the most time-efficient route. For example, it analyzes past travel times and routes. The route generation unit also proposes the optimal route based on past travel data. For example, it proposes the most time-efficient route among routes used in the past. The route generation unit also proposes routes that take time efficiency into consideration. For example, it proposes routes with less traffic volume or fewer wait times at traffic lights. This makes it possible to propose the most time-efficient route based on the user's past travel data.

[0064] The route generation unit can analyze traffic light timing in real time and propose a route that minimizes waiting time at traffic lights. For example, the route generation unit collects traffic light timing in real time, and the generation AI proposes a route that minimizes waiting time at traffic lights. For example, it selects a route that takes into account the timing of traffic light changes. The route generation unit also analyzes traffic light data and proposes the optimal route. For example, it proposes a route with minimal waiting time at traffic lights. The route generation unit also dynamically adjusts the route based on real-time traffic light data. For example, it recalculates the route to match the timing of traffic lights. This makes it possible to propose a route that minimizes waiting time at traffic lights.

[0065] The route generation unit can use the emotion estimation function to suggest a route for reducing stress when the user is in a hurry. The route generation unit, for example, uses the emotion estimation function to suggest a route for reducing stress when the user is in a hurry. For example, the route generation unit preferentially suggests the shortest route to a user who is in a hurry. The route generation unit also analyzes the emotional state of the user and suggests a route for reducing stress. For example, if the user is in a hurry, it suggests a route with less traffic. The route generation unit also monitors the emotional state of the user in real time using the emotion estimation function and suggests a route for reducing stress. For example, it suggests an alternative route to avoid traffic congestion. In this way, it is possible to suggest a route for reducing stress when the user is in a hurry.

[0066] The route generation unit can analyze the availability of parking spaces at the user's destination and propose the shortest route to the parking space. For example, the route generation unit collects information on the availability of parking spaces around the user's destination in real time, and the generation AI proposes the optimal route to the parking space. For example, it gives priority to vacant parking spaces. The route generation unit also analyzes parking space data and proposes the optimal route. For example, it selects a route based on the availability of parking spaces. The route generation unit also monitors the availability of parking spaces in real time and proposes the optimal route. For example, it recalculates the route depending on the availability of parking spaces. This makes it possible to propose the shortest route based on the availability of parking spaces.

[0067] The route generation unit can predict waiting times at the user's destination and propose routes that minimize waiting times. For example, the route generation unit collects waiting times at the user's destination in real time, and the generation AI proposes the optimal route. For example, it selects a time period with low waiting times. The route generation unit also analyzes waiting time data and proposes the optimal route. For example, it proposes a route with low waiting times. The route generation unit also predicts waiting times and dynamically adjusts the route based on that. For example, it recalculates the route to match time periods with low waiting times. This makes it possible to propose routes that minimize waiting times.

[0068] The route generation unit can provide route guidance while suggesting music or podcasts that can help the user relax using the emotion estimation function. The route generation unit, for example, uses the emotion estimation function to suggest music or podcasts that can help the user relax. For example, it plays music that has a relaxing effect. The route generation unit also analyzes the user's emotional state and suggests relaxing content. For example, it uses the emotion estimation function to analyze the user's preferences. The route generation unit also collects music or podcast data and suggests content based on that data. For example, it suggests music or podcasts that match the user's preferences. This allows route guidance to be provided while suggesting music or podcasts that can help the user relax.

[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 navigation system may further include a health monitoring unit that monitors the user's health condition and suggests an optimal route based on the health condition. For example, the system may monitor the user's heart rate and blood pressure, and if the user's health condition worsens, suggest a route that passes through the nearest hospital or rest area. The health monitoring unit may also analyze the user's exercise volume and suggest routes that recommend walking or cycling to help the user get more exercise. Furthermore, the health monitoring unit may suggest routes that reduce stress based on the user's health data. For example, the system may suggest routes with plenty of natural scenery or routes that pass through parks. This allows the system to suggest optimal routes based on the user's health condition.

[0071] The navigation system may also be equipped with a maintenance monitoring unit that monitors the maintenance status of the user's vehicle and suggests a route that passes through the nearest repair shop if maintenance is required. For example, if an oil change or tire pressure check is required, the system suggests a route that passes through the nearest gas station or repair shop. The maintenance monitoring unit may also analyze the vehicle's breakdown risk and suggest a safe route if the risk of breakdown is high. The maintenance monitoring unit may also analyze the vehicle's fuel efficiency and suggest a route that improves fuel efficiency. For example, the system may suggest a route that avoids steep slopes or a route that allows driving at a constant speed. This makes it possible to suggest the optimal route according to the vehicle's maintenance status.

[0072] The navigation system may also include a restaurant recommendation unit that suggests routes that pass through restaurants and cafes based on the user's preferences. For example, the system suggests an optimal route based on the type of cuisine the user likes or highly rated restaurants. The restaurant recommendation unit may also analyze the user's past visit history to suggest restaurants that the user likes. Furthermore, the restaurant recommendation unit may analyze the user's current emotional state to suggest cafes and restaurants where the user can relax. For example, if the user is feeling stressed, the system may suggest a quiet cafe or a restaurant with a beautiful natural view. This allows the system to suggest an optimal route that suits the user's preferences.

[0073] The navigation system may further include a driving assistance unit that provides feedback to improve the user's driving skills. For example, the driving assistance unit may analyze the frequency of sudden acceleration and braking and provide advice for safe driving. The driving assistance unit may also evaluate the user's driving skills and suggest training programs to improve the driving skills. Furthermore, the driving assistance unit may analyze the user's emotional state and provide driving advice to reduce stress. For example, the driving assistance unit may suggest a relaxing driving method or a route that is less stressful. This allows the system to provide feedback to improve the user's driving skills.

[0074] The navigation system may also include a sightseeing recommendation unit that suggests routes that pass through tourist spots based on the user's hobbies and interests. For example, the system may suggest an optimal route based on historical buildings or art museums that interest the user. The sightseeing recommendation unit may also analyze the user's past visit history and suggest tourist spots that the user likes. Furthermore, the sightseeing recommendation unit may analyze the user's current emotional state and suggest tourist spots where the user can relax. For example, if the user is feeling stressed, the system may suggest tourist spots with lots of natural scenery or quiet places. This allows the system to suggest optimal routes that match the user's hobbies and interests.

[0075] The navigation system may further include an entertainment provider that provides entertainment to the user while driving. For example, it may play music or podcasts based on the user's preferences. The entertainment provider may also analyze the user's emotional state and suggest relaxing content. For example, if the user is feeling stressed, it may suggest music or podcasts that have a relaxing effect. The entertainment provider may also provide content to improve the user's mood while driving. For example, it may suggest enjoyable music or interesting podcasts. In this way, entertainment can be provided to the user while driving.

[0076] The navigation system may further include a safety monitoring unit to ensure the user's safety while driving. For example, the system may monitor the user's level of attention while driving and issue a warning if the user's attention is declining. The safety monitoring unit may also analyze the user's level of fatigue while driving and suggest taking a break. The safety monitoring unit may also analyze the user's emotional state while driving and provide advice to reduce stress. For example, the safety monitoring unit may suggest a relaxing driving method or a route that is less stressful. This allows the system to provide feedback to ensure the user's safety while driving.

[0077] The navigation system may further include an energy efficiency unit for minimizing energy consumption while the user is driving. For example, the energy efficiency unit may analyze fuel consumption data of the user's vehicle and suggest a fuel-efficient route. The energy efficiency unit may also analyze the user's driving style and provide driving advice for minimizing energy consumption. Furthermore, the energy efficiency unit may suggest an optimal route based on the energy consumption data of the user's vehicle. For example, the energy efficiency unit may suggest a route that avoids steep slopes or a route that allows driving at a constant speed. In this way, a route for minimizing energy consumption while the user is driving can be suggested.

[0078] The navigation system may further include a comfort improvement unit for improving the user's driving comfort. For example, the comfort improvement unit may optimize the user's seat position or air conditioning settings. The comfort improvement unit may also analyze the user's emotional state and provide advice for improving comfort. For example, the comfort improvement unit may suggest a relaxing seat position or comfortable air conditioning settings. The comfort improvement unit may also suggest routes for improving the user's driving comfort. For example, the comfort improvement unit may suggest routes with beautiful scenery or routes with less traffic. This allows the system to provide feedback for improving the user's driving comfort.

[0079] The navigation system may further include an environmental protection unit for minimizing the environmental impact of the user's driving. For example, the environmental protection unit may analyze exhaust gas data of the user's vehicle and suggest a route with a low environmental impact. The environmental protection unit may also analyze the user's driving style and provide driving advice for minimizing the environmental impact. Furthermore, the environmental protection unit may suggest an optimal route based on the environmental impact data of the user's vehicle. For example, the environmental protection unit may suggest a route that prioritizes roads with low exhaust gases or a route that allows driving at a certain speed. In this way, a route that minimizes the environmental impact of the user's driving can be suggested.

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

[0081] Step 1: The current location acquisition unit acquires the user's current location. For example, the current location is acquired using GPS. The current location can also be acquired using Wi-Fi or a cell tower. Furthermore, the current location information can also be acquired directly from the user's device. Step 2: The destination acquisition unit acquires the user's destination. For example, it acquires the destination entered by the user into the app. It can also acquire the destination from calendar information. It can also estimate the destination from past history. Step 3: The traffic situation analysis unit analyzes the current traffic situation. For example, it analyzes real-time traffic data. It can also analyze past traffic data. It can also analyze traffic situations using sensor data. Step 4: The route generation unit generates an optimal route based on the data acquired and analyzed by the current location acquisition unit, destination acquisition unit, and traffic condition analysis unit. For example, it generates a route with the shortest distance. It can also generate a route with the shortest time. It can also generate a route with less traffic.

[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, 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.

[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 current location acquisition unit that acquires the current location of the user; a destination acquisition unit that acquires a destination of a user; a traffic situation analysis unit that analyzes the current traffic situation; a route generation unit that generates an optimal route based on the data acquired and analyzed by the current location acquisition unit, the destination acquisition unit, and the traffic condition analysis unit. A system characterized by:

2. The route generation unit Predicting arrival times at the destinations and dynamically adjusting optimal routes based on the arrival times.

2. The system of claim 1.

3. The route generation unit Suggests the best route for cycling, walking, and other modes of transportation 2. The system of claim 1.

4. The route generation unit Learns from past traffic data, predicts future traffic congestion, and suggests routes to avoid it 2. The system of claim 1.

5. The route generation unit Analyzes the user's current emotional state and suggests routes to reduce stress 2. The system of claim 1.

6. The route generation unit Analyzing the operation status of public transport and suggesting routes using public transport 2. The system of claim 1.

7. The route generation unit Analyzes users' past travel data, learns and suggests time-efficient routes 2. The system of claim 1.

8. The route generation unit Using emotion estimation, the system provides route guidance while suggesting relaxing music and podcasts to users.

2. The system of claim 1.

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