System

Abstract

An object of a system according to an embodiment is to propose an efficient route for an electric vehicle.SOLUTION: A system according to an embodiment includes a route proposal unit, a charging station information providing unit, a charging time optimization unit, a traffic condition consideration unit, and an energy consumption minimization unit. The route suggestion unit suggests a route using the generated AI. The charging station information providing unit provides location information of a charging station on the route proposed by the route proposal unit. The charging time optimization unit optimizes the charging time based on the position information of the charging station provided by the charging station information providing unit. The traffic situation consideration unit considers the traffic situation of the route proposed by the route proposal unit. The energy consumption minimizing unit minimizes energy consumption of the route proposed by the route proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has faced the challenge of making efficient route suggestions difficult due to the limited battery capacity of electric vehicles and the long charging times.

[0005] The system according to the embodiment aims to propose efficient routes for electric vehicles. [Means for solving the problem]

[0006] The system according to the embodiment includes a route proposal unit, a charging station information provision unit, a charging time optimization unit, a traffic condition consideration unit, and an energy consumption minimization unit. The route proposal unit proposes a route using a generation AI. The charging station information provision unit provides location information of charging stations on the route proposed by the route proposal unit. The charging time optimization unit optimizes the charging time based on the location information of charging stations provided by the charging station information provision unit. The traffic condition consideration unit considers the traffic conditions of the route proposed by the route proposal unit. The energy consumption minimization unit minimizes the energy consumption of the route proposed by the route proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose efficient routes for electric vehicles. [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 route suggestion system according to an embodiment of the present invention is a system that proposes optimal routes for electric vehicles (EVs) using a generation AI. As a result, the route suggestion system can provide efficient routes to EV users and optimize the locations of charging stations and charging times.

[0029] A route proposal system according to an embodiment includes a route proposal unit, a charging station information provision unit, a charging time optimization unit, a traffic condition consideration unit, and an energy consumption minimization unit. The route proposal unit uses a generation AI to propose an optimal route based on a destination input by a user. For example, when a user inputs a prompt such as "Please tell me the shortest route to my destination," the generation AI analyzes the prompt and calculates an optimal route. The charging station information provision unit provides location information of charging stations along the route proposed by the route proposal unit. For example, when a user inputs a prompt such as "Please tell me the best charging station if I need to charge along the way," the generation AI analyzes the prompt and proposes the location of the best charging station. The charging time optimization unit optimizes the charging time based on the location information of charging stations provided by the charging station information provision unit. For example, when a user inputs a prompt such as "Please tell me the best charging station to minimize charging time," the generation AI analyzes the prompt and proposes the best charging station and charging time. The traffic condition consideration unit considers the traffic conditions of the route proposed by the route proposal unit. For example, when a prompt such as "Please tell me the best route to avoid traffic jams" is input, the generation AI analyzes the prompt and proposes an optimal route that takes traffic conditions into consideration. The energy consumption minimization unit minimizes the energy consumption of the route proposed by the route proposal unit. For example, when a prompt such as "Please tell me the best route to minimize energy consumption" is input, the generation AI analyzes the prompt and proposes a route that minimizes energy consumption. As a result, the route proposal system according to the embodiment can provide an efficient route to an EV user and optimize the location of charging stations and charging time.

[0030] The route suggestion unit can learn the user's driving style and suggest the optimal route based on that driving style. For example, the generation AI in the route suggestion unit collects the user's driving data and analyzes the frequency of sudden acceleration and deceleration. This allows the unit to suggest the optimal route based on the driving style. For example, for a user who frequently accelerates suddenly, it would suggest a route with fewer traffic lights. In addition, to learn the user's driving style, the route suggestion unit analyzes data obtained from the vehicle's sensors and identifies driving patterns. Based on this, it suggests an energy-efficient route. For example, the generation AI calculates the optimal route for the user's driving style in real time based on the user's driving history. For example, it selects a route with fewer sudden decelerations to reduce battery consumption. This improves driving efficiency by suggesting the optimal route based on the user's driving style.

[0031] The route suggestion unit can acquire weather data in real time and propose the optimal route according to the weather conditions. For example, the generation AI in the route suggestion unit acquires weather data in real time and proposes the optimal route taking into account weather conditions such as rain and snow. For example, on rainy days, it selects roads that are less slippery. The route suggestion unit also evaluates the safety of routes based on weather data and proposes the optimal route to the user. For example, on snowy days, it prioritizes routes that have been cleared of snow. The route suggestion unit also analyzes weather forecast data and proposes routes that take future weather changes into consideration. For example, if a storm is forecast, it proposes an evacuation route. This improves safety by proposing the optimal route according to weather conditions.

[0032] The route suggestion unit can suggest routes based on the user's interests in tourist spots or restaurants. For example, the generation AI analyzes the user's interests and preferences and suggests routes that include tourist spots and restaurants. For example, it selects a route that passes through restaurants in the user's favorite genre. The route suggestion unit also suggests routes based on the user's interests, based on the user's past search history and visit history. For example, it suggests routes that include tourist spots that the user has not visited. The route suggestion unit also suggests routes that include specific events or festivals based on the user's interests. For example, it selects a route that passes through places where events that the user is interested in are being held. In this way, suggesting routes based on the user's interests increases the enjoyment of travel.

[0033] The route suggestion unit can propose routes that take into account integration with other means of transportation. For example, the generation AI obtains train and bus operation information in real time and proposes routes that take into account integration with other means of transportation. For example, it selects a route that allows smooth train transfers. In addition, to consider integration with other means of transportation, the generation AI analyzes public transportation timetables and proposes the optimal route. For example, it selects a route that passes through bus stops and stations. In addition, the generation AI adjusts the route in real time, taking into account integration with trains and buses, according to the user's travel needs. For example, it recalculates the route based on train delay information. This improves travel efficiency by proposing routes that take into account integration with other means of transportation.

[0034] The charging station information providing unit can obtain the congestion status of charging stations in real time and suggest stations with short waiting times. For example, the generation AI of the charging station information providing unit obtains the congestion status of charging stations in real time and suggests stations with short waiting times. For example, stations with low congestion levels are selected preferentially. The charging station information providing unit also builds a system in which the generation AI suggests the optimal charging station based on the congestion status data. For example, stations with short waiting times are displayed in real time. The charging station information providing unit also uses the generation AI to predict congestion at charging stations and suggest stations that will avoid future congestion. For example, stations that avoid times when congestion is predicted are selected. This improves charging efficiency by suggesting charging stations with short waiting times.

[0035] The charging station information providing unit can take into account charging station fee information and suggest cost-effective stations. For example, the generation AI of the charging station information providing unit obtains charging station fee information in real time and suggests cost-effective stations. For example, it may prioritize stations with low fees. The charging station information providing unit also builds a system in which the generation AI suggests optimal charging stations based on fee information. For example, it may display stations taking into account fee and charging speed. The charging station information providing unit also uses the generation AI to predict charging station fees and suggest stations that take future fee fluctuations into account. For example, it may select stations where charging can be done before fees increase. This allows the user's charging costs to be reduced by suggesting cost-effective charging stations.

[0036] The charging station information providing unit provides facility information about charging stations and can suggest stations that meet the user's needs. For example, the generation AI of the charging station information providing unit obtains facility information about charging stations in real time and suggests stations that meet the user's needs. For example, it selects stations that have Wi-Fi and restrooms. The charging station information providing unit also builds a system in which the generation AI suggests the optimal charging station based on the facility information. For example, it displays stations that take into account the level of facility availability. The charging station information providing unit also uses the generation AI to predict charging station facilities and suggest stations that take into account the future level of facility availability. For example, it selects stations where new facilities are scheduled to be added. This improves convenience during charging by suggesting charging stations that meet the user's needs.

[0037] The charging station information providing unit can suggest highly rated charging stations based on reviews from other EV users. For example, the generation AI in the charging station information providing unit analyzes reviews from other EV users and suggests highly rated charging stations. For example, stations with high review ratings are selected preferentially. The charging station information providing unit also builds a system in which the generation AI suggests optimal charging stations based on review information. For example, the content of reviews is analyzed and station ratings are displayed. The generation AI in the charging station information providing unit also analyzes review trends and suggests stations that are likely to become more highly rated in the future. For example, stations where new facilities are scheduled to be added are selected. In this way, by suggesting highly rated charging stations, user satisfaction is improved.

[0038] The charging time optimization unit can monitor the battery's deterioration status and propose the optimal charging pattern. For example, the generation AI of the charging time optimization unit monitors the battery's deterioration status in real time and proposes the optimal charging pattern. For example, it selects a charging method according to the battery's health state. The charging time optimization unit also builds a system in which the generation AI proposes the optimal charging pattern based on the deterioration status data. For example, it displays a charging method to extend the battery's lifespan. The charging time optimization unit also uses the generation AI to predict battery deterioration and propose a charging pattern to prevent future deterioration. For example, it selects an appropriate charging method before deterioration progresses. In this way, the battery's lifespan can be extended by monitoring the battery's deterioration status and proposing the optimal charging pattern.

[0039] The charging time optimization unit can take into account the charging speed of the charging station and suggest the station that offers the fastest charging. For example, the generation AI in the charging time optimization unit obtains charging speed data of charging stations in real time and suggests the station that offers the fastest charging. For example, it prioritizes selecting stations with the fastest charging speed. The charging time optimization unit also builds a system in which the generation AI suggests the optimal charging station based on the charging speed data. For example, it displays stations that take into account charging speed and waiting time. The charging time optimization unit also has the generation AI predict the charging speed of the charging station and suggest stations that take into account future charging speed. For example, it selects stations where the charging speed is expected to increase. This allows the charging time to be shortened by suggesting the station that offers the fastest charging.

[0040] The charging time optimization unit can suggest nearby tourist spots or activities that can be done while charging. For example, the generation AI of the charging time optimization unit obtains information on tourist spots and activities around the charging station in real time and suggests them to the user. For example, it displays tourist spots that can be visited while charging. The charging time optimization unit also builds a system in which the generation AI suggests the optimal charging station based on information on nearby tourist spots and activities. For example, it selects stations with an abundance of tourist spots and activities. The charging time optimization unit also predicts tourist spots and activities around the charging station and suggests places that will be enjoyable in the future. For example, it selects a route that includes newly opened tourist spots. This allows the time spent charging to be used effectively by suggesting tourist spots and activities that can be done while charging.

[0041] The charging time optimization unit suggests sharing of charging stations with other EV users, enabling efficient charging. For example, the generation AI in the charging time optimization unit analyzes the charging schedules of other EV users and suggests sharing of charging stations. For example, it matches users who wish to charge during the same time period. The charging time optimization unit also builds a system in which the generation AI suggests the optimal charging station based on sharing information. For example, it displays stations that are available for sharing. The charging time optimization unit also predicts charging station sharing and suggests stations that will be available for sharing in the future. For example, it selects stations that avoid time periods when sharing increases. This improves charging efficiency by suggesting sharing of charging stations with other EV users.

[0042] The traffic condition consideration unit can analyze past traffic data and propose routes that avoid predicted congestion. In the traffic condition consideration unit, for example, the generation AI analyzes past traffic data and proposes routes that avoid predicted congestion. For example, it selects the optimal route based on past congestion patterns. The traffic condition consideration unit also builds a system in which the generation AI proposes the optimal route based on traffic data. For example, it displays a route that takes into account time periods with less congestion. In addition, the traffic condition consideration unit has the generation AI predict traffic data and proposes routes that avoid future congestion. For example, it selects a route that avoids time periods when congestion is predicted. In this way, travel time can be reduced by proposing a route that avoids predicted congestion.

[0043] The traffic condition consideration unit can acquire traffic accident or construction information in real time and propose the optimal detour route. In the traffic condition consideration unit, for example, the generation AI acquires traffic accident and construction information in real time and proposes the optimal detour route. For example, it selects a route that avoids locations where accidents or construction have occurred. The traffic condition consideration unit also builds a system in which the generation AI proposes the optimal detour route based on accident and construction information. For example, it displays a route that takes into account the safety of the detour route. In addition, the traffic condition consideration unit has the generation AI predict traffic accidents and construction and proposes a route to avoid future accidents or construction. For example, it selects a route that avoids locations where accidents or construction are predicted. In this way, travel safety is improved by acquiring traffic accident and construction information in real time and proposing the optimal detour route.

[0044] The traffic condition consideration unit can propose the optimal transfer route by taking into account the operating status of public transportation. For example, the generation AI of the traffic condition consideration unit obtains the operating status of public transportation in real time and proposes the optimal transfer route. For example, it selects the optimal route based on train and bus operation information. The traffic condition consideration unit also builds a system in which the generation AI proposes the optimal transfer route based on operation status data. For example, it displays a route with smooth transfers. The traffic condition consideration unit also has the generation AI predict public transportation operation and propose a transfer route that takes future operation status into account. For example, it selects a route that avoids routes that are likely to be delayed. In this way, travel efficiency is improved by considering the operating status of public transportation and proposing the optimal transfer route.

[0045] The traffic situation consideration unit can suggest carpooling with other drivers and reduce traffic volume. In this case, for example, the generation AI analyzes route information of other drivers and suggests carpooling. For example, it matches drivers heading to the same destination. The traffic situation consideration unit also builds a system in which the generation AI suggests optimal routes based on carpool information. For example, it displays routes where carpooling is possible. The traffic situation consideration unit also predicts carpooling and suggests routes where carpooling will be possible in the future. For example, it selects a route that avoids times when carpooling is more common. In this way, traffic volume can be reduced by suggesting carpooling with other drivers.

[0046] The energy consumption minimization unit can optimize the vehicle's air conditioning system and reduce energy consumption. For example, the generation AI in the energy consumption minimization unit analyzes data on the vehicle's air conditioning system and proposes optimal settings. For example, it adjusts the temperature and airflow of the air conditioning to reduce energy consumption. The energy consumption minimization unit also builds a system in which the generation AI proposes optimal settings based on data on the air conditioning system. For example, it displays air conditioning settings that are energy efficient. The energy consumption minimization unit also uses the generation AI to make predictions on the air conditioning system and propose settings to reduce future energy consumption. For example, it selects air conditioning settings that take into account the outside temperature and interior temperature. This optimizes the vehicle's air conditioning system, reduces energy consumption, and improves battery life.

[0047] The energy consumption minimization unit can optimize the vehicle's speed or acceleration pattern to improve energy efficiency. For example, the energy consumption minimization unit uses a generation AI to analyze data on the vehicle's speed and acceleration pattern and propose optimal driving methods. For example, it recommends a driving method that maintains a constant speed to improve energy efficiency. The energy consumption minimization unit also builds a system in which the generation AI proposes optimal driving methods based on speed and acceleration pattern data. For example, it displays driving methods that avoid sudden acceleration and deceleration. The energy consumption minimization unit also uses a generation AI to predict speed and acceleration patterns and propose driving methods to improve future energy efficiency. For example, it selects driving methods that take traffic and road conditions into consideration. This optimizes the vehicle's speed and acceleration pattern, improving energy efficiency and improving battery life.

[0048] The energy consumption minimization unit can suggest charging stations that use other energy sources. For example, the generation AI of the energy consumption minimization unit obtains information on charging stations that use renewable energy such as solar or wind power in real time and suggests them to the user. For example, it selects stations that use solar power generation. The energy consumption minimization unit also builds a system in which the generation AI suggests optimal charging stations based on renewable energy information. For example, it displays stations that use wind power generation. The energy consumption minimization unit also predicts renewable energy and suggests charging stations that will be available in the future. For example, it selects stations that take into account the amount of solar or wind power generated. This allows the suggestion of charging stations that use other energy sources, thereby reducing the environmental impact.

[0049] The energy consumption minimization unit can provide vehicle maintenance information to minimize energy consumption. For example, the generation AI analyzes vehicle maintenance data and provides maintenance information to minimize energy consumption. For example, it notifies the user of tire pressure and engine oil change times. The energy consumption minimization unit also builds a system in which the generation AI provides optimal maintenance information based on the maintenance data. For example, it displays maintenance methods to improve energy efficiency. The energy consumption minimization unit also predicts maintenance and provides maintenance information to minimize future energy consumption. For example, it selects maintenance methods that take into account the deterioration status of parts. As a result, vehicle efficiency is improved by providing vehicle maintenance information to minimize energy consumption.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The route suggestion unit can learn the user's driving style and suggest the optimal route based on that driving style. For example, the generation AI collects the user's driving data and analyzes the frequency of sudden acceleration and deceleration. This allows it to suggest the optimal route based on the driving style. For example, for a user who frequently accelerates suddenly, it would suggest a route with fewer traffic lights. In addition, to learn the user's driving style, the route suggestion unit analyzes data obtained from the vehicle's sensors and identifies driving patterns. Based on this, it suggests an energy-efficient route. For example, the generation AI calculates the optimal route for the user's driving style in real time based on the user's driving history. For example, it selects a route with fewer sudden decelerations to reduce battery consumption. This improves driving efficiency by suggesting the optimal route based on the user's driving style.

[0052] The route suggestion unit can obtain weather data in real time and propose the optimal route according to the weather conditions. For example, the generation AI obtains weather data in real time and proposes the optimal route taking into account weather conditions such as rain and snow. For example, on rainy days, it selects roads that are less slippery. The route suggestion unit also evaluates the safety of routes based on weather data and proposes the optimal route to the user. For example, on snowy days, it prioritizes routes that have been cleared of snow. The route suggestion unit also analyzes weather forecast data and proposes routes that take future weather changes into consideration. For example, if a storm is forecast, it proposes an evacuation route. This improves safety by proposing the optimal route according to weather conditions.

[0053] The route suggestion unit can suggest routes based on the user's interests in tourist spots or restaurants. For example, the generation AI analyzes the user's interests and preferences and suggests routes that include tourist spots and restaurants. For example, it selects a route that passes through restaurants in the user's favorite genre. The route suggestion unit also suggests routes based on the user's interests, based on the user's past search history and visit history. For example, it suggests routes that include tourist spots that the user has not visited. The route suggestion unit also suggests routes that include specific events or festivals based on the user's interests. For example, it selects a route that passes through places where events that the user is interested in are being held. In this way, suggesting routes based on the user's interests increases the enjoyment of travel.

[0054] The route suggestion unit can propose routes that take into account integration with other means of transportation. For example, the generation AI obtains train and bus operation information in real time and proposes routes that take into account integration with other means of transportation. For example, it selects a route that allows for smooth train transfers. In addition, to consider integration with other means of transportation, the route suggestion unit analyzes public transportation timetables and proposes the optimal route. For example, it selects a route that passes through bus stops and stations. In addition, the route suggestion unit adjusts the route in real time, taking into account integration with trains and buses, according to the user's travel needs. For example, it recalculates the route based on train delay information. This improves travel efficiency by proposing routes that take into account integration with other means of transportation.

[0055] The charging station information providing unit can obtain the congestion status of charging stations in real time and suggest stations with short waiting times. For example, the generation AI obtains the congestion status of charging stations in real time and suggests stations with short waiting times. For example, it will prioritize selecting stations with low congestion. The charging station information providing unit also builds a system in which the generation AI suggests the optimal charging station based on the congestion status data. For example, it will display stations with short waiting times in real time. The charging station information providing unit also uses the generation AI to predict congestion at charging stations and suggest stations that will avoid future congestion. For example, it will select stations that avoid times when congestion is predicted. This improves charging efficiency by suggesting charging stations with short waiting times.

[0056] The charging station information provider can take into account charging station fee information and suggest cost-effective stations. For example, the generation AI obtains charging station fee information in real time and suggests cost-effective stations. For example, it may prioritize stations with low fees. The charging station information provider also builds a system in which the generation AI suggests optimal charging stations based on fee information. For example, it may display stations taking into account fee and charging speed. The charging station information provider also uses the generation AI to predict charging station fees and suggest stations that take future fee fluctuations into account. For example, it may select stations where charging can be done before fees increase. This allows the system to suggest cost-effective charging stations, thereby reducing the user's charging costs.

[0057] The charging time optimization unit can monitor the battery's deterioration status and suggest the optimal charging pattern. For example, the generation AI monitors the battery's deterioration status in real time and suggests the optimal charging pattern. For example, it selects a charging method according to the battery's health state. The charging time optimization unit also builds a system in which the generation AI suggests the optimal charging pattern based on the deterioration status data. For example, it displays a charging method to extend the battery's lifespan. The charging time optimization unit also uses the generation AI to predict battery deterioration and suggest a charging pattern to prevent future deterioration. For example, it selects an appropriate charging method before deterioration progresses. In this way, the battery's lifespan can be extended by monitoring the battery's deterioration status and suggesting the optimal charging pattern.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The route suggestion unit uses the generation AI to suggest the optimal route based on the destination entered by the user. For example, if the user enters a prompt such as "Please tell me the shortest route to my destination," the generation AI analyzes the prompt and calculates the optimal route. Step 2: The charging station information provider provides location information of charging stations along the route proposed by the route suggestor. For example, if the user inputs a prompt such as "If I need to charge along the way, please tell me the best charging station," the generation AI analyzes the prompt and suggests the location of the best charging station. Step 3: The charging time optimization unit optimizes the charging time based on the location information of the charging station provided by the charging station information provision unit. For example, if a user inputs a prompt such as "Please tell me the best charging station to minimize the charging time," the generation AI analyzes the prompt and suggests the best charging station and charging time. Step 4: The traffic condition consideration unit considers the traffic conditions of the route proposed by the route suggestion unit. For example, if a user inputs a prompt such as "Please tell me the best route to avoid traffic jams," the generation AI analyzes the prompt and proposes the best route taking traffic conditions into account. Step 5: The energy consumption minimization unit minimizes the energy consumption of the route proposed by the route suggestion unit. For example, if a user inputs a prompt such as "Please tell me the optimal route to minimize energy consumption," the generation AI analyzes the prompt and proposes a route that minimizes energy consumption.

[0060] (Example 2) A route suggestion system according to an embodiment of the present invention is a system that proposes optimal routes for electric vehicles (EVs) using a generation AI. As a result, the route suggestion system can provide efficient routes to EV users and optimize the locations of charging stations and charging times.

[0061] A route proposal system according to an embodiment includes a route proposal unit, a charging station information provision unit, a charging time optimization unit, a traffic condition consideration unit, and an energy consumption minimization unit. The route proposal unit uses a generation AI to propose an optimal route based on a destination input by a user. For example, when a user inputs a prompt such as "Please tell me the shortest route to my destination," the generation AI analyzes the prompt and calculates an optimal route. The charging station information provision unit provides location information of charging stations along the route proposed by the route proposal unit. For example, when a user inputs a prompt such as "Please tell me the best charging station if I need to charge along the way," the generation AI analyzes the prompt and proposes the location of the best charging station. The charging time optimization unit optimizes the charging time based on the location information of charging stations provided by the charging station information provision unit. For example, when a user inputs a prompt such as "Please tell me the best charging station to minimize charging time," the generation AI analyzes the prompt and proposes the best charging station and charging time. The traffic condition consideration unit considers the traffic conditions of the route proposed by the route proposal unit. For example, when a prompt such as "Please tell me the best route to avoid traffic jams" is input, the generation AI analyzes the prompt and proposes an optimal route that takes traffic conditions into consideration. The energy consumption minimization unit minimizes the energy consumption of the route proposed by the route proposal unit. For example, when a prompt such as "Please tell me the best route to minimize energy consumption" is input, the generation AI analyzes the prompt and proposes a route that minimizes energy consumption. As a result, the route proposal system according to the embodiment can provide an efficient route to an EV user and optimize the location of charging stations and charging time.

[0062] The route suggestion unit can learn the user's driving style and suggest the optimal route based on that driving style. For example, the generation AI in the route suggestion unit collects the user's driving data and analyzes the frequency of sudden acceleration and deceleration. This allows the unit to suggest the optimal route based on the driving style. For example, for a user who frequently accelerates suddenly, it would suggest a route with fewer traffic lights. In addition, to learn the user's driving style, the route suggestion unit analyzes data obtained from the vehicle's sensors and identifies driving patterns. Based on this, it suggests an energy-efficient route. For example, the generation AI calculates the optimal route for the user's driving style in real time based on the user's driving history. For example, it selects a route with fewer sudden decelerations to reduce battery consumption. This improves driving efficiency by suggesting the optimal route based on the user's driving style.

[0063] The route suggestion unit can acquire weather data in real time and propose the optimal route according to the weather conditions. For example, the generation AI in the route suggestion unit acquires weather data in real time and proposes the optimal route taking into account weather conditions such as rain and snow. For example, on rainy days, it selects roads that are less slippery. The route suggestion unit also evaluates the safety of routes based on weather data and proposes the optimal route to the user. For example, on snowy days, it prioritizes routes that have been cleared of snow. The route suggestion unit also analyzes weather forecast data and proposes routes that take future weather changes into consideration. For example, if a storm is forecast, it proposes an evacuation route. This improves safety by proposing the optimal route according to weather conditions.

[0064] The route suggestion unit can estimate the user's stress level using the emotion estimation function and suggest a route to reduce the stress level. For example, the route suggestion unit uses the emotion estimation function to analyze the user's stress level in real time and suggest a route to reduce stress. For example, it selects a route with less congestion. The route suggestion unit also analyzes facial expressions and voice data to estimate the user's stress level, and suggests a relaxing route if the stress level is high. For example, it selects a route with good scenery. The route suggestion unit also adjusts the route in real time to reduce the user's stress using the generation AI based on the emotion estimation data. For example, it selects a route with good scenery. This allows the system to suggest a route to reduce the user's stress level, enabling a more comfortable driving experience.

[0065] The route suggestion unit can suggest routes based on the user's interests in tourist spots or restaurants. For example, the generation AI analyzes the user's interests and preferences and suggests routes that include tourist spots and restaurants. For example, it selects a route that passes through restaurants in the user's favorite genre. The route suggestion unit also suggests routes based on the user's interests, based on the user's past search history and visit history. For example, it suggests routes that include tourist spots that the user has not visited. The route suggestion unit also suggests routes that include specific events or festivals based on the user's interests. For example, it selects a route that passes through places where events that the user is interested in are being held. In this way, suggesting routes based on the user's interests increases the enjoyment of travel.

[0066] The route suggestion unit can propose routes that take into account integration with other means of transportation. For example, the generation AI obtains train and bus operation information in real time and proposes routes that take into account integration with other means of transportation. For example, it selects a route that allows smooth train transfers. In addition, to consider integration with other means of transportation, the generation AI analyzes public transportation timetables and proposes the optimal route. For example, it selects a route that passes through bus stops and stations. In addition, the generation AI adjusts the route in real time, taking into account integration with trains and buses, according to the user's travel needs. For example, it recalculates the route based on train delay information. This improves travel efficiency by proposing routes that take into account integration with other means of transportation.

[0067] The route suggestion unit can use the emotion estimation function to suggest relaxing music or podcasts to the user while guiding the user along the route. For example, the route suggestion unit uses the emotion estimation function to analyze the user's emotional state and suggest relaxing music or podcasts. For example, relaxing music is played when stress levels are high. The route suggestion unit also uses the generation AI to suggest appropriate music or podcasts during route guidance based on the user's emotional data. For example, it provides content that is relaxing during long-distance travel. The route suggestion unit also uses the emotion estimation function to adjust the music or podcasts in real time according to the user's emotional state. For example, appropriate content is played whenever emotions change. This allows the user to enjoy a more comfortable driving experience by suggesting relaxing music or podcasts.

[0068] The charging station information providing unit can obtain the congestion status of charging stations in real time and suggest stations with short waiting times. For example, the generation AI of the charging station information providing unit obtains the congestion status of charging stations in real time and suggests stations with short waiting times. For example, stations with low congestion levels are selected preferentially. The charging station information providing unit also builds a system in which the generation AI suggests the optimal charging station based on the congestion status data. For example, stations with short waiting times are displayed in real time. The charging station information providing unit also uses the generation AI to predict congestion at charging stations and suggest stations that will avoid future congestion. For example, stations that avoid times when congestion is predicted are selected. This improves charging efficiency by suggesting charging stations with short waiting times.

[0069] The charging station information providing unit can take into account charging station fee information and suggest cost-effective stations. For example, the generation AI of the charging station information providing unit obtains charging station fee information in real time and suggests cost-effective stations. For example, it may prioritize stations with low fees. The charging station information providing unit also builds a system in which the generation AI suggests optimal charging stations based on fee information. For example, it may display stations taking into account fee and charging speed. The charging station information providing unit also uses the generation AI to predict charging station fees and suggest stations that take future fee fluctuations into account. For example, it may select stations where charging can be done before fees increase. This allows the user's charging costs to be reduced by suggesting cost-effective charging stations.

[0070] The charging station information providing unit provides facility information about charging stations and can suggest stations that meet the user's needs. For example, the generation AI of the charging station information providing unit obtains facility information about charging stations in real time and suggests stations that meet the user's needs. For example, it selects stations that have Wi-Fi and restrooms. The charging station information providing unit also builds a system in which the generation AI suggests the optimal charging station based on the facility information. For example, it displays stations that take into account the level of facility availability. The charging station information providing unit also uses the generation AI to predict charging station facilities and suggest stations that take into account the future level of facility availability. For example, it selects stations where new facilities are scheduled to be added. This improves convenience during charging by suggesting charging stations that meet the user's needs.

[0071] The charging station information providing unit can suggest highly rated charging stations based on reviews from other EV users. For example, the generation AI in the charging station information providing unit analyzes reviews from other EV users and suggests highly rated charging stations. For example, stations with high review ratings are selected preferentially. The charging station information providing unit also builds a system in which the generation AI suggests optimal charging stations based on review information. For example, the content of reviews is analyzed and station ratings are displayed. The generation AI in the charging station information providing unit also analyzes review trends and suggests stations that are likely to become more highly rated in the future. For example, stations where new facilities are scheduled to be added are selected. In this way, by suggesting highly rated charging stations, user satisfaction is improved.

[0072] The charging station information providing unit can use the emotion estimation function to suggest charging station activities that will help the user relax. For example, the charging station information providing unit uses the emotion estimation function to analyze the user's emotional state and suggest charging stations that offer relaxing activities. For example, it selects stations with reading spaces or movie viewing facilities. The charging station information providing unit also builds a system in which a generation AI suggests charging stations that offer relaxing activities based on the user's emotional data. For example, it displays stations that have a relaxing environment. The charging station information providing unit also uses the emotion estimation function to adjust in real time charging stations that offer activities according to the user's emotional state. For example, if stress is high, it will prioritize suggesting stations that offer relaxing activities. This allows the user to make effective use of their charging time by suggesting relaxing activities.

[0073] The charging time optimization unit can monitor the battery's deterioration status and propose the optimal charging pattern. For example, the generation AI of the charging time optimization unit monitors the battery's deterioration status in real time and proposes the optimal charging pattern. For example, it selects a charging method according to the battery's health state. The charging time optimization unit also builds a system in which the generation AI proposes the optimal charging pattern based on the deterioration status data. For example, it displays a charging method to extend the battery's lifespan. The charging time optimization unit also uses the generation AI to predict battery deterioration and propose a charging pattern to prevent future deterioration. For example, it selects an appropriate charging method before deterioration progresses. In this way, the battery's lifespan can be extended by monitoring the battery's deterioration status and proposing the optimal charging pattern.

[0074] The charging time optimization unit can take into account the charging speed of the charging station and suggest the station that offers the fastest charging. For example, the generation AI in the charging time optimization unit obtains charging speed data of charging stations in real time and suggests the station that offers the fastest charging. For example, it prioritizes selecting stations with the fastest charging speed. The charging time optimization unit also builds a system in which the generation AI suggests the optimal charging station based on the charging speed data. For example, it displays stations that take into account charging speed and waiting time. The charging time optimization unit also has the generation AI predict the charging speed of the charging station and suggest stations that take into account future charging speed. For example, it selects stations where the charging speed is expected to increase. This allows the charging time to be shortened by suggesting the station that offers the fastest charging.

[0075] The charging time optimization unit can use the emotion estimation function to suggest activities that allow the user to relax while charging. For example, the charging time optimization unit uses the emotion estimation function to analyze the user's emotional state and suggest activities that allow the user to relax while charging. For example, it provides a space where the user can read or watch a movie. The charging time optimization unit also builds a system in which a generation AI suggests activities that allow the user to relax while charging based on the user's emotional data. For example, it displays charging stations with a relaxing environment. The charging time optimization unit also uses the emotion estimation function to adjust activities in real time according to the user's emotional state. For example, it suggests relaxing activities when stress is high. This allows the user to make effective use of the time spent charging by suggesting activities that allow the user to relax while charging.

[0076] The charging time optimization unit can suggest nearby tourist spots or activities that can be done while charging. For example, the generation AI of the charging time optimization unit obtains information on tourist spots and activities around the charging station in real time and suggests them to the user. For example, it displays tourist spots that can be visited while charging. The charging time optimization unit also builds a system in which the generation AI suggests the optimal charging station based on information on nearby tourist spots and activities. For example, it selects stations with an abundance of tourist spots and activities. The charging time optimization unit also predicts tourist spots and activities around the charging station and suggests places that will be enjoyable in the future. For example, it selects a route that includes newly opened tourist spots. This allows the time spent charging to be used effectively by suggesting tourist spots and activities that can be done while charging.

[0077] The charging time optimization unit suggests sharing of charging stations with other EV users, enabling efficient charging. For example, the generation AI in the charging time optimization unit analyzes the charging schedules of other EV users and suggests sharing of charging stations. For example, it matches users who wish to charge during the same time period. The charging time optimization unit also builds a system in which the generation AI suggests the optimal charging station based on sharing information. For example, it displays stations that are available for sharing. The charging time optimization unit also predicts charging station sharing and suggests stations that will be available for sharing in the future. For example, it selects stations that avoid time periods when sharing increases. This improves charging efficiency by suggesting sharing of charging stations with other EV users.

[0078] The charging time optimization unit can use the emotion estimation function to suggest entertainment that the user can enjoy while charging. For example, the charging time optimization unit uses the emotion estimation function to analyze the user's emotional state and suggest entertainment that can be enjoyed while charging. For example, it can provide games or movies. The charging time optimization unit also builds a system in which a generative AI suggests entertainment that can be enjoyed while charging based on the user's emotional data. For example, it can display charging stations with a relaxing environment. The charging time optimization unit also uses the emotion estimation function to adjust entertainment in real time according to the user's emotional state. For example, it can provide relaxing entertainment if the user is under high stress. This allows the time spent charging to be used effectively by suggesting entertainment that the user can enjoy while charging.

[0079] The traffic condition consideration unit can analyze past traffic data and propose routes that avoid predicted congestion. In the traffic condition consideration unit, for example, the generation AI analyzes past traffic data and proposes routes that avoid predicted congestion. For example, it selects the optimal route based on past congestion patterns. The traffic condition consideration unit also builds a system in which the generation AI proposes the optimal route based on traffic data. For example, it displays a route that takes into account time periods with less congestion. In addition, the traffic condition consideration unit has the generation AI predict traffic data and proposes routes that avoid future congestion. For example, it selects a route that avoids time periods when congestion is predicted. In this way, travel time can be reduced by proposing a route that avoids predicted congestion.

[0080] The traffic condition consideration unit can acquire traffic accident or construction information in real time and propose the optimal detour route. In the traffic condition consideration unit, for example, the generation AI acquires traffic accident and construction information in real time and proposes the optimal detour route. For example, it selects a route that avoids locations where accidents or construction have occurred. The traffic condition consideration unit also builds a system in which the generation AI proposes the optimal detour route based on accident and construction information. For example, it displays a route that takes into account the safety of the detour route. In addition, the traffic condition consideration unit has the generation AI predict traffic accidents and construction and proposes a route to avoid future accidents or construction. For example, it selects a route that avoids locations where accidents or construction are predicted. In this way, travel safety is improved by acquiring traffic accident and construction information in real time and proposing the optimal detour route.

[0081] The traffic condition consideration unit can propose the optimal transfer route by taking into account the operating status of public transportation. For example, the generation AI of the traffic condition consideration unit obtains the operating status of public transportation in real time and proposes the optimal transfer route. For example, it selects the optimal route based on train and bus operation information. The traffic condition consideration unit also builds a system in which the generation AI proposes the optimal transfer route based on operation status data. For example, it displays a route with smooth transfers. The traffic condition consideration unit also has the generation AI predict public transportation operation and propose a transfer route that takes future operation status into account. For example, it selects a route that avoids routes that are likely to be delayed. In this way, travel efficiency is improved by considering the operating status of public transportation and proposing the optimal transfer route.

[0082] The traffic situation consideration unit can suggest carpooling with other drivers and reduce traffic volume. In this case, for example, the generation AI analyzes route information of other drivers and suggests carpooling. For example, it matches drivers heading to the same destination. The traffic situation consideration unit also builds a system in which the generation AI suggests optimal routes based on carpool information. For example, it displays routes where carpooling is possible. The traffic situation consideration unit also predicts carpooling and suggests routes where carpooling will be possible in the future. For example, it selects a route that avoids times when carpooling is more common. In this way, traffic volume can be reduced by suggesting carpooling with other drivers.

[0083] The traffic condition consideration unit can use the emotion estimation function to suggest a scenic route that allows the user to relax. For example, the traffic condition consideration unit uses the emotion estimation function to analyze the user's emotional state and suggest a scenic route that allows the user to relax. For example, it selects a route with a lot of nature. The traffic condition consideration unit also builds a system in which a generation AI suggests scenic routes based on the user's emotional data. For example, it displays routes with a relaxing environment. The traffic condition consideration unit also uses the emotion estimation function to adjust scenic routes in real time according to the user's emotional state. For example, if stress is high, it will prioritize suggesting a relaxing route. This allows the user to drive more comfortably by suggesting scenic routes that allow them to relax.

[0084] The energy consumption minimization unit can optimize the vehicle's air conditioning system and reduce energy consumption. For example, the generation AI in the energy consumption minimization unit analyzes data on the vehicle's air conditioning system and proposes optimal settings. For example, it adjusts the temperature and airflow of the air conditioning to reduce energy consumption. The energy consumption minimization unit also builds a system in which the generation AI proposes optimal settings based on data on the air conditioning system. For example, it displays air conditioning settings that are energy efficient. The energy consumption minimization unit also uses the generation AI to make predictions on the air conditioning system and propose settings to reduce future energy consumption. For example, it selects air conditioning settings that take into account the outside temperature and interior temperature. This optimizes the vehicle's air conditioning system, reduces energy consumption, and improves battery life.

[0085] The energy consumption minimization unit can optimize the vehicle's speed or acceleration pattern to improve energy efficiency. For example, the energy consumption minimization unit uses a generation AI to analyze data on the vehicle's speed and acceleration pattern and propose optimal driving methods. For example, it recommends a driving method that maintains a constant speed to improve energy efficiency. The energy consumption minimization unit also builds a system in which the generation AI proposes optimal driving methods based on speed and acceleration pattern data. For example, it displays driving methods that avoid sudden acceleration and deceleration. The energy consumption minimization unit also uses a generation AI to predict speed and acceleration patterns and propose driving methods to improve future energy efficiency. For example, it selects driving methods that take traffic and road conditions into consideration. This optimizes the vehicle's speed and acceleration pattern, improving energy efficiency and improving battery life.

[0086] The energy consumption minimization unit can use the emotion estimation function to suggest an energy-efficient driving style to reduce the user's stress. For example, the energy consumption minimization unit uses the emotion estimation function to analyze the user's emotional state and suggest an energy-efficient driving style to reduce stress. For example, it recommends a driving method that allows the user to relax. The energy consumption minimization unit also builds a system in which a generative AI suggests an energy-efficient driving style based on the user's emotional data. For example, it displays driving methods that result in less stress. The energy consumption minimization unit also uses the emotion estimation function to adjust an energy-efficient driving style in real time according to the user's emotional state. For example, if stress is high, it will prioritize suggesting a relaxing driving method. This allows the user to drive more comfortably by suggesting an energy-efficient driving style to reduce stress.

[0087] The energy consumption minimization unit can suggest charging stations that use other energy sources. For example, the generation AI of the energy consumption minimization unit obtains information on charging stations that use renewable energy such as solar or wind power in real time and suggests them to the user. For example, it selects stations that use solar power generation. The energy consumption minimization unit also builds a system in which the generation AI suggests optimal charging stations based on renewable energy information. For example, it displays stations that use wind power generation. The energy consumption minimization unit also predicts renewable energy and suggests charging stations that will be available in the future. For example, it selects stations that take into account the amount of solar or wind power generated. This allows the suggestion of charging stations that use other energy sources, thereby reducing the environmental impact.

[0088] The energy consumption minimization unit can provide vehicle maintenance information to minimize energy consumption. For example, the generation AI analyzes vehicle maintenance data and provides maintenance information to minimize energy consumption. For example, it notifies the user of tire pressure and engine oil change times. The energy consumption minimization unit also builds a system in which the generation AI provides optimal maintenance information based on the maintenance data. For example, it displays maintenance methods to improve energy efficiency. The energy consumption minimization unit also predicts maintenance and provides maintenance information to minimize future energy consumption. For example, it selects maintenance methods that take into account the deterioration status of parts. As a result, vehicle efficiency is improved by providing vehicle maintenance information to minimize energy consumption.

[0089] The energy consumption minimization unit can use the emotion estimation function to suggest an energy-efficient driving route that allows the user to relax. For example, the energy consumption minimization unit uses the emotion estimation function to analyze the user's emotional state and suggest an energy-efficient driving route that allows the user to relax. For example, it selects a route with less congestion. The energy consumption minimization unit also builds a system in which a generation AI suggests energy-efficient driving routes based on the user's emotional data. For example, it displays routes with a relaxing environment. The energy consumption minimization unit also uses the emotion estimation function to adjust the energy-efficient driving route in real time according to the user's emotional state. For example, if stress is high, it will prioritize suggesting a relaxing route. This allows the user to drive comfortably by suggesting an energy-efficient driving route that allows them to relax.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The route suggestion unit can learn the user's driving style and suggest the optimal route based on that driving style. For example, the generation AI collects the user's driving data and analyzes the frequency of sudden acceleration and deceleration. This allows it to suggest the optimal route based on the driving style. For example, for a user who frequently accelerates suddenly, it would suggest a route with fewer traffic lights. In addition, to learn the user's driving style, the route suggestion unit analyzes data obtained from the vehicle's sensors and identifies driving patterns. Based on this, it suggests an energy-efficient route. For example, the generation AI calculates the optimal route for the user's driving style in real time based on the user's driving history. For example, it selects a route with fewer sudden decelerations to reduce battery consumption. This improves driving efficiency by suggesting the optimal route based on the user's driving style.

[0092] The route suggestion unit can obtain weather data in real time and propose the optimal route according to the weather conditions. For example, the generation AI obtains weather data in real time and proposes the optimal route taking into account weather conditions such as rain and snow. For example, on rainy days, it selects roads that are less slippery. The route suggestion unit also evaluates the safety of routes based on weather data and proposes the optimal route to the user. For example, on snowy days, it prioritizes routes that have been cleared of snow. The route suggestion unit also analyzes weather forecast data and proposes routes that take future weather changes into consideration. For example, if a storm is forecast, it proposes an evacuation route. This improves safety by proposing the optimal route according to weather conditions.

[0093] The route suggestion unit can estimate the user's stress level using the emotion estimation function and suggest a route to reduce the stress level. For example, the emotion estimation function can be used to analyze the user's stress level in real time and suggest a route to reduce stress. For example, a route with less congestion can be selected. The route suggestion unit can also analyze facial expressions and voice data to estimate the user's stress level, and if stress is high, suggest a relaxing route. For example, a route with good scenery can be selected. The route suggestion unit can also use the emotion estimation data to adjust the route in real time to reduce the user's stress. For example, a route with good scenery can be selected. This allows the generation AI to adjust the route in real time to reduce the user's stress level.

[0094] The route suggestion unit can suggest routes based on the user's interests in tourist spots or restaurants. For example, the generation AI analyzes the user's interests and preferences and suggests routes that include tourist spots and restaurants. For example, it selects a route that passes through restaurants in the user's favorite genre. The route suggestion unit also suggests routes based on the user's interests, based on the user's past search history and visit history. For example, it suggests routes that include tourist spots that the user has not visited. The route suggestion unit also suggests routes that include specific events or festivals based on the user's interests. For example, it selects a route that passes through places where events that the user is interested in are being held. In this way, suggesting routes based on the user's interests increases the enjoyment of travel.

[0095] The route suggestion unit can propose routes that take into account integration with other means of transportation. For example, the generation AI obtains train and bus operation information in real time and proposes routes that take into account integration with other means of transportation. For example, it selects a route that allows for smooth train transfers. In addition, to consider integration with other means of transportation, the route suggestion unit analyzes public transportation timetables and proposes the optimal route. For example, it selects a route that passes through bus stops and stations. In addition, the route suggestion unit adjusts the route in real time, taking into account integration with trains and buses, according to the user's travel needs. For example, it recalculates the route based on train delay information. This improves travel efficiency by proposing routes that take into account integration with other means of transportation.

[0096] The route suggestion unit can use the emotion estimation function to suggest relaxing music or podcasts to the user while guiding the user along the route. For example, the emotion estimation function can be used to analyze the user's emotional state and suggest relaxing music or podcasts. For example, relaxing music can be played when the user is highly stressed. The route suggestion unit also uses the generation AI to suggest appropriate music or podcasts during route guidance based on the user's emotional data. For example, it can provide relaxing content during long-distance travel. The route suggestion unit also uses the emotion estimation function to adjust the music or podcasts in real time according to the user's emotional state. For example, it can play appropriate content whenever the user's emotions change. This allows the user to enjoy a more comfortable driving experience by suggesting relaxing music or podcasts.

[0097] The charging station information providing unit can obtain the congestion status of charging stations in real time and suggest stations with short waiting times. For example, the generation AI obtains the congestion status of charging stations in real time and suggests stations with short waiting times. For example, it will prioritize selecting stations with low congestion. The charging station information providing unit also builds a system in which the generation AI suggests the optimal charging station based on the congestion status data. For example, it will display stations with short waiting times in real time. The charging station information providing unit also uses the generation AI to predict congestion at charging stations and suggest stations that will avoid future congestion. For example, it will select stations that avoid times when congestion is predicted. This improves charging efficiency by suggesting charging stations with short waiting times.

[0098] The charging station information provider can take into account charging station fee information and suggest cost-effective stations. For example, the generation AI obtains charging station fee information in real time and suggests cost-effective stations. For example, it may prioritize stations with low fees. The charging station information provider also builds a system in which the generation AI suggests optimal charging stations based on fee information. For example, it may display stations taking into account fee and charging speed. The charging station information provider also uses the generation AI to predict charging station fees and suggest stations that take future fee fluctuations into account. For example, it may select stations where charging can be done before fees increase. This allows the system to suggest cost-effective charging stations, thereby reducing the user's charging costs.

[0099] The charging station information provider can use the emotion estimation function to suggest charging station activities that will help the user relax. For example, it can use the emotion estimation function to analyze the user's emotional state and suggest charging stations that offer relaxing activities. For example, it can select stations with reading spaces or movie watching facilities. The charging station information provider can also build a system in which a generation AI suggests charging stations that offer relaxing activities based on the user's emotional data. For example, it can display stations with a relaxing environment. The charging station information provider can also use the emotion estimation function to adjust in real time charging stations that offer activities according to the user's emotional state. For example, if stress is high, it can prioritize suggesting stations that offer relaxing activities. This allows the user to make effective use of their charging time by suggesting relaxing activities.

[0100] The charging time optimization unit can monitor the battery's deterioration status and suggest the optimal charging pattern. For example, the generation AI monitors the battery's deterioration status in real time and suggests the optimal charging pattern. For example, it selects a charging method according to the battery's health state. The charging time optimization unit also builds a system in which the generation AI suggests the optimal charging pattern based on the deterioration status data. For example, it displays a charging method to extend the battery's lifespan. The charging time optimization unit also uses the generation AI to predict battery deterioration and suggest a charging pattern to prevent future deterioration. For example, it selects an appropriate charging method before deterioration progresses. In this way, the battery's lifespan can be extended by monitoring the battery's deterioration status and suggesting the optimal charging pattern.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The route suggestion unit uses the generation AI to suggest the optimal route based on the destination entered by the user. For example, if the user enters a prompt such as "Please tell me the shortest route to my destination," the generation AI analyzes the prompt and calculates the optimal route. Step 2: The charging station information provider provides location information of charging stations along the route proposed by the route suggestor. For example, if the user inputs a prompt such as "If I need to charge along the way, please tell me the best charging station," the generation AI analyzes the prompt and suggests the location of the best charging station. Step 3: The charging time optimization unit optimizes the charging time based on the location information of the charging station provided by the charging station information provision unit. For example, if a user inputs a prompt such as "Please tell me the best charging station to minimize the charging time," the generation AI analyzes the prompt and suggests the best charging station and charging time. Step 4: The traffic condition consideration unit considers the traffic conditions of the route proposed by the route suggestion unit. For example, if a user inputs a prompt such as "Please tell me the best route to avoid traffic jams," the generation AI analyzes the prompt and proposes the best route taking traffic conditions into account. Step 5: The energy consumption minimization unit minimizes the energy consumption of the route proposed by the route suggestion unit. For example, if a user inputs a prompt such as "Please tell me the optimal route to minimize energy consumption," the generation AI analyzes the prompt and proposes a route that minimizes energy consumption.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A route suggestion unit using generation AI, a charging station information providing unit that provides location information of charging stations on the route proposed by the route proposing unit; a charging time optimization unit that optimizes a charging time based on the location information of the charging station provided by the charging station information providing unit; a traffic condition consideration unit that considers traffic conditions along the route proposed by the route proposal unit; an energy consumption minimization unit that minimizes the energy consumption of the route proposed by the route proposal unit. A system characterized by:

2. The charging station information providing unit Obtaining the congestion status of the charging station in real time and suggesting a station with a short waiting time 2. The system of claim 1.

3. The charging time optimization unit Monitors battery deterioration and suggests optimal charging patterns 2. The system of claim 1.

4. The traffic condition consideration unit Analyzes past traffic data and suggests routes that avoid predicted congestion 2. The system of claim 1.

5. The energy consumption minimization unit Optimize vehicle speed or acceleration patterns to improve energy efficiency 2. The system of claim 1.

6. The route suggestion unit Estimating a user's stress level and suggesting a route to reduce said stress level 2. The system of claim 1.

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