System

Abstract

The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims.SOLUTION: A system according to an embodiment includes a vehicle registration unit, a parking management unit, a rental management unit, and a delivery cooperation unit. The vehicle registration unit registers a vehicle. The parking management unit parks the vehicle registered by the vehicle registration unit at the designated spot. The rental management unit rents out and returns the vehicle parked by the parking management unit. The delivery cooperation unit cooperates the vehicle rented by the rental management unit with the delivery service.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 limited means for making effective use of unused vehicles and bicycles, leaving room for improvement in providing efficient sharing services.

[0005] The system according to the embodiment aims to efficiently share unused vehicles and bicycles and to collaborate with delivery services. [Means for solving the problem]

[0006] The system according to the embodiment includes a vehicle registration unit, a parking management unit, a rental management unit, and a delivery coordination unit. The vehicle registration unit registers vehicles. The parking management unit parks vehicles registered by the vehicle registration unit in designated spots. The rental management unit rents out and returns vehicles parked by the parking management unit. The delivery coordination unit coordinates vehicles rented by the rental management unit with a delivery service. [Effects of the Invention]

[0007] The system according to the embodiment enables efficient sharing of unused vehicles and bicycles and can be linked to delivery services. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The short-term rental system according to an embodiment of the present invention is a system that allows users to register unused cars and bicycles using an app and rent them out to people who need them. This allows users to use vehicles efficiently and pick up and take home all the vehicles at a time of their choice, without having to wait long at home for the scheduled delivery time.

[0029] The short-term rental system according to the embodiment includes a vehicle registration unit, a parking management unit, a rental management unit, and a delivery coordination unit. The vehicle registration unit registers vehicles. For example, a user inputs the time periods when the user will not be using the vehicle into an app, allowing other users to rent the vehicle during those times. The vehicle registration unit also manages vehicle information and displays available time periods and locations. The parking management unit parks the vehicle registered by the vehicle registration unit in a designated spot. For example, the user parks the car or bicycle in a designated parking or bicycle parking lot at a commercial facility, store, or station. The parking management unit also provides information about the designated spot so that the user can easily find the vehicle. The rental management unit rents and returns the vehicle parked by the parking management unit. For example, after shopping, the user can use the app to rent a car from a parking lot and have their luggage delivered to their home. After use, the user returns the car to the designated location. The rental management unit also manages rental and return procedures and sends notifications to the user. The delivery cooperation unit cooperates with delivery services to cooperate with vehicles rented by the rental management unit. For example, by cooperating with online ordering at supermarkets and other delivery services, users can pick up and take home all their deliveries at any time they like, without having to wait long at home for the scheduled delivery time. As a result, the short-term rental system according to the embodiment allows users to use vehicles efficiently and pick up and take home all their deliveries at any time they like, without having to wait long at home for the scheduled delivery time.

[0030] The vehicle registration unit can analyze usage history and maintenance information using generation AI and propose optimal rental time slots. For example, the vehicle registration unit can use generation AI to analyze the vehicle's usage history and propose optimal rental time slots based on past usage patterns. For example, it can identify time slots during the daytime on weekdays when the vehicle is not being used and propose those times to rent out to other users. The vehicle registration unit can also analyze vehicle maintenance information using generation AI and predict when maintenance is required. For example, it can automatically detect when oil or tire changes are due and propose rental time slots that avoid those periods. The vehicle registration unit can also integrate the vehicle's usage history and maintenance information, and the generation AI can propose optimal rental time slots. For example, it can create an optimal vehicle usage schedule based on past usage frequency and maintenance history. This allows for efficient use by proposing optimal vehicle rental time slots.

[0031] The vehicle registration department can automatically check the vehicle's condition using sensors, cameras, and internal systems and issue a warning if an abnormality is detected. For example, the vehicle registration department introduces a system that automatically checks the vehicle's condition using sensors when registering a vehicle. For example, the system monitors the engine condition and tire pressure in real time and issues a warning if an abnormality is detected. The vehicle registration department also introduces a system that automatically checks the vehicle's exterior condition using cameras when registering a vehicle. For example, the system detects scratches and dents on the body and issues a warning if an abnormality is detected. The vehicle registration department also checks for abnormalities using software that diagnoses the vehicle's internal systems when registering a vehicle. For example, the system analyzes engine error logs and battery status and issues a warning if an abnormality is detected. In this way, safety can be ensured by automatically checking the vehicle's condition and issuing a warning if an abnormality is detected.

[0032] The parking management unit can use generative AI to analyze usage status in real time and suggest optimal parking and bicycle parking locations. For example, the parking management unit uses generative AI to build a system that analyzes the usage status of designated spots in real time and suggests available parking and bicycle parking locations. For example, it analyzes sensor information from parking lots and identifies available spaces. The parking management unit also introduces a system that analyzes the usage status of designated spots using generative AI and predicts the optimal parking and bicycle parking locations based on past data. For example, it predicts available locations during specific time periods and suggests them to users. The parking management unit also uses generative AI to build a system that analyzes the usage status of designated spots in real time and suggests the optimal parking and bicycle parking locations according to user needs. For example, it prioritizes suggesting locations close to the user's destination. This improves user convenience by analyzing the usage status of designated spots in real time and suggesting the optimal parking and bicycle parking locations.

[0033] The parking management unit can automatically record the vehicle's location using GPS, QR codes, and beacons, allowing users to easily find their vehicles. For example, the parking management unit introduces a system that automatically records the vehicle's location using GPS when the vehicle is parked or parked. For example, a specific location within the parking lot can be recorded using GPS and the user can check it using an app. The parking management unit also introduces a system that records the vehicle's location using QR codes when the vehicle is parked or parked. For example, the QR code installed in the parking space can be scanned and the location information can be saved in the app. The parking management unit also introduces a system that automatically records the vehicle's location using beacons when the vehicle is parked or parked. For example, the system can link with beacons installed in the parking lot to identify the vehicle's location. This automatically records the vehicle's location, allowing users to easily find their vehicle and improving convenience.

[0034] The parking management department can install solar power generation panels in designated spots to charge vehicle batteries while parking or bicycles are parked. For example, the parking management department installs solar power generation panels in designated spots and introduces a system that charges vehicle batteries while parking. For example, solar panels are installed above parking spaces to supply power. The parking management department also installs solar power generation panels in bicycle parking areas and builds a system that charges electric bicycle batteries while bicycles are parked. For example, solar panels are installed in bicycle parking racks to supply power. The parking management department also introduces a system that uses solar power generation panels to supply power to the entire designated spot. For example, the solar panels could provide power for parking lot lighting and surveillance cameras. This improves energy efficiency by charging vehicle batteries while parking or bicycles are parked.

[0035] The parking management unit can display information about designated spots using augmented reality (AR) technology, allowing users to intuitively find parking and bicycle parking spaces. For example, the parking management unit introduces a system that uses augmented reality (AR) technology to display information about designated spots through a smartphone camera. For example, vacant parking spaces can be displayed using AR, allowing users to intuitively find them. The parking management unit also builds a system that uses AR technology to display the availability of bicycle parking spaces in real time. For example, when a smartphone camera is pointed at a bicycle parking space, vacant racks are highlighted. The parking management unit also introduces a system that uses AR technology to display detailed information about designated spots. For example, parking fees and usage times can be displayed using AR, allowing users to easily check them. In this way, by displaying information about designated spots using augmented reality (AR) technology, users can intuitively find parking and bicycle parking spaces.

[0036] The rental management department can use generative AI to analyze a user's usage history and suggest optimal rental and return times. For example, the rental management department can use generative AI to analyze a user's past usage history and build a system that suggests optimal rental and return times. For example, it can suggest the most convenient time period for a user based on past usage patterns. The rental management department can also introduce a system that analyzes a user's past usage history with generative AI and predicts optimal rental and return times based on usage frequency and purpose. For example, it can take into account usage during commuting hours and weekends. The rental management department can also use generative AI to integrate a user's past usage history with data from other users and build a system that suggests optimal rental and return times. For example, it can suggest optimal time periods based on data from users in the same area. This improves user convenience by analyzing a user's past usage history and suggesting optimal rental and return times.

[0037] The rental management department can automatically check the condition of the vehicle using sensors, cameras, and internal systems and issue a warning if an abnormality is detected. For example, the rental management department introduces a system that automatically checks the condition of the vehicle using sensors when the vehicle is rented out. For example, the system monitors the engine condition and tire pressure in real time and issues a warning if an abnormality is detected. The rental management department also introduces a system that automatically checks the exterior condition of the vehicle using cameras when the vehicle is rented out. For example, the system detects scratches and dents on the body and issues a warning if an abnormality is detected. The rental management department also checks for abnormalities using software that diagnoses the vehicle's internal system when the vehicle is rented out. For example, the system analyzes engine error logs and battery status and issues a warning if an abnormality is detected. In this way, safety can be ensured by automatically checking the condition of the vehicle and issuing a warning if an abnormality is detected.

[0038] The rental management department can verify the identity of users using facial recognition technology. For example, the rental management department introduces a system that uses facial recognition technology to verify the identity of users when renting or returning a vehicle. For example, it uses a smartphone camera to perform facial recognition to verify identity. The rental management department also builds a system that uses facial recognition technology to strengthen security when renting or returning a vehicle. For example, it prevents the vehicle engine from starting unless facial recognition is passed. The rental management department also introduces a system that uses facial recognition technology to verify the identity of users, simplifying the rental and return procedures. For example, it allows the rental and return procedures to be completed simply by performing facial recognition. In this way, using facial recognition technology to verify the identity of users strengthens security and simplifies the procedures.

[0039] The rental management department can add an app function that visualizes the vehicle's condition, allowing users to easily check the vehicle's condition when renting or returning it. The rental management department can add an app function that visualizes the vehicle's condition, allowing users to easily check the vehicle's condition when renting or returning it. For example, the app could display the engine condition and tire pressure. The rental management department can also introduce a system that records the vehicle's exterior and interior condition with photos and videos and allows users to check them in the app. For example, it could display photos of scratches and dents on the vehicle. The rental management department can also build a system that monitors the vehicle's condition in real time and allows users to check it in the app. For example, it could display vehicle sensor information in the app. By visualizing the vehicle's condition, users can easily check the vehicle's condition and use it with peace of mind.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The rental management department can monitor the user's health condition and suggest breaks at appropriate times. For example, it can measure heart rate and stress levels with sensors and encourage breaks if signs of overwork are detected. The rental management department can also introduce a system that suggests optimal driving times based on the user's health data. For example, it can suggest appropriate rest points to avoid long driving times. The rental management department can also provide driving advice based on the user's health condition. For example, it can suggest relaxing music if stress levels are high. In this way, monitoring the user's health condition and suggesting breaks at appropriate times improves safety and comfort.

[0042] The parking management unit can make suggestions to minimize energy consumption when users park their cars or bicycles. For example, it can suggest the shortest route within the parking lot, reducing unnecessary travel. The parking management unit can also introduce a system that suggests energy-efficient parking spaces. For example, it can prioritize shaded spaces, reducing air conditioning use. The parking management unit can also monitor energy consumption in real time and provide eco-driving advice to users. For example, it can encourage users to avoid sudden acceleration and braking. This minimizes energy consumption and reduces environmental impact.

[0043] The rental management unit can provide customization functions according to the user's preferences. For example, it can automatically adjust the temperature and music settings inside the vehicle to suit the user's preferences. The rental management unit can also implement a system that suggests preferred settings based on the user's past usage history. For example, it can suggest a specific music genre or radio station. The rental management unit can also provide customized driving advice according to the user's preferences. For example, it can suggest switching between sport mode and eco mode. This improves user satisfaction by providing customization functions according to the user's preferences.

[0044] The parking management department will install solar panels in designated spots, allowing vehicle batteries to be charged while parking or bicycles are parked. For example, solar panels will be installed above the parking spaces to supply power. The parking management department will also install solar panels in bicycle parking areas and build a system to charge electric bicycle batteries while bicycles are parked. For example, solar panels will be installed in bicycle parking racks to supply power. The parking management department will also introduce a system that uses solar panels to supply power to the entire designated spot. For example, the solar panels will provide power for parking lot lighting and surveillance cameras. This will improve energy efficiency by charging vehicle batteries while parking or bicycles are parked.

[0045] The parking management unit can display information about designated spots using augmented reality (AR) technology, allowing users to intuitively find parking and bicycle parking spaces. For example, vacant parking spaces can be displayed using AR, allowing users to find them intuitively. The parking management unit will also build a system that uses AR technology to display the availability of bicycle parking spaces in real time. For example, when a smartphone camera is pointed at a bicycle parking space, vacant racks will be highlighted. The parking management unit will also introduce a system that uses AR technology to display detailed information about designated spots. For example, parking fees and usage times can be displayed using AR, allowing users to easily check them. In this way, by displaying information about designated spots using augmented reality (AR) technology, users will be able to intuitively find parking and bicycle parking spaces.

[0046] The rental management department can verify the identity of users using facial recognition technology. For example, a system can be introduced that uses facial recognition technology to verify the identity of users when renting or returning a vehicle. For example, a smartphone camera can be used to perform facial recognition to verify identity. The rental management department can also build a system that uses facial recognition technology to strengthen security when renting or returning a vehicle. For example, the vehicle engine can be prevented from starting unless facial recognition is passed. The rental management department can also introduce a system that uses facial recognition technology to verify the identity of users, simplifying the rental and return procedures. For example, the rental and return procedures can be completed simply by performing facial recognition. In this way, using facial recognition technology to verify the identity of users strengthens security and simplifies the procedures.

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

[0048] Step 1: The vehicle registration unit registers a vehicle. For example, a user can enter the time periods when they will not be using their car into the app, allowing other users to borrow their car during those times. The vehicle registration unit also manages vehicle information and displays available time periods and locations. Step 2: The parking management unit parks the vehicle registered by the vehicle registration unit in a designated spot. For example, the car or bicycle is parked in a parking or bicycle parking lot at a designated spot in a commercial facility, store, or station. The parking management unit also provides information about the designated spot so that the user can easily find the vehicle. Step 3: The rental management unit rents and returns the vehicle parked by the parking management unit. For example, after a user finishes shopping, they can use the app to rent a car from the parking lot and transport their belongings to their home. After use, they return the car to the designated location. The rental management unit also manages the rental and return procedures and sends notifications to the user. Step 4: The delivery coordination department coordinates the vehicles rented by the rental management department with delivery services. For example, by linking with online supermarket orders and other delivery services, customers can collect and take home their packages at any time they like, without having to wait long at home for the scheduled delivery time.

[0049] (Example 2) The short-term rental system according to an embodiment of the present invention is a system that allows users to register unused cars and bicycles using an app and rent them out to people who need them. This allows users to use vehicles efficiently and pick up and take home all the vehicles at a time of their choice, without having to wait long at home for the scheduled delivery time.

[0050] The short-term rental system according to the embodiment includes a vehicle registration unit, a parking management unit, a rental management unit, and a delivery coordination unit. The vehicle registration unit registers vehicles. For example, a user inputs the time periods when the user will not be using the vehicle into an app, allowing other users to rent the vehicle during those times. The vehicle registration unit also manages vehicle information and displays available time periods and locations. The parking management unit parks the vehicle registered by the vehicle registration unit in a designated spot. For example, the user parks the car or bicycle in a designated parking or bicycle parking lot at a commercial facility, store, or station. The parking management unit also provides information about the designated spot so that the user can easily find the vehicle. The rental management unit rents and returns the vehicle parked by the parking management unit. For example, after shopping, the user can use the app to rent a car from a parking lot and have their luggage delivered to their home. After use, the user returns the car to the designated location. The rental management unit also manages rental and return procedures and sends notifications to the user. The delivery cooperation unit cooperates with delivery services to cooperate with vehicles rented by the rental management unit. For example, by cooperating with online ordering at supermarkets and other delivery services, users can pick up and take home all their deliveries at any time they like, without having to wait long at home for the scheduled delivery time. As a result, the short-term rental system according to the embodiment allows users to use vehicles efficiently and pick up and take home all their deliveries at any time they like, without having to wait long at home for the scheduled delivery time.

[0051] The vehicle registration unit can analyze usage history and maintenance information using generation AI and propose optimal rental time slots. For example, the vehicle registration unit can use generation AI to analyze the vehicle's usage history and propose optimal rental time slots based on past usage patterns. For example, it can identify time slots during the daytime on weekdays when the vehicle is not being used and propose those times to rent out to other users. The vehicle registration unit can also analyze vehicle maintenance information using generation AI and predict when maintenance is required. For example, it can automatically detect when oil or tire changes are due and propose rental time slots that avoid those periods. The vehicle registration unit can also integrate the vehicle's usage history and maintenance information, and the generation AI can propose optimal rental time slots. For example, it can create an optimal vehicle usage schedule based on past usage frequency and maintenance history. This allows for efficient use by proposing optimal vehicle rental time slots.

[0052] The vehicle registration department can automatically check the vehicle's condition using sensors, cameras, and internal systems and issue a warning if an abnormality is detected. For example, the vehicle registration department introduces a system that automatically checks the vehicle's condition using sensors when registering a vehicle. For example, the system monitors the engine condition and tire pressure in real time and issues a warning if an abnormality is detected. The vehicle registration department also introduces a system that automatically checks the vehicle's exterior condition using cameras when registering a vehicle. For example, the system detects scratches and dents on the body and issues a warning if an abnormality is detected. The vehicle registration department also checks for abnormalities using software that diagnoses the vehicle's internal systems when registering a vehicle. For example, the system analyzes engine error logs and battery status and issues a warning if an abnormality is detected. In this way, safety can be ensured by automatically checking the vehicle's condition and issuing a warning if an abnormality is detected.

[0053] The vehicle registration unit can use the emotion estimation function to analyze the owner's emotions and provide advice to alleviate anxiety or concerns about renting a vehicle. For example, the vehicle registration unit uses the emotion estimation function to analyze the emotions of the vehicle owner in real time when registering the vehicle and provide advice if there are any anxiety or concerns. For example, if the owner is feeling anxious, it displays a message that provides reassurance. The vehicle registration unit also collects emotional data of the vehicle owner and provides advice to alleviate anxiety or concerns based on past data. For example, it presents success stories and positive feedback from other users. The vehicle registration unit also uses the emotion estimation function to design an interface to alleviate the stress that vehicle owners feel when registering a vehicle. For example, it adopts simple operation and an intuitive design to alleviate the owner's stress. This reduces the anxiety and concerns of vehicle owners, thereby promoting rental use.

[0054] The parking management unit can use generative AI to analyze usage status in real time and suggest optimal parking and bicycle parking locations. For example, the parking management unit uses generative AI to build a system that analyzes the usage status of designated spots in real time and suggests available parking and bicycle parking locations. For example, it analyzes sensor information from parking lots and identifies available spaces. The parking management unit also introduces a system that analyzes the usage status of designated spots using generative AI and predicts the optimal parking and bicycle parking locations based on past data. For example, it predicts available locations during specific time periods and suggests them to users. The parking management unit also uses generative AI to build a system that analyzes the usage status of designated spots in real time and suggests the optimal parking and bicycle parking locations according to user needs. For example, it prioritizes suggesting locations close to the user's destination. This improves user convenience by analyzing the usage status of designated spots in real time and suggesting the optimal parking and bicycle parking locations.

[0055] The parking management unit can automatically record the vehicle's location using GPS, QR codes, and beacons, allowing users to easily find their vehicles. For example, the parking management unit introduces a system that automatically records the vehicle's location using GPS when the vehicle is parked or parked. For example, a specific location within the parking lot can be recorded using GPS and the user can check it using an app. The parking management unit also introduces a system that records the vehicle's location using QR codes when the vehicle is parked or parked. For example, the QR code installed in the parking space can be scanned and the location information can be saved in the app. The parking management unit also introduces a system that automatically records the vehicle's location using beacons when the vehicle is parked or parked. For example, the system can link with beacons installed in the parking lot to identify the vehicle's location. This automatically records the vehicle's location, allowing users to easily find their vehicle and improving convenience.

[0056] The parking management unit can use the emotion estimation function to analyze the user's stress when parking and make suggestions to reduce stress. For example, the parking management unit could use the emotion estimation function to build a system that analyzes the user's stress when parking and makes suggestions to reduce stress. For example, it could suggest an easier parking method when parking is difficult. The parking management unit could also introduce a system that provides advice to reduce stress when parking and making suggestions based on the user's emotion data. For example, the app could display tips on how to choose a parking space and parking tips. The parking management unit could also use the emotion estimation function to analyze the user's stress when parking and design an interface to reduce stress. For example, it could visually display the availability of parking spaces. This would reduce the user's stress when parking and improving user satisfaction.

[0057] The parking management department can install solar power generation panels in designated spots to charge vehicle batteries while parking or bicycles are parked. For example, the parking management department installs solar power generation panels in designated spots and introduces a system that charges vehicle batteries while parking. For example, solar panels are installed above parking spaces to supply power. The parking management department also installs solar power generation panels in bicycle parking areas and builds a system that charges electric bicycle batteries while bicycles are parked. For example, solar panels are installed in bicycle parking racks to supply power. The parking management department also introduces a system that uses solar power generation panels to supply power to the entire designated spot. For example, the solar panels could provide power for parking lot lighting and surveillance cameras. This improves energy efficiency by charging vehicle batteries while parking or bicycles are parked.

[0058] The parking management unit can display information about designated spots using augmented reality (AR) technology, allowing users to intuitively find parking and bicycle parking spaces. For example, the parking management unit introduces a system that uses augmented reality (AR) technology to display information about designated spots through a smartphone camera. For example, vacant parking spaces can be displayed using AR, allowing users to intuitively find them. The parking management unit also builds a system that uses AR technology to display the availability of bicycle parking spaces in real time. For example, when a smartphone camera is pointed at a bicycle parking space, vacant racks are highlighted. The parking management unit also introduces a system that uses AR technology to display detailed information about designated spots. For example, parking fees and usage times can be displayed using AR, allowing users to easily check them. In this way, by displaying information about designated spots using augmented reality (AR) technology, users can intuitively find parking and bicycle parking spaces.

[0059] The parking management unit can use the emotion estimation function to provide guidance to reduce the anxiety users feel when parking or parking their bicycles. For example, the parking management unit uses the emotion estimation function to analyze the anxiety users feel when parking or parking their bicycles in real time and build a system that provides guidance to reduce anxiety. For example, for users who are not good at parking, the app displays parking tips and points to note. The parking management unit also introduces a system that provides customized guidance to reduce anxiety based on the user's emotion data. For example, it analyzes the user's past parking history and provides individual advice. The parking management unit also uses the emotion estimation function to design an interface that provides audio guidance to reduce the anxiety users feel when parking or parking their bicycles. For example, it plays audio guidance as the user approaches a parking space. This reduces the anxiety users feel when parking or parking their bicycles, thereby improving user satisfaction.

[0060] The rental management department can use generative AI to analyze a user's usage history and suggest optimal rental and return times. For example, the rental management department can use generative AI to analyze a user's past usage history and build a system that suggests optimal rental and return times. For example, it can suggest the most convenient time period for a user based on past usage patterns. The rental management department can also introduce a system that analyzes a user's past usage history with generative AI and predicts optimal rental and return times based on usage frequency and purpose. For example, it can take into account usage during commuting hours and weekends. The rental management department can also use generative AI to integrate a user's past usage history with data from other users and build a system that suggests optimal rental and return times. For example, it can suggest optimal time periods based on data from users in the same area. This improves user convenience by analyzing a user's past usage history and suggesting optimal rental and return times.

[0061] The rental management department can automatically check the condition of the vehicle using sensors, cameras, and internal systems and issue a warning if an abnormality is detected. For example, the rental management department introduces a system that automatically checks the condition of the vehicle using sensors when the vehicle is rented out. For example, the system monitors the engine condition and tire pressure in real time and issues a warning if an abnormality is detected. The rental management department also introduces a system that automatically checks the exterior condition of the vehicle using cameras when the vehicle is rented out. For example, the system detects scratches and dents on the body and issues a warning if an abnormality is detected. The rental management department also checks for abnormalities using software that diagnoses the vehicle's internal system when the vehicle is rented out. For example, the system analyzes engine error logs and battery status and issues a warning if an abnormality is detected. In this way, safety can be ensured by automatically checking the condition of the vehicle and issuing a warning if an abnormality is detected.

[0062] The lending management department can use the emotion estimation function to analyze users' emotions when borrowing and returning items and propose improvements to the service. For example, the lending management department uses the emotion estimation function to analyze users' emotions when borrowing and returning items in real time and build a system to propose improvements to the service. For example, if a user is dissatisfied, the department can identify the cause and propose improvements. The lending management department also introduces a system that identifies improvements to the lending and returning service based on user emotion data. For example, it can analyze user feedback and propose specific improvements. The lending management department also uses the emotion estimation function to analyze users' emotions when borrowing and returning items and design an interface to improve the quality of the service. For example, it can provide operating procedures that do not cause users stress. This allows the department to analyze users' emotions and propose improvements to the service, thereby improving the quality of the service.

[0063] The rental management department can verify the identity of users using facial recognition technology. For example, the rental management department introduces a system that uses facial recognition technology to verify the identity of users when renting or returning a vehicle. For example, it uses a smartphone camera to perform facial recognition to verify identity. The rental management department also builds a system that uses facial recognition technology to strengthen security when renting or returning a vehicle. For example, it prevents the vehicle engine from starting unless facial recognition is passed. The rental management department also introduces a system that uses facial recognition technology to verify the identity of users, simplifying the rental and return procedures. For example, it allows the rental and return procedures to be completed simply by performing facial recognition. In this way, using facial recognition technology to verify the identity of users strengthens security and simplifies the procedures.

[0064] The rental management department can add an app function that visualizes the vehicle's condition, allowing users to easily check the vehicle's condition when renting or returning it. The rental management department can add an app function that visualizes the vehicle's condition, allowing users to easily check the vehicle's condition when renting or returning it. For example, the app could display the engine condition and tire pressure. The rental management department can also introduce a system that records the vehicle's exterior and interior condition with photos and videos and allows users to check them in the app. For example, it could display photos of scratches and dents on the vehicle. The rental management department can also build a system that monitors the vehicle's condition in real time and allows users to check it in the app. For example, it could display vehicle sensor information in the app. By visualizing the vehicle's condition, users can easily check the vehicle's condition and use it with peace of mind.

[0065] The lending management unit can use the emotion estimation function to provide support functions to reduce the anxiety users feel when borrowing or returning items. For example, the lending management unit uses the emotion estimation function to analyze the anxiety users feel when borrowing or returning items in real time and builds a system that provides support functions to reduce anxiety. For example, if a user feels anxious, a message that gives a sense of security is displayed. The lending management unit also introduces a system that provides customized support functions to reduce anxiety based on the user's emotional data. For example, it analyzes the user's past usage history and provides individual advice. The lending management unit also uses the emotion estimation function to design an interface that provides audio guidance to reduce the anxiety users feel when borrowing or returning items. For example, it plays audio guidance during the lending and returning procedures. This reduces the anxiety users feel when borrowing or returning items, thereby improving user satisfaction.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The rental management department can monitor the user's health condition and suggest breaks at appropriate times. For example, it can measure heart rate and stress levels with sensors and encourage breaks if signs of overwork are detected. The rental management department can also introduce a system that suggests optimal driving times based on the user's health data. For example, it can suggest appropriate rest points to avoid long driving times. The rental management department can also provide driving advice based on the user's health condition. For example, it can suggest relaxing music if stress levels are high. In this way, monitoring the user's health condition and suggesting breaks at appropriate times improves safety and comfort.

[0068] The parking management unit can make suggestions to minimize energy consumption when users park their cars or bicycles. For example, it can suggest the shortest route within the parking lot, reducing unnecessary travel. The parking management unit can also introduce a system that suggests energy-efficient parking spaces. For example, it can prioritize shaded spaces, reducing air conditioning use. The parking management unit can also monitor energy consumption in real time and provide eco-driving advice to users. For example, it can encourage users to avoid sudden acceleration and braking. This minimizes energy consumption and reduces environmental impact.

[0069] The rental management unit can provide customization functions according to the user's preferences. For example, it can automatically adjust the temperature and music settings inside the vehicle to suit the user's preferences. The rental management unit can also implement a system that suggests preferred settings based on the user's past usage history. For example, it can suggest a specific music genre or radio station. The rental management unit can also provide customized driving advice according to the user's preferences. For example, it can suggest switching between sport mode and eco mode. This improves user satisfaction by providing customization functions according to the user's preferences.

[0070] The parking management unit can use the emotion estimation function to analyze the stress a user experiences when parking and make suggestions to reduce stress. For example, it can suggest an easier parking method when parking is difficult. The parking management unit will also introduce a system that provides advice to reduce stress when parking and bicycle parking based on the user's emotion data. For example, the app could display tips on how to choose a parking space and parking tips. The parking management unit will also use the emotion estimation function to analyze the stress a user experiences when parking and bicycle parking and design an interface to reduce stress. For example, it could visually display the availability of parking spaces. This will reduce stress when parking and bicycle parking, thereby improving user satisfaction.

[0071] The lending management department can use the emotion estimation function to analyze users' emotions when borrowing or returning items and propose improvements to the service. For example, if a user is dissatisfied, the department can identify the cause and propose improvements. The lending management department also introduces a system that identifies improvements to the lending and returning service based on user emotion data. For example, it can analyze user feedback and propose specific improvements. The lending management department also uses the emotion estimation function to analyze users' emotions when borrowing or returning items and design an interface to improve the quality of the service. For example, it can provide operating procedures that do not cause stress to the user. This allows the department to analyze users' emotions and propose improvements to the service, thereby improving the quality of the service.

[0072] The parking management department will install solar panels in designated spots, allowing vehicle batteries to be charged while parking or bicycles are parked. For example, solar panels will be installed above the parking spaces to supply power. The parking management department will also install solar panels in bicycle parking areas and build a system to charge electric bicycle batteries while bicycles are parked. For example, solar panels will be installed in bicycle parking racks to supply power. The parking management department will also introduce a system that uses solar panels to supply power to the entire designated spot. For example, the solar panels will provide power for parking lot lighting and surveillance cameras. This will improve energy efficiency by charging vehicle batteries while parking or bicycles are parked.

[0073] The lending management department can use the emotion estimation function to provide support functions to reduce the anxiety users feel when borrowing or returning items. For example, if a user feels anxious, a message that provides reassurance can be displayed. The lending management department also introduces a system that provides customized support functions to reduce anxiety based on the user's emotional data. For example, it can analyze the user's past usage history and provide individualized advice. The lending management department also uses the emotion estimation function to design an interface that provides audio guidance to reduce the anxiety users feel when borrowing or returning items. For example, it can play audio guidance during the lending and returning procedures. This reduces the anxiety users feel when borrowing or returning items, thereby improving user satisfaction.

[0074] The parking management unit can display information about designated spots using augmented reality (AR) technology, allowing users to intuitively find parking and bicycle parking spaces. For example, vacant parking spaces can be displayed using AR, allowing users to find them intuitively. The parking management unit will also build a system that uses AR technology to display the availability of bicycle parking spaces in real time. For example, when a smartphone camera is pointed at a bicycle parking space, vacant racks will be highlighted. The parking management unit will also introduce a system that uses AR technology to display detailed information about designated spots. For example, parking fees and usage times can be displayed using AR, allowing users to easily check them. In this way, by displaying information about designated spots using augmented reality (AR) technology, users will be able to intuitively find parking and bicycle parking spaces.

[0075] The lending management department can use the emotion estimation function to analyze users' emotions when borrowing or returning items and propose improvements to the service. For example, if a user is dissatisfied, the department can identify the cause and propose improvements. The lending management department also introduces a system that identifies improvements to the lending and returning service based on user emotion data. For example, it can analyze user feedback and propose specific improvements. The lending management department also uses the emotion estimation function to analyze users' emotions when borrowing or returning items and design an interface to improve the quality of the service. For example, it can provide operating procedures that do not cause stress to the user. This allows the department to analyze users' emotions and propose improvements to the service, thereby improving the quality of the service.

[0076] The rental management department can verify the identity of users using facial recognition technology. For example, a system can be introduced that uses facial recognition technology to verify the identity of users when renting or returning a vehicle. For example, a smartphone camera can be used to perform facial recognition to verify identity. The rental management department can also build a system that uses facial recognition technology to strengthen security when renting or returning a vehicle. For example, the vehicle engine can be prevented from starting unless facial recognition is passed. The rental management department can also introduce a system that uses facial recognition technology to verify the identity of users, simplifying the rental and return procedures. For example, the rental and return procedures can be completed simply by performing facial recognition. In this way, using facial recognition technology to verify the identity of users strengthens security and simplifies the procedures.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The vehicle registration unit registers a vehicle. For example, a user can enter the time periods when they will not be using their car into the app, allowing other users to borrow their car during those times. The vehicle registration unit also manages vehicle information and displays available time periods and locations. Step 2: The parking management unit parks the vehicle registered by the vehicle registration unit in a designated spot. For example, the car or bicycle is parked in a parking or bicycle parking lot at a designated spot in a commercial facility, store, or station. The parking management unit also provides information about the designated spot so that the user can easily find the vehicle. Step 3: The rental management unit rents and returns the vehicle parked by the parking management unit. For example, after a user finishes shopping, they can use the app to rent a car from the parking lot and transport their belongings to their home. After use, they return the car to the designated location. The rental management unit also manages the rental and return procedures and sends notifications to the user. Step 4: The delivery coordination department coordinates the vehicles rented by the rental management department with delivery services. For example, by linking with online supermarket orders and other delivery services, customers can collect and take home their packages at any time they like, without having to wait long at home for the scheduled delivery time.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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."

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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]

[0146] 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 vehicle registration unit that registers vehicles; a parking management unit that parks the vehicle registered by the vehicle registration unit in a designated spot; the rental management unit that rents out and returns vehicles parked by the parking management unit; a delivery linking unit that links the vehicle rented by the rental management unit with a delivery service. A system characterized by:

2. The parking management unit Using generative AI to analyze usage in real time and suggest optimal parking and bicycle parking locations 2. The system of claim 1.

3. The lending management unit Generative AI is used to analyze the user's usage history and suggest the optimal rental and return times.

2. The system of claim 1.

4. The vehicle registration unit Analyze owner sentiment and provide advice to alleviate rental fears and concerns 2. The system of claim 1.

5. The parking management unit Automatically record the vehicle's location using GPS, QR codes, and beacons, allowing users to easily find the vehicle 2. The system of claim 1.

6. The lending management unit Using sensors, cameras, and internal systems, the system automatically checks the vehicle's condition and issues an alert if something is wrong.

2. The system of claim 1.

7. The parking management unit Solar panels will be installed in the designated spots to charge the vehicle's battery while the vehicle is parked.

2. The system of claim 1.

8. The lending management unit Analyze users' feelings when borrowing and returning items and suggest improvements to the service 2. The system of claim 1.

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