System

Abstract

A system is provided.SOLUTION: The system includes a video analysis means for analyzing vacancy of a parking lot, a position information acquisition means for acquiring position information and an action pattern of a user, a parking lot information provision means for proposing optimum parking lot information and a charge to the user on the basis of information acquired from the analysis means and the position information acquisition means, a notification means for notifying the user of information of the parking lot information provision means, and a navigation means for performing navigation to a selected parking lot on the basis of information of the notification means.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] Amid a shortage of parking spaces in urban areas and calls for improved parking efficiency, drivers find it difficult to find parking spaces and are concerned about fees. Parking lot owners lack the means to properly monitor and guide parking lot usage, resulting in missed opportunities. Furthermore, existing systems require the installation of expensive IoT sensors, resulting in high implementation costs. To address these issues, an effective, low-cost system is needed that analyzes parking lot availability in real time and provides drivers with appropriate parking information and fees. [Means for solving the problem]

[0005] The present invention provides a system that includes a video analysis means for analyzing parking availability, a location information acquisition means for acquiring user location information and behavioral patterns, a parking information provision means for proposing optimal parking information and fees to users based on information acquired from the analysis means and the location information acquisition means, a notification means for notifying the user of information from the parking information provision means to their device, and a navigation means for navigating to the selected parking lot based on information from the notification means. This allows drivers to obtain real-time information on parking availability and fees, enabling efficient parking use. Furthermore, by using security camera footage, the system eliminates the need for additional IoT sensors, enabling a low-cost system to be implemented. Furthermore, parking lot owners can monitor parking usage and revenue in real time through a cloud-based management dashboard, allowing them to set appropriate fees and promote their parking lots.

[0006] "Video analysis means" refers to technology and devices that analyze video data obtained from security cameras and other video acquisition devices and identify the availability of parking spaces in real time.

[0007] "Location information acquisition means" refers to technology and devices that acquire a user's current location using GPS or other location information services.

[0008] "Parking lot information providing means" refers to technology and devices that calculate appropriate parking lot information and fee information based on data obtained from the video analysis means and location information acquisition means, and provide it to users.

[0009] The "notification means" refers to technology and devices that notify the user of the parking lot information and fee information generated by the parking lot information providing means to a terminal such as a smartphone or tablet.

[0010] The "navigation means" refers to a technology and device that guides the user to the selected parking lot based on the information obtained through the notification means.

[0011] "Parking space" means an area in which a vehicle can be parked, and refers to a portion of a parking lot.

[0012] "Real-time" means that all processing, from data acquisition to providing results, is done almost instantly.

[0013] "Behavioral patterns" refer to trends and predictions of behavior based on a user's past parking history and schedule information.

[0014] The "cloud-based management dashboard" is a web interface accessible via the Internet that allows parking lot owners to manage parking lot usage and revenue information. [Brief explanation of the drawings]

[0015] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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, a 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), and an APU (Accelerated Processing Unit).

[0019] 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.

[0020] 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.

[0021] 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), Bluetooth (registered trademark), etc.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0027] 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.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The present invention provides a system that includes an image analysis means for analyzing the availability of parking spaces, a location information acquisition means for acquiring user location information and behavioral patterns, a parking information provision means for proposing optimal parking information and fees to the user based on information obtained from the analysis means and location information acquisition means, a notification means for notifying the user of information from the parking information provision means to their terminal, and a navigation means for navigating to the selected parking space based on information from the notification means.

[0037] Program processing details

[0038] Server-side processing

[0039] 1. Video analysis methods

[0040] The server receives video data in real time from security cameras installed in the parking lot.

[0041] The video analytics module analyzes the video data to determine the availability of parking spaces, thereby identifying vacant and occupied spaces and storing this information in a database.

[0042] 2. Location information acquisition means

[0043] The server uses the smartphone's GPS function to obtain the user's current location information, which is then sent to the server in real time.

[0044] 3. Behavioral pattern analysis methods

[0045] The server uses a behavioral pattern analysis module to analyze the user's behavioral patterns based on their past parking history and schedule information, which then predicts their next parking needs and stores future parking patterns in a database.

[0046] 4. Means of providing parking information

[0047] The server calculates the most suitable parking information and fees for the user based on the information obtained from the video analysis means and location information acquisition means.

[0048] The fee optimization module dynamically calculates the optimal fee based on the parking lot's location and usage.

[0049] 5. Means of notification

[0050] The server notifies the user of the optimal parking information and fees via a smartphone app or tablet.

[0051] Notifications are provided in real time, and if the user is driving, information is provided through a voice prompt function.

[0052] 6. Navigation Methods

[0053] The server provides a navigation function based on the notified parking lot information.

[0054] The route to the parking lot selected by the user is calculated and navigation is performed in real time.

[0055] Specific usage scenarios

[0056] When a driver uses a parking lot

[0057] 1. User (Driver)

[0058] Users launch the smartphone app and search for parking spaces. The app automatically obtains their current location and requests information about the nearest parking spaces.

[0059] 2. Server

[0060] The server receives video data from security cameras, and the video analysis module identifies available parking spaces.

[0061] The server analyzes the user's location information and behavioral patterns to select the most suitable parking space.

[0062] The server transmits the selected parking lot information and the dynamically calculated fee to the user's terminal via the notification means.

[0063] 3. Device (smartphone app)

[0064] The user's smartphone app receives notifications from the server and displays information about available parking spaces and fees on the screen. If the user is driving, the voice guidance function is activated.

[0065] Once the user selects a suggested parking spot, the app sends the information to the server and begins navigation.

[0066] 4. Navigation Methods

[0067] The server sends navigation information to the selected parking lot to the device and guides the user through the app.

[0068] When the parking lot owner manages the system

[0069] 1. User (parking lot owner)

[0070] Parking lot owners have access to a cloud-based management dashboard to view parking lot usage and revenue data.

[0071] 2. Server

[0072] The server collects data in real time and displays it on a dashboard, displaying the information in a format that is easy for the owner to manage.

[0073] 3. User (parking lot owner)

[0074] Owners set pricing and promotions through the dashboard, and the settings information is stored on the server.

[0075] The server runs a fee optimization module based on the new fee settings and dynamically adjusts the parking fee.

[0076] This allows users to easily find parking spaces and use them efficiently, while allowing parking lot owners to maximize their parking lot revenue.

[0077] The processing flow will be explained below.

[0078] Step 1:

[0079] The server receives video data in real time from security cameras installed in the parking lot.

[0080] Step 2:

[0081] The server's video analysis module analyzes the availability of parking spaces based on the received video data, identifies vacant and occupied spaces, and stores the results in a database.

[0082] Step 3:

[0083] The user (driver) launches the smartphone app and obtains their current location information. The device (smartphone app) obtains the location information using its GPS function and sends it to the server.

[0084] Step 4:

[0085] The server uses a behavioral pattern analysis module to analyze the user's behavioral patterns based on the user's current location information, past parking history, and schedule information, and predicts the user's next parking needs.

[0086] Step 5:

[0087] The parking lot information providing means of the server calculates the most suitable parking lot information and fees for the user based on the information obtained from the video analysis means and the location information acquisition means.

[0088] Step 6:

[0089] The fee optimization module of the server dynamically calculates the optimal fee based on the location and usage of the parking lot, and reflects the result in the parking lot information providing means.

[0090] Step 7:

[0091] The server's notification means notifies the user's device (smartphone app) in real time of the optimal parking information and fees.

[0092] Step 8:

[0093] The user (driver) checks the parking lot information and fee information provided through the smartphone app and selects a parking lot. The device (smartphone app) then sends the selected parking lot information to the server.

[0094] Step 9:

[0095] The server generates navigation information to the selected parking lot and sends it to the device (smartphone app).

[0096] Step 10:

[0097] The device (smartphone app) guides the user to the parking lot based on the navigation information received from the server. The user (driver) follows the navigation on the smartphone app to head to the parking lot.

[0098] Step 11:

[0099] Parking lot owners have access to a cloud-based management dashboard that provides real-time access to parking lot usage and revenue data.

[0100] Step 12:

[0101] The server receives the owner's pricing and promotion information provided through the management dashboard, and recalculates the price optimization module based on it, resulting in new pricing information being provided to the user.

[0102] Example 1

[0103] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0104] Real-time video analysis and analysis of user behavior patterns are necessary to efficiently grasp parking availability and propose appropriate parking information and fees to users. However, if these functions are insufficient, it becomes difficult to provide appropriate parking information, which causes trouble and stress for users. Furthermore, providing only visual information while driving can be dangerous, so safe information provision via audio guidance is also required.

[0105] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0106] In this invention, the server includes: a video analysis means for analyzing the availability of parking spaces; a location information acquisition means for acquiring user location information and behavioral patterns; a parking information provision means for proposing optimal parking information and fees to the user based on information acquired from the analysis means and the location information acquisition means; a notification means for notifying the user of information from the parking information provision means to the user's terminal; a navigation means for navigating to the selected parking space based on information from the notification means; a server including a video analysis algorithm for determining the availability of parking spaces using a video analysis module; a server including a behavior pattern analysis module for analyzing behavioral patterns based on the user's past parking history and schedule information; a terminal equipped with a voice guidance function for providing parking information via voice guidance while the vehicle is being driven; and a server equipped with a navigation function for providing navigation information in real time. This makes it possible to efficiently analyze available parking spaces in real time and safely and efficiently provide optimal parking information and navigation to the user.

[0107] The "video analysis means for analyzing parking space availability" is a device or module that analyzes video data acquired from cameras installed in the parking lot and identifies vacant and occupied parking spaces in real time.

[0108] "Location information acquisition means for acquiring user location information and behavioral patterns" refers to a device or system that uses a smartphone or other GPS device to collect a user's current location information and past behavioral history.

[0109] The "parking lot information providing means" is a device or system that calculates and presents optimal parking lot information and fees to users based on information obtained from the video analysis means and location information acquisition means.

[0110] The "notification means" is a device or system that notifies the user of calculated parking information and fees in real time via their smartphone app or tablet.

[0111] The "navigation means" is a device or system that provides real-time guidance on routes to a selected parking lot based on the information provided by the notification means.

[0112] "Video analysis algorithms" are image processing technologies and machine learning models used to analyze acquired video data and determine the availability of parking spaces.

[0113] The "behavior pattern analysis module" is a device or software module for predicting a user's parking behavior based on the user's past parking history and schedule information.

[0114] A "voice guidance function" is a device or system that conveys information through voice to safely provide parking information and navigation information while driving a vehicle.

[0115] The "navigation function that provides real-time navigation information" is a function that calculates the route to the parking lot selected by the user in real time and displays and provides voice guidance on the user's smartphone app.

[0116] The present invention is a system that includes an image analysis means for analyzing parking availability, a location information acquisition means for acquiring user location information and behavioral patterns, a parking information provision means for proposing optimal parking information and fees to users based on that information, a notification means for notifying the user of parking information to their terminal, and a navigation means for navigating to the selected parking lot based on that information.

[0117] System configuration

[0118] 1. Video analysis methods

[0119] The server uses video reception technologies such as RTSP (Real-Time Streaming Protocol) to receive video data in real time from security cameras installed in the parking lot.

[0120] The server uses a video analysis module (e.g., OpenCV or TENSORFLOW®) to analyze the availability of parking spaces based on the acquired video data, thereby identifying vacant and occupied spaces and storing the results in a database.

[0121] 2. Location information acquisition means

[0122] The device (smartphone) uses its GPS function to obtain the user's current location information. When the user launches the application, the current location information is periodically sent to the server.

[0123] 3. Behavioral pattern analysis methods

[0124] The server collects users' past parking history and schedule information, and uses a behavioral pattern analysis module to predict their next parking needs. The analysis results are stored in a database.

[0125] 4. Means of providing parking information

[0126] The server integrates data obtained from the video analysis and location information acquisition means to calculate the optimal parking information and fees for the user. The fee optimization module dynamically provides the optimal fee, taking into account real-time usage and the location of the parking lot.

[0127] 5. Means of notification

[0128] The server notifies users of the best parking information and fees via a smartphone app or tablet. For example, it sends real-time information about available parking spaces via push notifications or SMS. It also provides information safely to drivers through a voice guidance function.

[0129] 6. Navigation Methods

[0130] The server calculates the route to the parking lot selected by the user and sends the navigation information to the device, which uses a dedicated app to display the route on a map and provide real-time voice guidance.

[0131] Specific usage scenarios

[0132] When a driver uses a parking lot

[0133] The user (driver) starts the smartphone app, which acquires the current location information and searches for the nearest parking lot information. The app then sends a request to the server.

[0134] The server receives video data from security cameras, and the video analysis module identifies available parking spaces. It also analyzes the user's location and behavioral patterns to calculate the optimal parking information and fees.

[0135] The server notifies the user of the selected parking lot information and fees via a smartphone app. If the user is driving, the information is provided via the device's voice guidance function.

[0136] When the user (driver) selects a suggested parking lot, the server calculates navigation information based on that information and sends it to the terminal.

[0137] When the parking lot owner manages the system

[0138] Users (parking lot owners) access a cloud-based management dashboard to view parking lot usage and revenue data.

[0139] The server then feeds the collected data into a dashboard in real time, displaying revenue data and usage in an easy-to-manage format.

[0140] Prompt Sentence Examples

[0141] "I want to use the feature in my app that notifies me of parking availability and best parking options. How can I do this?"

[0142] The system allows users to find parking spaces efficiently and parking lot owners to maximize parking lot utilization and revenue.

[0143] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0144] Step 1:

[0145] Video data reception

[0146] The server receives real-time video data from security cameras installed in the parking lot, which then transmit the video data to the server via the network using RTSP (Real-Time Streaming Protocol).

[0147] Input: Video stream from a security camera.

[0148] Output: Raw video data stored on the server.

[0149] Step 2:

[0150] Video Analysis

[0151] The server passes the received video data to a video analysis module, which uses image processing algorithms (such as OpenCV or TensorFlow) to identify vacant parking spaces in real time. The analysis results are used to generate data indicating the availability of each parking space.

[0152] Input: Raw video data.

[0153] Output: Information about available parking spaces (stored in a database).

[0154] Step 3:

[0155] Location information acquisition

[0156] When a user launches an application, the device (smartphone) uses its GPS function to obtain current location information. The application then periodically sends this location information to a server.

[0157] Input: GPS data from your smartphone.

[0158] Output: Real-time location information transmitted to the server.

[0159] Step 4:

[0160] Behavioral pattern analysis

[0161] The server collects users' past parking history and schedule information, analyzes this data using a behavioral pattern analysis module, and stores the analysis results in a database as information to predict future parking needs.

[0162] Input: User's past parking history, schedule information.

[0163] Output: Predicted user parking needs (stored in database).

[0164] Step 5:

[0165] Parking information provided

[0166] The server integrates the data obtained from the video analysis means and the location information acquisition means, and calculates the optimal parking information and fees using the fee optimization module. The fee is dynamically determined taking into account real-time parking lot usage and location conditions.

[0167] Input: available parking space information, user location information, predicted parking needs.

[0168] Output: Optimal parking information and prices (passed to notification means).

[0169] Step 6:

[0170] Information Notification

[0171] The server then sends calculated parking information and fees to the user's smartphone app or tablet, with real-time updates and voice guidance enabled if the user is driving.

[0172] Input: Best parking information and prices.

[0173] Output: The notification message sent to the user's device.

[0174] Step 7:

[0175] Navigation provided

[0176] The server calculates navigation information to the parking lot selected by the user and sends it to the device via a dedicated app. The smartphone displays the route on a map and provides real-time voice guidance.

[0177] Input: Location information of selected parking lot, user's current location information.

[0178] Output: Navigation information (displayed on the user's device and voice guidance).

[0179] In this way, the system can efficiently analyze parking availability in real time and provide users with optimal parking information and navigation.

[0180] (Application example 1)

[0181] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0182] As autonomous vehicles become more widespread, there is a demand for systems that automatically find parking spaces and park at appropriate fees, without the driver having to search for a parking space themselves. However, current systems lack the means to analyze parking availability in real time, and do not optimize parking fees or suggest parking spaces based on behavioral patterns. Furthermore, there is no integrated system available for autonomous vehicles to navigate to a selected parking space. To address these challenges, a system combining advanced video analysis technology, location information acquisition technology, and behavioral pattern analysis technology is needed.

[0183] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0184] In this invention, the server includes an image analysis means for analyzing the availability of parking spaces, a location information acquisition means for acquiring user location information and behavioral patterns, a parking information provision means for proposing optimal parking information and fees to the user based on information acquired from the analysis means and the location information acquisition means, a notification means for notifying the user's terminal of information from the parking information provision means, and a navigation means for navigating the autonomous vehicle to the selected parking space based on information from the notification means. This allows the user to obtain optimal parking information and fees in real time, and enables the autonomous vehicle to smoothly navigate to the selected parking space.

[0185] The "video analysis means" is a means for analyzing video data acquired from a monitoring device installed in a parking lot and determining the availability and occupancy status of the parking lot in real time.

[0186] "Location information acquisition means" refers to a means of collecting location data in real time using technologies such as GPS in order to acquire user location information and behavioral patterns.

[0187] The "parking lot information providing means" is a means for proposing optimal parking lot information and fees to users based on information obtained from the video analysis means and location information acquisition means.

[0188] The "notification means" is a means for notifying the user's terminal of the information obtained by the parking lot information providing means, and may be email, a smartphone app, voice guidance, or the like.

[0189] The "navigation means" is a means for calculating a route to a parking lot selected by a user based on information obtained from the notification means, and guiding an autonomous vehicle to that parking lot.

[0190] A "surveillance camera" is a device that captures the situation in a parking lot as video data in real time.

[0191] An "autonomous vehicle" refers to a vehicle that can drive and park automatically.

[0192] "Behavioral patterns" are predictive data on a user's next parking needs and behavior, analyzed based on their past parking history, schedule information, etc.

[0193] "Optimal parking information" is information on the most convenient parking lot, selected based on the user's location and behavioral patterns.

[0194] "Fee" refers to the cost for using a parking lot, and is dynamically set based on factors such as parking time and the location of the parking lot.

[0195] The present invention relates to a parking assistance system for autonomous vehicles, and in particular to a system that analyzes parking lot availability in real time and provides users with optimal parking lot information and fees. This system includes an image analysis means for analyzing parking lot availability, a location information acquisition means for acquiring user location information and behavior patterns, a parking lot information provision means for proposing optimal parking lot information and fees based on information obtained from the analysis means and the location information acquisition means, a notification means for notifying the user's terminal of information from the parking lot information provision means, and a navigation means for navigating the autonomous vehicle to a selected parking lot based on information from the notification means.

[0196] The specific configuration of the system will be explained below. The system's video analysis means acquires video data using surveillance cameras installed in parking lots and analyzes the parking lot availability in real time. This identifies vacant and occupied parking spaces and stores this information in a database.

[0197] The location information acquisition means acquires location information using the GPS function of the user's smartphone or vehicle, allowing the user's current location to be grasped in real time. In addition, the behavior pattern analysis module analyzes the user's behavior patterns based on their past parking history and schedule information, and predicts their next parking needs.

[0198] The parking lot information providing means proposes optimal parking lot information and fees to the user based on information obtained from the video analysis means and location information acquisition means. Fees are dynamically set based on the location and usage status of the parking lot. The proposed parking lot information and fees are notified to the user's device in real time via the notification means.

[0199] The navigation means navigates the autonomous vehicle to the selected parking lot based on the notified parking lot information. This navigation is performed using a map service such as Google (registered trademark) Maps API.

[0200] As a concrete example, consider a self-driving vehicle arriving in a city center. It can automatically find the best parking spot, calculate the cost, and provide route guidance. The process goes like this:

[0201] 1. When an autonomous vehicle arrives in a city center, the system analyzes available parking spaces in real time based on video data obtained from surveillance cameras.

[0202] 2. At the same time, the vehicle's GPS function is used to obtain current location information.

[0203] 3. Based on the user's past parking history and schedule information, behavioral patterns are analyzed and their next parking needs are predicted.

[0204] 4. Calculate the optimal parking information and fees and notify the user's device.

[0205] 5. After receiving the notification, the user selects the suggested parking lot through the app, and the system uses that information to calculate and navigate the route to the parking lot.

[0206] An example of a prompt is, "When you arrive in the city center, automatically analyze available parking spaces and begin navigation to the best available parking space." This allows users to find parking spaces without stress and park their self-driving vehicles smoothly.

[0207] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0208] Step 1:

[0209] The server acquires video data from surveillance cameras installed in the parking lot, analyzes the video data using a video analysis means, and identifies vacant and occupied parking spaces. The analysis results are stored in a database.

[0210] Input: Video data from a surveillance camera

[0211] Output: Information on vacant and occupied parking spaces

[0212] Specific operation: After receiving real-time video data captured by the surveillance camera, the system uses video analysis technology such as OpenCV to determine the status of the parking space. The analysis results are stored in a database and retained for use in the next step.

[0213] Step 2:

[0214] The server obtains the user's current location via the GPS function of the user's smartphone or vehicle, and this location information is sent to the server in real time.

[0215] Input: GPS data from the user's smartphone or vehicle

[0216] Output: User's current location information

[0217] How it works: Smartphones and autonomous vehicles collect GPS data and send it to a server, which receives this location information and stores it in a database in real time.

[0218] Step 3:

[0219] The server analyzes the user's behavioral patterns based on their past parking history and schedule information, and uses the results of this analysis to predict their next parking needs.

[0220] Input: User's past parking history, schedule information

[0221] Output: Predicted data for next parking needs

[0222] How it works: The server references historical databases and uses machine learning algorithms to analyze user behavior patterns, which then predicts upcoming parking needs and generates data on future parking needs.

[0223] Step 4:

[0224] The server integrates the information obtained from the video analysis and location information acquisition means to provide users with optimal parking information and fees. Fees are dynamically set based on the location and usage of the parking lot.

[0225] Input: Information on available parking spaces, current location, behavioral pattern analysis results

[0226] Output: Optimal parking information and prices

[0227] How it works: The server integrates the collected data and uses an algorithm to select the most suitable parking spot. The fee optimization module then dynamically sets fees based on parking spot usage and location.

[0228] Step 5:

[0229] The server notifies the user's device of the calculated optimal parking information and fees using a notification method. This notification is done in real time, and if the user is driving, the information is provided through a voice guidance function.

[0230] Input: Best parking information, fee information

[0231] Output: Notification to the user's device

[0232] How it works: The server generates a notification message and sends it to the user's smartphone or tablet. If the user is driving, the information is provided as voice guidance.

[0233] Step 6:

[0234] Based on the parking information notified to the user, the route to the selected parking lot is calculated and the autonomous vehicle is navigated. Navigation is provided in real time using the navigation means.

[0235] Input: Selected parking lot information, current location information

[0236] Output: Navigation information to the parking lot

[0237] How it works: The server uses map services such as Google Maps API to calculate the optimal route from the current location to the selected parking lot, then sends the navigation information to the autonomous vehicle to guide it to the parking lot.

[0238] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0239] The present invention is a system that includes an image analysis means for analyzing the availability of parking spaces, a location information acquisition means for acquiring user location information and behavioral patterns, a parking information provision means for proposing optimal parking information and fees to the user based on information acquired from the analysis means and the location information acquisition means, a notification means for notifying the user of information from the parking information provision means to the user's terminal, a navigation means for navigating to the selected parking space based on information from the notification means, and an emotion engine for recognizing the user's emotions.

[0240] Program processing details

[0241] Server-side processing

[0242] 1. Video analysis methods

[0243] The server receives video data in real time from security cameras installed in the parking lot.

[0244] The video analytics module analyzes the video data to determine the availability of parking spaces, thereby identifying vacant and occupied spaces and storing this information in a database.

[0245] 2. Location information acquisition means

[0246] The server uses the smartphone's GPS function to obtain the user's current location information, which is then sent to the server in real time.

[0247] 3. Behavioral pattern analysis methods

[0248] The server uses a behavioral pattern analysis module to analyze the user's behavioral patterns based on their past parking history and schedule information, which then predicts their next parking needs and stores future parking patterns in a database.

[0249] 4. Emotion Engine

[0250] The server uses an emotion engine to analyze the user's facial expressions and tone of voice to recognize the user's emotions in real time. It uses facial recognition and voice analysis technology to identify the user's emotions while operating the app.

[0251] 5. Means of providing parking information

[0252] The server calculates the most suitable parking information and fees for the user based on information obtained from the video analysis means, location information acquisition means, behavioral pattern analysis means and emotion engine.

[0253] The fee optimization module dynamically calculates the optimal fee by taking into account the location and usage of the parking lot, as well as the user's emotions identified by the emotion engine.

[0254] 6. Means of notification

[0255] The server notifies the user of the optimal parking information and fees via a smartphone app or tablet.

[0256] Notifications are provided in real time, and if the user is driving, information is provided through a voice prompt function.

[0257] 7. Navigation Methods

[0258] The server provides a navigation function based on the notified parking lot information.

[0259] The route to the parking lot selected by the user is calculated and navigation is performed in real time.

[0260] Specific usage scenarios

[0261] When a driver uses a parking lot

[0262] 1. User (Driver)

[0263] Users launch the smartphone app and search for parking spaces. The app automatically obtains their current location and requests information about the nearest parking spaces.

[0264] 2. Server

[0265] The server receives video data from security cameras, and the video analysis module identifies available parking spaces.

[0266] The server analyzes the user's location information and behavioral patterns, as well as the user's emotions identified by an emotion engine, to select the most suitable parking space.

[0267] The server transmits the selected parking lot information and the dynamically calculated fee to the user's terminal via the notification means.

[0268] 3. Device (smartphone app)

[0269] The user's smartphone app receives notifications from the server and displays information about available parking spaces and fees on the screen. If the user is driving, the voice guidance function is activated.

[0270] Once the user selects a suggested parking spot, the app sends the information to the server and begins navigation.

[0271] 4. Navigation Methods

[0272] The server sends navigation information to the selected parking lot to the device and guides the user through the app.

[0273] When the parking lot owner manages the system

[0274] 1. User (parking lot owner)

[0275] Parking lot owners have access to a cloud-based management dashboard to view parking lot usage and revenue data.

[0276] 2. Server

[0277] The server collects data in real time and displays it on a dashboard, displaying the information in a format that is easy for the owner to manage.

[0278] 3. User (parking lot owner)

[0279] Owners set pricing and promotions through the dashboard, and the settings information is stored on the server.

[0280] The server runs a fee optimization module based on the new fee settings and dynamically adjusts the parking fee.

[0281] This allows users to efficiently find the best parking space based on their emotions, and parking lot owners to maximize their parking lot revenue.

[0282] The processing flow will be explained below.

[0283] Step 1:

[0284] The server receives video data in real time from security cameras installed in the parking lot.

[0285] Step 2:

[0286] The server's video analysis module analyzes the availability of parking spaces based on the received video data, identifies vacant and occupied spaces, and stores the results in a database.

[0287] Step 3:

[0288] The user (driver) launches the smartphone app and obtains their current location information. The device (smartphone app) obtains the location information using its GPS function and sends it to the server.

[0289] Step 4:

[0290] The server uses a behavioral pattern analysis module to analyze the user's behavioral patterns based on the user's current location information, past parking history, and schedule information, and predicts the user's next parking needs.

[0291] Step 5:

[0292] The server's emotion engine receives the user's facial recognition data and voice data from the smartphone app and analyzes it to recognize the user's emotions in real time, for example, determining whether the user is tired, angry, or relaxed.

[0293] Step 6:

[0294] The server's parking lot information providing means calculates the optimal parking lot information and fees for the user based on information obtained from the video analysis means, location information acquisition means, behavior pattern analysis means, and emotion engine. Taking into account the user's emotional state, it can suggest, for example, a nearby and safe parking lot to a tired user, and a slightly further but cheaper parking lot to a relaxed user.

[0295] Step 7:

[0296] The fee optimization module of the server dynamically calculates the optimal fee based on the location and usage of the parking lot, and reflects the result in the parking lot information providing means.

[0297] Step 8:

[0298] The server's notification method notifies the user's device (smartphone app) in real time of the optimal parking information and fees. If the user is driving, the notification is provided through a voice guidance function.

[0299] Step 9:

[0300] The user (driver) checks the parking lot information and fee information provided through the smartphone app and selects a parking lot. The device (smartphone app) then sends the selected parking lot information to the server.

[0301] Step 10:

[0302] The server generates navigation information to the selected parking lot and sends it to the device (smartphone app).

[0303] Step 11:

[0304] The device (smartphone app) guides the user to the parking lot based on the navigation information received from the server. The user (driver) follows the navigation on the smartphone app to head to the parking lot.

[0305] Step 12:

[0306] Parking lot owners have access to a cloud-based management dashboard that provides real-time access to parking lot usage and revenue data.

[0307] Step 13:

[0308] The server receives the owner's pricing and promotion information provided through the management dashboard, and recalculates the price optimization module based on it, resulting in new pricing information being provided to the user.

[0309] Example 2

[0310] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0311] While existing parking information systems provide information on parking availability and fees, they have limitations in suggesting optimal parking spaces that take into account the user's behavioral patterns and emotions. Furthermore, they lack support for selecting parking spaces that reflects the user's emotional state when operating the app and reduces stress. For these reasons, there was a need for the development of a new system that would improve user convenience while maximizing parking lot owners' profits.

[0312] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0313] In this invention, the server includes a video analysis means for analyzing the availability of parking spaces, a location information acquisition means for acquiring the user's location information and behavioral patterns, and an emotion engine for recognizing the user's emotions, making it possible to propose optimal parking information and fees taking into account the user's current location, behavioral patterns, and emotional state.

[0314] The "video analysis means" is a means for analyzing video data obtained from security cameras and the like, and identifying vacant and occupied spaces within a parking lot.

[0315] "Location information acquisition means" refers to a means of acquiring the user's current location information using a smartphone or GPS device and transmitting that information to a server.

[0316] The "behavioral pattern analysis means" is a means that uses data analysis and modeling technology to predict a user's next parking needs based on their past parking history and schedule information.

[0317] The "emotion engine" is a means of analyzing the user's facial expressions and tone of voice to recognize their emotional state in real time. It uses facial recognition and voice analysis technologies.

[0318] The "parking lot information providing means" is a means that integrates information obtained from the video analysis means, location information acquisition means, behavioral pattern analysis means, and emotion engine, and proposes optimal parking lot information and fees to users.

[0319] The "notification means" is a means by which the server notifies the user's device, such as a smartphone app or tablet, of the optimal parking information and fees obtained from the parking information providing means.

[0320] The "navigation means" is a means for calculating a route to a parking lot selected by a user based on the notified parking lot information and providing real-time navigation.

[0321] The present invention is a system that analyzes parking availability and provides users with optimal parking information and fees. This system includes a video analysis unit, a location information acquisition unit, a behavioral pattern analysis unit, an emotion engine, a parking information provision unit, a notification unit, and a navigation unit.

[0322] Server-side implementation

[0323] 1. Video analysis methods

[0324] The server receives video data from security cameras and uses a video analysis module to identify vacant parking spaces. This analysis is performed using image processing libraries such as OpenCV. The analysis results are stored in a database.

[0325] 2. Location information acquisition means

[0326] The server uses the smartphone's GPS function to obtain the user's current location information in real time. The device encrypts the location information using the HTTPS protocol and sends it to the server.

[0327] 3. Behavioral pattern analysis methods

[0328] The server retrieves the user's past parking history and schedule information from the database, analyzes it using a behavioral pattern analysis module, and predicts the next parking need using a machine learning model (e.g., Scikit-Learn's clustering algorithm).

[0329] 4. Emotion Engine

[0330] The server uses an emotion engine to analyze the user's facial expressions and tone of voice to recognize their emotions, using, for example, Microsoft® Azure® facial recognition API and voice analysis technology.

[0331] 5. Means of providing parking information

[0332] The server calculates the optimal parking information and fees using a fee optimization module (using, for example, NumPy) based on the information obtained from the video analysis means, location information acquisition means, behavioral pattern analysis means, and emotion engine, and sends the information to the notification means.

[0333] 6. Means of notification

[0334] The server notifies the user's device, such as a smartphone app or tablet, of the calculated parking information and fees in real time. The notification also has a voice guidance function, so it can be used by users who are driving. The voice notification uses the Google Text-to-Speech API.

[0335] 7. Navigation Methods

[0336] The server calculates the route to the selected parking lot using a map API (e.g., Google Maps API) and generates navigation information. The information is sent to the device, which displays the route on a map in real time and provides voice guidance.

[0337] Specific examples of usage scenarios

[0338] When a driver uses a parking lot

[0339] 1. User (Driver)

[0340] Launch the smartphone app and request "Find the nearest available parking space." The app will automatically obtain your current location and send it to the server.

[0341] 2. Server

[0342] The system analyzes security camera footage to identify available parking spaces, then selects the most suitable parking spot based on the acquired location information and the user's behavioral patterns and emotional state, and notifies the user of the calculated information and fees.

[0343] 3. Device (smartphone app)

[0344] Users will receive notifications and be shown information about available parking spaces and fees. If they are driving, voice guidance will be activated, telling them, "The nearest parking lot is 300 meters ahead on the right." Once the user selects a parking spot, navigation will begin.

[0345] 4. Navigation Methods

[0346] The system calculates the route to the selected parking lot and sends navigation information to the device, which then displays the route on a map and provides voice guidance such as "Turn right at the next intersection."

[0347] When the parking lot owner manages the system

[0348] 1. User (parking lot owner)

[0349] Access your cloud-based management dashboard and click "Check usage."

[0350] 2. Server

[0351] The data collected in real time is reflected in the dashboard, displaying usage and revenue data.

[0352] 3. User (parking lot owner)

[0353] Use the dashboard to set up pricing and promotions, then save the new settings to the server.

[0354] 4. Server

[0355] The new pricing is reflected in the pricing optimization module, dynamically adjusting parking fees.

[0356] Prompt Sentence Examples

[0357] "Please tell me the nearest available parking lot from my current location."

[0358] "Please set up special parking rates for this weekend's event."

[0359] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0360] Processing Steps

[0361] Step 1: Obtaining and sending location information

[0362] 1. User: Launches the smartphone app and requests to "find the nearest available parking space."

[0363] 2. Device: Uses the smartphone's GPS function to obtain current location information.

[0364] 3. Device: The acquired location information is encrypted using the HTTPS protocol and sent to the server.

[0365] Input: User requests, smartphone GPS data.

[0366] Output: Your location information is encrypted and sent to the server.

[0367] Step 2: Identifying parking availability through video analysis

[0368] 1. Server: Receives video data in real time from security cameras installed in the parking lot.

[0369] 2. Server: Uses a video analytics module (e.g., OpenCV) to determine the availability of parking spaces, specifically identifying the presence or absence of a vehicle and identifying vacant and occupied spaces.

[0370] 3. Server: Stores the analysis results in a database.

[0371] Input: Real-time video data from security cameras.

[0372] Output: Availability information stored in a database.

[0373] Step 3: Analyze behavioral patterns

[0374] 1. Server: Obtains the user's past parking history and schedule information from the database.

[0375] 2. Server: Analyzes user behavior patterns using a behavioral pattern analysis module (e.g., Scikit-Learn clustering algorithm).

[0376] 3. Server: Save the results of predicting the next parking need in a database.

[0377] Input: Historical parking history and schedule information retrieved from the database.

[0378] Output: Behavioral patterns and parking needs prediction results stored in a database.

[0379] Step 4: Emotion Recognition

[0380] 1. Device: Using the smartphone's camera and microphone, the user's facial expressions and tone of voice are captured in real time.

[0381] 2. Terminal: Sends the acquired data to the server.

[0382] 3. Server: Uses an emotion engine (e.g., Microsoft Azure facial recognition API) to recognize the user's emotions from facial expressions and tone of voice.

[0383] Input: Facial expression data and voice data obtained from the smartphone camera and microphone.

[0384] Output: User emotion data recognized by the server.

[0385] Step 5: Calculate the best parking information

[0386] 1. Server: Integrates information obtained from video analysis, location information acquisition, behavioral pattern analysis, and emotion engine.

[0387] 2. Server: Calculates optimal parking information and fees using a fee optimization module (e.g., NumPy).

[0388] 3. Server: Sends the calculated parking information and fees to the notification means.

[0389] Input: Availability information from video analysis, location information, behavioral pattern analysis results, emotion recognition results.

[0390] Output: Optimal parking information and pricing information.

[0391] Step 6: Parking information notification

[0392] 1. Server: Calculated parking information and fees are sent in real time to user devices such as smartphone apps and tablets.

[0393] 2. Device: Receives notifications and displays information on the screen. If you are driving, voice guidance is enabled.

[0394] Input: Best parking information and prices from the server.

[0395] Output: Notifications to user terminals, screen displays and voice guidance.

[0396] Step 7: Providing navigation

[0397] 1. Server: Calculate the route to the selected parking lot using a map API (e.g., Google Maps API).

[0398] 2. Server: Sends navigation information to the device.

[0399] 3. Terminal: Receives navigation information sent from the server and displays the route on a map in real time. Voice guidance is also provided simultaneously.

[0400] Input: Best parking information, map data.

[0401] Output: Navigation information, map display and voice guidance.

[0402] (Application example 2)

[0403] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0404] Autonomous vehicles are required to quickly and accurately grasp the availability of parking spaces, and to select and guide the optimal parking space by taking into account the driver's location, past behavioral patterns, and emotional state. However, current technology does not have a system that comprehensively considers these factors, which makes parking space selection inconvenient and does not sufficiently reduce stress. Furthermore, after a parking space is selected, navigation must also provide appropriate guidance in real time, but this has not been fully realized.

[0405] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0406] In this invention, the server includes an image analysis means for analyzing the availability of parking spaces, a location information acquisition means for acquiring user location information and behavioral patterns, an emotion analysis means for analyzing user emotions in real time, a parking information provision means for proposing optimal parking information and fees to the user based on information acquired from the analysis means, the location information acquisition means, and the emotion analysis means, a notification means for notifying the user of information from the parking information provision means to the user's terminal, and a navigation means for navigating to the selected parking space. This enables quick and accurate understanding of parking space availability, selection of a parking space taking into account the driver's emotional state, and appropriate navigation in real time.

[0407] The "video analysis means for analyzing parking space availability" is a device that analyzes video data acquired from cameras installed in the parking lot and identifies vacant and occupied parking spaces.

[0408] "Location information acquisition means for acquiring user location information and behavioral patterns" refers to a device that acquires the user's current location information using technology such as GPS, and analyzes behavioral patterns based on past parking history and schedule information.

[0409] The "emotion analysis means" is a device that analyzes the user's facial expressions and voice to analyze their emotional state in real time.

[0410] The "parking lot information providing means" is a device that calculates the most suitable parking lot information and fees for the user based on the information obtained from the video analysis means, location information acquisition means, and emotion analysis means.

[0411] The "notification means" is a device that notifies the user's terminal of the information obtained by the parking lot information providing means in real time.

[0412] The "navigation means" is a device that calculates the optimal route to the selected parking lot and provides guidance to the user in real time.

[0413] The present invention uses hardware and software to realize the following system configuration and processing flow: This system provides optimal parking lot selection and navigation specifically for autonomous vehicles.

[0414] The server drives various modules to achieve the following main functions:

[0415] 1. Video analysis methods

[0416] The server receives video data in real time from cameras installed inside the autonomous vehicles. The video analytics module uses image processing libraries such as OpenCV to analyze the video data and determine the availability of parking spaces. This identifies vacant and occupied parking spaces and stores this information in a database.

[0417] 2. Location information acquisition means

[0418] The server uses the GPS module to obtain the vehicle's current location information, which then transmits the user's location information to the server in real time.

[0419] 3. Behavioral pattern analysis methods

[0420] The server uses a behavioral pattern analysis module to analyze the user's behavioral patterns based on their past parking history and schedule information, which then predicts their next parking needs and stores future parking patterns in a database.

[0421] 4. Emotion analysis method

[0422] The server uses a camera and microphone to analyze the user's facial expressions and tone of voice. It uses an emotion analysis engine (such as IBM Watson (registered trademark) Tone Analyzer) to recognize the user's emotions in real time. It uses facial expression analysis technology and voice analysis technology to quantify the user's emotional state.

[0423] 5. Means of providing parking information

[0424] The server calculates the optimal parking information and fees for the user based on information obtained from video analysis, location information acquisition, behavioral pattern analysis, and emotion analysis.The optimal fee is calculated dynamically, taking into account the location and usage conditions of the parking lot, as well as the user's emotions identified by the emotion engine.

[0425] 6. Means of notification

[0426] The server displays the best parking information and prices on the vehicle's display, providing real-time notifications and, if the driver is driving, providing information via the voice guidance system, allowing the driver to get the information they need without having to look away.

[0427] 7. Navigation Methods

[0428] The server automatically calculates the route to the selected parking lot and provides real-time navigation, linking with the vehicle's navigation system to provide optimal route guidance.

[0429] This will enable quick and accurate understanding of parking availability, parking lot selection that takes into account the user's emotional state, and appropriate navigation in real time.

[0430] Example: A vehicle camera detects available parking spaces in real time, and the user's current location is identified by GPS. Based on past parking patterns, frequently used parking areas are prioritized. Furthermore, if the user is feeling stressed, emotion analysis can be used to suggest a suitable parking space for the user.

[0431] Example prompts for generative AI models

[0432] Design a system for autonomous vehicles that analyzes parking availability in real time, suggests the best parking spot and its price based on the user's location, behavioral patterns, and emotions, and then navigates to the parking spot.

[0433] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0434] Step 1:

[0435] The server receives video data in real time from cameras installed inside the vehicles. The video data acquired from the cameras is used as input and analyzed using image processing libraries such as OpenCV. After analyzing the video, vacant and occupied parking spaces are identified and the information is stored in a database. This allows the parking lot availability to be updated in real time.

[0436] Input: Video data from the camera

[0437] Data processing: Video analysis using OpenCV

[0438] Output: Information about identified vacant parking spaces

[0439] Step 2:

[0440] The server obtains the current location information from the GPS module installed in the vehicle. It receives the current location information as input and sends it to the server. The location information is stored in a database and used for subsequent processing.

[0441] Input: Location information from GPS module

[0442] Data processing: Acquiring and storing location information

[0443] Output: Current location information stored in a database

[0444] Step 3:

[0445] The server analyzes behavioral patterns based on past parking history and schedule information. It obtains past history and schedule information from the database and uses this as input. It uses an analysis module such as Python to predict next parking needs and saves the analysis results in the database.

[0446] Input: Past parking history and schedule information

[0447] Data processing: Behavioral pattern analysis

[0448] Output: Analysis results stored in a database

[0449] Step 4:

[0450] The server uses a camera and microphone to collect the user's facial expressions and tone of voice. This data is used as input to analyze emotions in real time using an emotion analysis engine (such as IBM Watson Tone Analyzer). The analysis results are stored in a database.

[0451] Input: facial and voice data from camera and microphone

[0452] Data processing: Analysis using a sentiment analysis engine

[0453] Output: Sentiment analysis results stored in a database

[0454] Step 5:

[0455] The server integrates information obtained from the video analysis means, location information acquisition means, behavioral pattern analysis means, and emotion analysis means. Using this data as input, it calculates the optimal parking information and fees for the user. Based on the parking information and user information in the database, the fee optimization module dynamically calculates the optimal fee.

[0456] Input: Results of video analysis, location information, behavioral pattern analysis, and emotion analysis

[0457] Data processing: Integration of various information and calculation of optimal pricing

[0458] Output: Optimal parking information and prices

[0459] Step 6:

[0460] The server then notifies the vehicle's display and voice guidance system of the calculated optimal parking information and fees, allowing users to receive the necessary information visually and audibly.

[0461] Input: Best parking information and prices

[0462] Data processing: Creating notification messages

[0463] Output: Notification to vehicle display and voice guidance system

[0464] Step 7:

[0465] The server automatically calculates the optimal route to the selected parking lot and provides real-time navigation, linking with the vehicle's navigation system to provide directions to help users smoothly reach their destination.

[0466] Input: Selected parking information

[0467] Data processing: Calculation of optimal route using route calculation algorithm

[0468] Output: Instructions to the vehicle's navigation system

[0469] 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.

[0470] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0471] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0472] [Second embodiment]

[0473] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0474] 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.

[0475] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0476] 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.

[0477] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0478] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0479] 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.

[0480] 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.

[0481] 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 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.

[0482] 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.

[0483] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0484] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0485] The present invention provides a system that includes an image analysis means for analyzing the availability of parking spaces, a location information acquisition means for acquiring user location information and behavioral patterns, a parking information provision means for proposing optimal parking information and fees to the user based on information obtained from the analysis means and location information acquisition means, a notification means for notifying the user of information from the parking information provision means to their terminal, and a navigation means for navigating to the selected parking space based on information from the notification means.

[0486] Program processing details

[0487] Server-side processing

[0488] 1. Video analysis methods

[0489] The server receives video data in real time from security cameras installed in the parking lot.

[0490] The video analytics module analyzes the video data to determine the availability of parking spaces, thereby identifying vacant and occupied spaces and storing this information in a database.

[0491] 2. Location information acquisition means

[0492] The server uses the smartphone's GPS function to obtain the user's current location information, which is then sent to the server in real time.

[0493] 3. Behavioral pattern analysis methods

[0494] The server uses a behavioral pattern analysis module to analyze the user's behavioral patterns based on their past parking history and schedule information, which then predicts their next parking needs and stores future parking patterns in a database.

[0495] 4. Means of providing parking information

[0496] The server calculates the most suitable parking information and fees for the user based on the information obtained from the video analysis means and location information acquisition means.

[0497] The fee optimization module dynamically calculates the optimal fee based on the parking lot's location and usage.

[0498] 5. Means of notification

[0499] The server notifies the user of the optimal parking information and fees via a smartphone app or tablet.

[0500] Notifications are provided in real time, and if the user is driving, information is provided through a voice prompt function.

[0501] 6. Navigation Methods

[0502] The server provides a navigation function based on the notified parking lot information.

[0503] The route to the parking lot selected by the user is calculated and navigation is performed in real time.

[0504] Specific usage scenarios

[0505] When a driver uses a parking lot

[0506] 1. User (Driver)

[0507] Users launch the smartphone app and search for parking spaces. The app automatically obtains their current location and requests information about the nearest parking spaces.

[0508] 2. Server

[0509] The server receives video data from security cameras, and the video analysis module identifies available parking spaces.

[0510] The server analyzes the user's location information and behavioral patterns to select the most suitable parking space.

[0511] The server transmits the selected parking lot information and the dynamically calculated fee to the user's terminal via the notification means.

[0512] 3. Device (smartphone app)

[0513] The user's smartphone app receives notifications from the server and displays information about available parking spaces and fees on the screen. If the user is driving, the voice guidance function is activated.

[0514] Once the user selects a suggested parking spot, the app sends the information to the server and begins navigation.

[0515] 4. Navigation Methods

[0516] The server sends navigation information to the selected parking lot to the device and guides the user through the app.

[0517] When the parking lot owner manages the system

[0518] 1. User (parking lot owner)

[0519] Parking lot owners have access to a cloud-based management dashboard to view parking lot usage and revenue data.

[0520] 2. Server

[0521] The server collects data in real time and displays it on a dashboard, displaying the information in a format that is easy for the owner to manage.

[0522] 3. User (parking lot owner)

[0523] Owners set pricing and promotions through the dashboard, and the settings information is stored on the server.

[0524] The server runs a fee optimization module based on the new fee settings and dynamically adjusts the parking fee.

[0525] This allows users to easily find parking spaces and use them efficiently, while allowing parking lot owners to maximize their parking lot revenue.

[0526] The processing flow will be explained below.

[0527] Step 1:

[0528] The server receives video data in real time from security cameras installed in the parking lot.

[0529] Step 2:

[0530] The server's video analysis module analyzes the availability of parking spaces based on the received video data, identifies vacant and occupied spaces, and stores the results in a database.

[0531] Step 3:

[0532] The user (driver) launches the smartphone app and obtains their current location information. The device (smartphone app) obtains the location information using its GPS function and sends it to the server.

[0533] Step 4:

[0534] The server uses a behavioral pattern analysis module to analyze the user's behavioral patterns based on the user's current location information, past parking history, and schedule information, and predicts the user's next parking needs.

[0535] Step 5:

[0536] The parking lot information providing means of the server calculates the most suitable parking lot information and fees for the user based on the information obtained from the video analysis means and the location information acquisition means.

[0537] Step 6:

[0538] The fee optimization module of the server dynamically calculates the optimal fee based on the location and usage of the parking lot, and reflects the result in the parking lot information providing means.

[0539] Step 7:

[0540] The server's notification means notifies the user's device (smartphone app) in real time of the optimal parking information and fees.

[0541] Step 8:

[0542] The user (driver) checks the parking lot information and fee information provided through the smartphone app and selects a parking lot. The device (smartphone app) then sends the selected parking lot information to the server.

[0543] Step 9:

[0544] The server generates navigation information to the selected parking lot and sends it to the device (smartphone app).

[0545] Step 10:

[0546] The device (smartphone app) guides the user to the parking lot based on the navigation information received from the server. The user (driver) follows the navigation on the smartphone app to head to the parking lot.

[0547] Step 11:

[0548] Parking lot owners have access to a cloud-based management dashboard that provides real-time access to parking lot usage and revenue data.

[0549] Step 12:

[0550] The server receives the owner's pricing and promotion information provided through the management dashboard, and recalculates the price optimization module based on it, resulting in new pricing information being provided to the user.

[0551] Example 1

[0552] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0553] Real-time video analysis and analysis of user behavior patterns are necessary to efficiently grasp parking availability and propose appropriate parking information and fees to users. However, if these functions are insufficient, it becomes difficult to provide appropriate parking information, which causes trouble and stress for users. Furthermore, providing only visual information while driving can be dangerous, so safe information provision via audio guidance is also required.

[0554] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0555] In this invention, the server includes: a video analysis means for analyzing the availability of parking spaces; a location information acquisition means for acquiring user location information and behavioral patterns; a parking information provision means for proposing optimal parking information and fees to the user based on information acquired from the analysis means and the location information acquisition means; a notification means for notifying the user of information from the parking information provision means to the user's terminal; a navigation means for navigating to the selected parking space based on information from the notification means; a server including a video analysis algorithm for determining the availability of parking spaces using a video analysis module; a server including a behavior pattern analysis module for analyzing behavioral patterns based on the user's past parking history and schedule information; a terminal equipped with a voice guidance function for providing parking information via voice guidance while the vehicle is being driven; and a server equipped with a navigation function for providing navigation information in real time. This makes it possible to efficiently analyze available parking spaces in real time and safely and efficiently provide optimal parking information and navigation to the user.

[0556] The "video analysis means for analyzing parking space availability" is a device or module that analyzes video data acquired from cameras installed in the parking lot and identifies vacant and occupied parking spaces in real time.

[0557] "Location information acquisition means for acquiring user location information and behavioral patterns" refers to a device or system that uses a smartphone or other GPS device to collect a user's current location information and past behavioral history.

[0558] The "parking lot information providing means" is a device or system that calculates and presents optimal parking lot information and fees to users based on information obtained from the video analysis means and location information acquisition means.

[0559] The "notification means" is a device or system that notifies the user of calculated parking information and fees in real time via their smartphone app or tablet.

[0560] The "navigation means" is a device or system that provides real-time guidance on routes to a selected parking lot based on the information provided by the notification means.

[0561] "Video analysis algorithms" are image processing technologies and machine learning models used to analyze acquired video data and determine the availability of parking spaces.

[0562] The "behavior pattern analysis module" is a device or software module for predicting a user's parking behavior based on the user's past parking history and schedule information.

[0563] A "voice guidance function" is a device or system that conveys information through voice to safely provide parking information and navigation information while driving a vehicle.

[0564] The "navigation function that provides real-time navigation information" is a function that calculates the route to the parking lot selected by the user in real time and displays and provides voice guidance on the user's smartphone app.

[0565] The present invention is a system that includes an image analysis means for analyzing parking availability, a location information acquisition means for acquiring user location information and behavioral patterns, a parking information provision means for proposing optimal parking information and fees to users based on that information, a notification means for notifying the user of parking information to their terminal, and a navigation means for navigating to the selected parking lot based on that information.

[0566] System configuration

[0567] 1. Video analysis methods

[0568] The server uses video reception technologies such as RTSP (Real-Time Streaming Protocol) to receive video data in real time from security cameras installed in the parking lot.

[0569] The server uses a video analysis module (e.g., OpenCV or TensorFlow) to analyze the availability of parking spaces based on the captured video data, thereby identifying vacant and occupied spaces and storing the results in a database.

[0570] 2. Location information acquisition means

[0571] The device (smartphone) uses its GPS function to obtain the user's current location information. When the user launches the application, the current location information is periodically sent to the server.

[0572] 3. Behavioral pattern analysis methods

[0573] The server collects users' past parking history and schedule information, and uses a behavioral pattern analysis module to predict their next parking needs. The analysis results are stored in a database.

[0574] 4. Means of providing parking information

[0575] The server integrates data obtained from the video analysis and location information acquisition means to calculate the optimal parking information and fees for the user. The fee optimization module dynamically provides the optimal fee, taking into account real-time usage and the location of the parking lot.

[0576] 5. Means of notification

[0577] The server notifies users of the best parking information and fees via a smartphone app or tablet. For example, it sends real-time information about available parking spaces via push notifications or SMS. It also provides information safely to drivers through a voice guidance function.

[0578] 6. Navigation Methods

[0579] The server calculates the route to the parking lot selected by the user and sends the navigation information to the device, which uses a dedicated app to display the route on a map and provide real-time voice guidance.

[0580] Specific usage scenarios

[0581] When a driver uses a parking lot

[0582] The user (driver) starts the smartphone app, which acquires the current location information and searches for the nearest parking lot information. The app then sends a request to the server.

[0583] The server receives video data from security cameras, and the video analysis module identifies available parking spaces. It also analyzes the user's location and behavioral patterns to calculate the optimal parking information and fees.

[0584] The server notifies the user of the selected parking lot information and fees via a smartphone app. If the user is driving, the information is provided via the device's voice guidance function.

[0585] When the user (driver) selects a suggested parking lot, the server calculates navigation information based on that information and sends it to the terminal.

[0586] When the parking lot owner manages the system

[0587] Users (parking lot owners) access a cloud-based management dashboard to view parking lot usage and revenue data.

[0588] The server then feeds the collected data into a dashboard in real time, displaying revenue data and usage in an easy-to-manage format.

[0589] Prompt Sentence Examples

[0590] "I want to use the feature in my app that notifies me of parking availability and best parking options. How can I do this?"

[0591] The system allows users to find parking spaces efficiently and parking lot owners to maximize parking lot utilization and revenue.

[0592] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0593] Step 1:

[0594] Video data reception

[0595] The server receives real-time video data from security cameras installed in the parking lot, which then transmit the video data to the server via the network using RTSP (Real-Time Streaming Protocol).

[0596] Input: Video stream from a security camera.

[0597] Output: Raw video data stored on the server.

[0598] Step 2:

[0599] Video Analysis

[0600] The server passes the received video data to a video analysis module, which uses image processing algorithms (such as OpenCV or TensorFlow) to identify vacant parking spaces in real time. The analysis results are used to generate data indicating the availability of each parking space.

[0601] Input: Raw video data.

[0602] Output: Information about available parking spaces (stored in a database).

[0603] Step 3:

[0604] Location information acquisition

[0605] When a user launches an application, the device (smartphone) uses its GPS function to obtain current location information. The application then periodically sends this location information to a server.

[0606] Input: GPS data from your smartphone.

[0607] Output: Real-time location information transmitted to the server.

[0608] Step 4:

[0609] Behavioral pattern analysis

[0610] The server collects users' past parking history and schedule information, analyzes this data using a behavioral pattern analysis module, and stores the analysis results in a database as information to predict future parking needs.

[0611] Input: User's past parking history, schedule information.

[0612] Output: Predicted user parking needs (stored in database).

[0613] Step 5:

[0614] Parking information provided

[0615] The server integrates the data obtained from the video analysis means and the location information acquisition means, and calculates the optimal parking information and fees using the fee optimization module. The fee is dynamically determined taking into account real-time parking lot usage and location conditions.

[0616] Input: available parking space information, user location information, predicted parking needs.

[0617] Output: Optimal parking information and prices (passed to notification means).

[0618] Step 6:

[0619] Information Notification

[0620] The server then sends calculated parking information and fees to the user's smartphone app or tablet, with real-time updates and voice guidance enabled if the user is driving.

[0621] Input: Best parking information and prices.

[0622] Output: The notification message sent to the user's device.

[0623] Step 7:

[0624] Navigation provided

[0625] The server calculates navigation information to the parking lot selected by the user and sends it to the device via a dedicated app. The smartphone displays the route on a map and provides real-time voice guidance.

[0626] Input: Location information of selected parking lot, user's current location information.

[0627] Output: Navigation information (displayed on the user's device and voice guidance).

[0628] In this way, the system can efficiently analyze parking availability in real time and provide users with optimal parking information and navigation.

[0629] (Application example 1)

[0630] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0631] As autonomous vehicles become more widespread, there is a demand for systems that automatically find parking spaces and park at appropriate fees, without the driver having to search for a parking space themselves. However, current systems lack the means to analyze parking availability in real time, and do not optimize parking fees or suggest parking spaces based on behavioral patterns. Furthermore, there is no integrated system available for autonomous vehicles to navigate to a selected parking space. To address these challenges, a system combining advanced video analysis technology, location information acquisition technology, and behavioral pattern analysis technology is needed.

[0632] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0633] In this invention, the server includes an image analysis means for analyzing the availability of parking spaces, a location information acquisition means for acquiring user location information and behavioral patterns, a parking information provision means for proposing optimal parking information and fees to the user based on information acquired from the analysis means and the location information acquisition means, a notification means for notifying the user's terminal of information from the parking information provision means, and a navigation means for navigating the autonomous vehicle to the selected parking space based on information from the notification means. This allows the user to obtain optimal parking information and fees in real time, and enables the autonomous vehicle to smoothly navigate to the selected parking space.

[0634] The "video analysis means" is a means for analyzing video data acquired from a monitoring device installed in a parking lot and determining the availability and occupancy status of the parking lot in real time.

[0635] "Location information acquisition means" refers to a means of collecting location data in real time using technologies such as GPS in order to acquire user location information and behavioral patterns.

[0636] The "parking lot information providing means" is a means for proposing optimal parking lot information and fees to users based on information obtained from the video analysis means and location information acquisition means.

[0637] The "notification means" is a means for notifying the user's terminal of the information obtained by the parking lot information providing means, and may be email, a smartphone app, voice guidance, or the like.

[0638] The "navigation means" is a means for calculating a route to a parking lot selected by a user based on information obtained from the notification means, and guiding an autonomous vehicle to that parking lot.

[0639] A "surveillance camera" is a device that captures the situation in a parking lot as video data in real time.

[0640] An "autonomous vehicle" refers to a vehicle that can drive and park automatically.

[0641] "Behavioral patterns" are predictive data on a user's next parking needs and behavior, analyzed based on their past parking history, schedule information, etc.

[0642] "Optimal parking information" is information on the most convenient parking lot, selected based on the user's location and behavioral patterns.

[0643] "Fee" refers to the cost for using a parking lot, and is dynamically set based on factors such as parking time and the location of the parking lot.

[0644] The present invention relates to a parking assistance system for autonomous vehicles, and in particular to a system that analyzes parking lot availability in real time and provides users with optimal parking lot information and fees. This system includes an image analysis means for analyzing parking lot availability, a location information acquisition means for acquiring user location information and behavior patterns, a parking lot information provision means for proposing optimal parking lot information and fees based on information obtained from the analysis means and the location information acquisition means, a notification means for notifying the user's terminal of information from the parking lot information provision means, and a navigation means for navigating the autonomous vehicle to a selected parking lot based on information from the notification means.

[0645] The specific configuration of the system will be explained below. The system's video analysis means acquires video data using surveillance cameras installed in parking lots and analyzes the parking lot availability in real time. This identifies vacant and occupied parking spaces and stores this information in a database.

[0646] The location information acquisition means acquires location information using the GPS function of the user's smartphone or vehicle, allowing the user's current location to be grasped in real time. In addition, the behavior pattern analysis module analyzes the user's behavior patterns based on their past parking history and schedule information, and predicts their next parking needs.

[0647] The parking lot information providing means proposes optimal parking lot information and fees to the user based on information obtained from the video analysis means and location information acquisition means. Fees are dynamically set based on the location and usage status of the parking lot. The proposed parking lot information and fees are notified to the user's device in real time via the notification means.

[0648] The navigation means navigates the autonomous vehicle to the selected parking lot based on the notified parking lot information. This navigation is performed using a map service such as Google Maps API.

[0649] As a concrete example, consider a self-driving vehicle arriving in a city center. It can automatically find the best parking spot, calculate the cost, and provide route guidance. The process goes like this:

[0650] 1. When an autonomous vehicle arrives in a city center, the system analyzes available parking spaces in real time based on video data obtained from surveillance cameras.

[0651] 2. At the same time, the vehicle's GPS function is used to obtain current location information.

[0652] 3. Based on the user's past parking history and schedule information, behavioral patterns are analyzed and their next parking needs are predicted.

[0653] 4. Calculate the optimal parking information and fees and notify the user's device.

[0654] 5. After receiving the notification, the user selects the suggested parking lot through the app, and the system uses that information to calculate and navigate the route to the parking lot.

[0655] An example of a prompt is, "When you arrive in the city center, automatically analyze available parking spaces and begin navigation to the best available parking space." This allows users to find parking spaces without stress and park their self-driving vehicles smoothly.

[0656] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0657] Step 1:

[0658] The server acquires video data from surveillance cameras installed in the parking lot, analyzes the video data using a video analysis means, and identifies vacant and occupied parking spaces. The analysis results are stored in a database.

[0659] Input: Video data from a surveillance camera

[0660] Output: Information on vacant and occupied parking spaces

[0661] Specific operation: After receiving real-time video data captured by the surveillance camera, the system uses video analysis technology such as OpenCV to determine the status of the parking space. The analysis results are stored in a database and retained for use in the next step.

[0662] Step 2:

[0663] The server obtains the user's current location via the GPS function of the user's smartphone or vehicle, and this location information is sent to the server in real time.

[0664] Input: GPS data from the user's smartphone or vehicle

[0665] Output: User's current location information

[0666] How it works: Smartphones and autonomous vehicles collect GPS data and send it to a server, which receives this location information and stores it in a database in real time.

[0667] Step 3:

[0668] The server analyzes the user's behavioral patterns based on their past parking history and schedule information, and uses the results of this analysis to predict their next parking needs.

[0669] Input: User's past parking history, schedule information

[0670] Output: Predicted data for next parking needs

[0671] How it works: The server references historical databases and uses machine learning algorithms to analyze user behavior patterns, which then predicts upcoming parking needs and generates data on future parking needs.

[0672] Step 4:

[0673] The server integrates the information obtained from the video analysis and location information acquisition means to provide users with optimal parking information and fees. Fees are dynamically set based on the location and usage of the parking lot.

[0674] Input: Information on available parking spaces, current location, behavioral pattern analysis results

[0675] Output: Optimal parking information and prices

[0676] How it works: The server integrates the collected data and uses an algorithm to select the most suitable parking spot. The fee optimization module then dynamically sets fees based on parking spot usage and location.

[0677] Step 5:

[0678] The server notifies the user's device of the calculated optimal parking information and fees using a notification method. This notification is done in real time, and if the user is driving, the information is provided through a voice guidance function.

[0679] Input: Best parking information, fee information

[0680] Output: Notification to the user's device

[0681] How it works: The server generates a notification message and sends it to the user's smartphone or tablet. If the user is driving, the information is provided as voice guidance.

[0682] Step 6:

[0683] Based on the parking information notified to the user, the route to the selected parking lot is calculated and the autonomous vehicle is navigated. Navigation is provided in real time using the navigation means.

[0684] Input: Selected parking lot information, current location information

[0685] Output: Navigation information to the parking lot

[0686] How it works: The server uses map services such as Google Maps API to calculate the optimal route from the current location to the selected parking lot, then sends the navigation information to the autonomous vehicle to guide it to the parking lot.

[0687] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0688] The present invention is a system that includes an image analysis means for analyzing the availability of parking spaces, a location information acquisition means for acquiring user location information and behavioral patterns, a parking information provision means for proposing optimal parking information and fees to the user based on information acquired from the analysis means and the location information acquisition means, a notification means for notifying the user of information from the parking information provision means to the user's terminal, a navigation means for navigating to the selected parking space based on information from the notification means, and an emotion engine for recognizing the user's emotions.

[0689] Program processing details

[0690] Server-side processing

[0691] 1. Video analysis methods

[0692] The server receives video data in real time from security cameras installed in the parking lot.

[0693] The video analytics module analyzes the video data to determine the availability of parking spaces, thereby identifying vacant and occupied spaces and storing this information in a database.

[0694] 2. Location information acquisition means

[0695] The server uses the smartphone's GPS function to obtain the user's current location information, which is then sent to the server in real time.

[0696] 3. Behavioral pattern analysis methods

[0697] The server uses a behavioral pattern analysis module to analyze the user's behavioral patterns based on their past parking history and schedule information, which then predicts their next parking needs and stores future parking patterns in a database.

[0698] 4. Emotion Engine

[0699] The server uses an emotion engine to analyze the user's facial expressions and tone of voice to recognize the user's emotions in real time. It uses facial recognition and voice analysis technology to identify the user's emotions while operating the app.

[0700] 5. Means of providing parking information

[0701] The server calculates the most suitable parking information and fees for the user based on information obtained from the video analysis means, location information acquisition means, behavioral pattern analysis means and emotion engine.

[0702] The fee optimization module dynamically calculates the optimal fee by taking into account the location and usage of the parking lot, as well as the user's emotions identified by the emotion engine.

[0703] 6. Means of notification

[0704] The server notifies the user of the optimal parking information and fees via a smartphone app or tablet.

[0705] Notifications are provided in real time, and if the user is driving, information is provided through a voice prompt function.

[0706] 7. Navigation Methods

[0707] The server provides a navigation function based on the notified parking lot information.

[0708] The route to the parking lot selected by the user is calculated and navigation is performed in real time.

[0709] Specific usage scenarios

[0710] When a driver uses a parking lot

[0711] 1. User (Driver)

[0712] Users launch the smartphone app and search for parking spaces. The app automatically obtains their current location and requests information about the nearest parking spaces.

[0713] 2. Server

[0714] The server receives video data from security cameras, and the video analysis module identifies available parking spaces.

[0715] The server analyzes the user's location information and behavioral patterns, as well as the user's emotions identified by an emotion engine, to select the most suitable parking space.

[0716] The server transmits the selected parking lot information and the dynamically calculated fee to the user's terminal via the notification means.

[0717] 3. Device (smartphone app)

[0718] The user's smartphone app receives notifications from the server and displays information about available parking spaces and fees on the screen. If the user is driving, the voice guidance function is activated.

[0719] Once the user selects a suggested parking spot, the app sends the information to the server and begins navigation.

[0720] 4. Navigation Methods

[0721] The server sends navigation information to the selected parking lot to the device and guides the user through the app.

[0722] When the parking lot owner manages the system

[0723] 1. User (parking lot owner)

[0724] Parking lot owners have access to a cloud-based management dashboard to view parking lot usage and revenue data.

[0725] 2. Server

[0726] The server collects data in real time and displays it on a dashboard, displaying the information in a format that is easy for the owner to manage.

[0727] 3. User (parking lot owner)

[0728] Owners set pricing and promotions through the dashboard, and the settings information is stored on the server.

[0729] The server runs a fee optimization module based on the new fee settings and dynamically adjusts the parking fee.

[0730] This allows users to efficiently find the best parking space based on their emotions, and parking lot owners to maximize their parking lot revenue.

[0731] The processing flow will be explained below.

[0732] Step 1:

[0733] The server receives video data in real time from security cameras installed in the parking lot.

[0734] Step 2:

[0735] The server's video analysis module analyzes the availability of parking spaces based on the received video data, identifies vacant and occupied spaces, and stores the results in a database.

[0736] Step 3:

[0737] The user (driver) launches the smartphone app and obtains their current location information. The device (smartphone app) obtains the location information using its GPS function and sends it to the server.

[0738] Step 4:

[0739] The server uses a behavioral pattern analysis module to analyze the user's behavioral patterns based on the user's current location information, past parking history, and schedule information, and predicts the user's next parking needs.

[0740] Step 5:

[0741] The server's emotion engine receives the user's facial recognition data and voice data from the smartphone app and analyzes it to recognize the user's emotions in real time, for example, determining whether the user is tired, angry, or relaxed.

[0742] Step 6:

[0743] The server's parking lot information providing means calculates the optimal parking lot information and fees for the user based on information obtained from the video analysis means, location information acquisition means, behavior pattern analysis means, and emotion engine. Taking into account the user's emotional state, it can suggest, for example, a nearby and safe parking lot to a tired user, and a slightly further but cheaper parking lot to a relaxed user.

[0744] Step 7:

[0745] The fee optimization module of the server dynamically calculates the optimal fee based on the location and usage of the parking lot, and reflects the result in the parking lot information providing means.

[0746] Step 8:

[0747] The server's notification method notifies the user's device (smartphone app) in real time of the optimal parking information and fees. If the user is driving, the notification is provided through a voice guidance function.

[0748] Step 9:

[0749] The user (driver) checks the parking lot information and fee information provided through the smartphone app and selects a parking lot. The device (smartphone app) then sends the selected parking lot information to the server.

[0750] Step 10:

[0751] The server generates navigation information to the selected parking lot and sends it to the device (smartphone app).

[0752] Step 11:

[0753] The device (smartphone app) guides the user to the parking lot based on the navigation information received from the server. The user (driver) follows the navigation on the smartphone app to head to the parking lot.

[0754] Step 12:

[0755] Parking lot owners have access to a cloud-based management dashboard that provides real-time access to parking lot usage and revenue data.

[0756] Step 13:

[0757] The server receives the owner's pricing and promotion information provided through the management dashboard, and recalculates the price optimization module based on it, resulting in new pricing information being provided to the user.

[0758] Example 2

[0759] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0760] While existing parking information systems provide information on parking availability and fees, they have limitations in suggesting optimal parking spaces that take into account the user's behavioral patterns and emotions. Furthermore, they lack support for selecting parking spaces that reflects the user's emotional state when operating the app and reduces stress. For these reasons, there was a need for the development of a new system that would improve user convenience while maximizing parking lot owners' profits.

[0761] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0762] In this invention, the server includes a video analysis means for analyzing the availability of parking spaces, a location information acquisition means for acquiring the user's location information and behavioral patterns, and an emotion engine for recognizing the user's emotions, making it possible to propose optimal parking information and fees taking into account the user's current location, behavioral patterns, and emotional state.

[0763] The "video analysis means" is a means for analyzing video data obtained from security cameras and the like, and identifying vacant and occupied spaces within a parking lot.

[0764] "Location information acquisition means" refers to a means of acquiring the user's current location information using a smartphone or GPS device and transmitting that information to a server.

[0765] The "behavioral pattern analysis means" is a means that uses data analysis and modeling technology to predict a user's next parking needs based on their past parking history and schedule information.

[0766] The "emotion engine" is a means of analyzing the user's facial expressions and tone of voice to recognize their emotional state in real time. It uses facial recognition and voice analysis technologies.

[0767] The "parking lot information providing means" is a means that integrates information obtained from the video analysis means, location information acquisition means, behavioral pattern analysis means, and emotion engine, and proposes optimal parking lot information and fees to users.

[0768] The "notification means" is a means by which the server notifies the user's device, such as a smartphone app or tablet, of the optimal parking information and fees obtained from the parking information providing means.

[0769] The "navigation means" is a means for calculating a route to a parking lot selected by a user based on the notified parking lot information and providing real-time navigation.

[0770] The present invention is a system that analyzes parking availability and provides users with optimal parking information and fees. This system includes a video analysis unit, a location information acquisition unit, a behavioral pattern analysis unit, an emotion engine, a parking information provision unit, a notification unit, and a navigation unit.

[0771] Server-side implementation

[0772] 1. Video analysis methods

[0773] The server receives video data from security cameras and uses a video analysis module to identify vacant parking spaces. This analysis is performed using image processing libraries such as OpenCV. The analysis results are stored in a database.

[0774] 2. Location information acquisition means

[0775] The server uses the smartphone's GPS function to obtain the user's current location information in real time. The device encrypts the location information using the HTTPS protocol and sends it to the server.

[0776] 3. Behavioral pattern analysis methods

[0777] The server retrieves the user's past parking history and schedule information from the database, analyzes it using a behavioral pattern analysis module, and predicts the next parking need using a machine learning model (e.g., Scikit-Learn's clustering algorithm).

[0778] 4. Emotion Engine

[0779] The server uses an emotion engine to recognize emotions by analyzing the user's facial expressions and tone of voice, using Microsoft Azure's facial recognition API and voice analysis technology, for example.

[0780] 5. Means of providing parking information

[0781] The server calculates the optimal parking information and fees using a fee optimization module (using, for example, NumPy) based on the information obtained from the video analysis means, location information acquisition means, behavioral pattern analysis means, and emotion engine, and sends the information to the notification means.

[0782] 6. Means of notification

[0783] The server notifies the user's device, such as a smartphone app or tablet, of the calculated parking information and fees in real time. The notification also has a voice guidance function, so it can be used by users who are driving. The voice notification uses the Google Text-to-Speech API.

[0784] 7. Navigation Methods

[0785] The server calculates the route to the selected parking lot using a map API (e.g., Google Maps API) and generates navigation information. The information is sent to the device, which displays the route on a map in real time and provides voice guidance.

[0786] Specific examples of usage scenarios

[0787] When a driver uses a parking lot

[0788] 1. User (Driver)

[0789] Launch the smartphone app and request "Find the nearest available parking space." The app will automatically obtain your current location and send it to the server.

[0790] 2. Server

[0791] The system analyzes security camera footage to identify available parking spaces, then selects the most suitable parking spot based on the acquired location information and the user's behavioral patterns and emotional state, and notifies the user of the calculated information and fees.

[0792] 3. Device (smartphone app)

[0793] Users will receive notifications and be shown information about available parking spaces and fees. If they are driving, voice guidance will be activated, telling them, "The nearest parking lot is 300 meters ahead on the right." Once the user selects a parking spot, navigation will begin.

[0794] 4. Navigation Methods

[0795] The system calculates the route to the selected parking lot and sends navigation information to the device, which then displays the route on a map and provides voice guidance such as "Turn right at the next intersection."

[0796] When the parking lot owner manages the system

[0797] 1. User (parking lot owner)

[0798] Access your cloud-based management dashboard and click "Check usage."

[0799] 2. Server

[0800] The data collected in real time is reflected in the dashboard, displaying usage and revenue data.

[0801] 3. User (parking lot owner)

[0802] Use the dashboard to set up pricing and promotions, then save the new settings to the server.

[0803] 4. Server

[0804] The new pricing is reflected in the pricing optimization module, dynamically adjusting parking fees.

[0805] Prompt Sentence Examples

[0806] "Please tell me the nearest available parking lot from my current location."

[0807] "Please set up special parking rates for this weekend's event."

[0808] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0809] Processing Steps

[0810] Step 1: Obtaining and sending location information

[0811] 1. User: Launches the smartphone app and requests to "find the nearest available parking space."

[0812] 2. Device: Uses the smartphone's GPS function to obtain current location information.

[0813] 3. Device: The acquired location information is encrypted using the HTTPS protocol and sent to the server.

[0814] Input: User requests, smartphone GPS data.

[0815] Output: Your location information is encrypted and sent to the server.

[0816] Step 2: Identifying parking availability through video analysis

[0817] 1. Server: Receives video data in real time from security cameras installed in the parking lot.

[0818] 2. Server: Uses a video analytics module (e.g., OpenCV) to determine the availability of parking spaces, specifically identifying the presence or absence of a vehicle and identifying vacant and occupied spaces.

[0819] 3. Server: Stores the analysis results in a database.

[0820] Input: Real-time video data from security cameras.

[0821] Output: Availability information stored in a database.

[0822] Step 3: Analyze behavioral patterns

[0823] 1. Server: Obtains the user's past parking history and schedule information from the database.

[0824] 2. Server: Analyzes user behavior patterns using a behavioral pattern analysis module (e.g., Scikit-Learn clustering algorithm).

[0825] 3. Server: Save the results of predicting the next parking need in a database.

[0826] Input: Historical parking history and schedule information retrieved from the database.

[0827] Output: Behavioral patterns and parking needs prediction results stored in a database.

[0828] Step 4: Emotion Recognition

[0829] 1. Device: Using the smartphone's camera and microphone, the user's facial expressions and tone of voice are captured in real time.

[0830] 2. Terminal: Sends the acquired data to the server.

[0831] 3. Server: Uses an emotion engine (e.g., Microsoft Azure facial recognition API) to recognize the user's emotions from facial expressions and tone of voice.

[0832] Input: Facial expression data and voice data obtained from the smartphone camera and microphone.

[0833] Output: User emotion data recognized by the server.

[0834] Step 5: Calculate the best parking information

[0835] 1. Server: Integrates information obtained from video analysis, location information acquisition, behavioral pattern analysis, and emotion engine.

[0836] 2. Server: Calculates optimal parking information and fees using a fee optimization module (e.g., NumPy).

[0837] 3. Server: Sends the calculated parking information and fees to the notification means.

[0838] Input: Availability information from video analysis, location information, behavioral pattern analysis results, emotion recognition results.

[0839] Output: Optimal parking information and pricing information.

[0840] Step 6: Parking information notification

[0841] 1. Server: Calculated parking information and fees are sent in real time to user devices such as smartphone apps and tablets.

[0842] 2. Device: Receives notifications and displays information on the screen. If you are driving, voice guidance is enabled.

[0843] Input: Best parking information and prices from the server.

[0844] Output: Notifications to user terminals, screen displays and voice guidance.

[0845] Step 7: Providing navigation

[0846] 1. Server: Calculate the route to the selected parking lot using a map API (e.g., Google Maps API).

[0847] 2. Server: Sends navigation information to the device.

[0848] 3. Terminal: Receives navigation information sent from the server and displays the route on a map in real time. Voice guidance is also provided simultaneously.

[0849] Input: Best parking information, map data.

[0850] Output: Navigation information, map display and voice guidance.

[0851] (Application example 2)

[0852] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0853] Autonomous vehicles are required to quickly and accurately grasp the availability of parking spaces, and to select and guide the optimal parking space by taking into account the driver's location, past behavioral patterns, and emotional state. However, current technology does not have a system that comprehensively considers these factors, which makes parking space selection inconvenient and does not sufficiently reduce stress. Furthermore, after a parking space is selected, navigation must also provide appropriate guidance in real time, but this has not been fully realized.

[0854] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0855] In this invention, the server includes an image analysis means for analyzing the availability of parking spaces, a location information acquisition means for acquiring user location information and behavioral patterns, an emotion analysis means for analyzing user emotions in real time, a parking information provision means for proposing optimal parking information and fees to the user based on information acquired from the analysis means, the location information acquisition means, and the emotion analysis means, a notification means for notifying the user of information from the parking information provision means to the user's terminal, and a navigation means for navigating to the selected parking space. This enables quick and accurate understanding of parking space availability, selection of a parking space taking into account the driver's emotional state, and appropriate navigation in real time.

[0856] The "video analysis means for analyzing parking space availability" is a device that analyzes video data acquired from cameras installed in the parking lot and identifies vacant and occupied parking spaces.

[0857] "Location information acquisition means for acquiring user location information and behavioral patterns" refers to a device that acquires the user's current location information using technology such as GPS, and analyzes behavioral patterns based on past parking history and schedule information.

[0858] The "emotion analysis means" is a device that analyzes the user's facial expressions and voice to analyze their emotional state in real time.

[0859] The "parking lot information providing means" is a device that calculates the most suitable parking lot information and fees for the user based on the information obtained from the video analysis means, location information acquisition means, and emotion analysis means.

[0860] The "notification means" is a device that notifies the user's terminal of the information obtained by the parking lot information providing means in real time.

[0861] The "navigation means" is a device that calculates the optimal route to the selected parking lot and provides guidance to the user in real time.

[0862] The present invention uses hardware and software to realize the following system configuration and processing flow: This system provides optimal parking lot selection and navigation specifically for autonomous vehicles.

[0863] The server drives various modules to achieve the following main functions:

[0864] 1. Video analysis methods

[0865] The server receives video data in real time from cameras installed inside the autonomous vehicles. The video analytics module uses image processing libraries such as OpenCV to analyze the video data and determine the availability of parking spaces. This identifies vacant and occupied parking spaces and stores this information in a database.

[0866] 2. Location information acquisition means

[0867] The server uses the GPS module to obtain the vehicle's current location information, which then transmits the user's location information to the server in real time.

[0868] 3. Behavioral pattern analysis methods

[0869] The server uses a behavioral pattern analysis module to analyze the user's behavioral patterns based on their past parking history and schedule information, which then predicts their next parking needs and stores future parking patterns in a database.

[0870] 4. Emotion analysis method

[0871] The server uses a camera and microphone to analyze the user's facial expressions and tone of voice. It uses an emotion analysis engine (such as IBM Watson Tone Analyzer) to recognize the user's emotions in real time. It then uses facial expression analysis and voice analysis technologies to quantify the user's emotional state.

[0872] 5. Means of providing parking information

[0873] The server calculates the optimal parking information and fees for the user based on information obtained from video analysis, location information acquisition, behavioral pattern analysis, and emotion analysis.The optimal fee is calculated dynamically, taking into account the location and usage conditions of the parking lot, as well as the user's emotions identified by the emotion engine.

[0874] 6. Means of notification

[0875] The server displays the best parking information and prices on the vehicle's display, providing real-time notifications and, if the driver is driving, providing information via the voice guidance system, allowing the driver to get the information they need without having to look away.

[0876] 7. Navigation Methods

[0877] The server automatically calculates the route to the selected parking lot and provides real-time navigation, linking with the vehicle's navigation system to provide optimal route guidance.

[0878] This will enable quick and accurate understanding of parking availability, parking lot selection that takes into account the user's emotional state, and appropriate navigation in real time.

[0879] Example: A vehicle camera detects available parking spaces in real time, and the user's current location is identified by GPS. Based on past parking patterns, frequently used parking areas are prioritized. Furthermore, if the user is feeling stressed, emotion analysis can be used to suggest a suitable parking space for the user.

[0880] Example prompts for generative AI models

[0881] Design a system for autonomous vehicles that analyzes parking availability in real time, suggests the best parking spot and its price based on the user's location, behavioral patterns, and emotions, and then navigates to the parking spot.

[0882] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0883] Step 1:

[0884] The server receives video data in real time from cameras installed inside the vehicles. The video data acquired from the cameras is used as input and analyzed using image processing libraries such as OpenCV. After analyzing the video, vacant and occupied parking spaces are identified and the information is stored in a database. This allows the parking lot availability to be updated in real time.

[0885] Input: Video data from the camera

[0886] Data processing: Video analysis using OpenCV

[0887] Output: Information about identified vacant parking spaces

[0888] Step 2:

[0889] The server obtains the current location information from the GPS module installed in the vehicle. It receives the current location information as input and sends it to the server. The location information is stored in a database and used for subsequent processing.

[0890] Input: Location information from GPS module

[0891] Data processing: Acquiring and storing location information

[0892] Output: Current location information stored in a database

[0893] Step 3:

[0894] The server analyzes behavioral patterns based on past parking history and schedule information. It obtains past history and schedule information from the database and uses this as input. It uses an analysis module such as Python to predict next parking needs and saves the analysis results in the database.

[0895] Input: Past parking history and schedule information

[0896] Data processing: Behavioral pattern analysis

[0897] Output: Analysis results stored in a database

[0898] Step 4:

[0899] The server uses a camera and microphone to collect the user's facial expressions and tone of voice. This data is used as input to analyze emotions in real time using an emotion analysis engine (such as IBM Watson Tone Analyzer). The analysis results are stored in a database.

[0900] Input: facial and voice data from camera and microphone

[0901] Data processing: Analysis using a sentiment analysis engine

[0902] Output: Sentiment analysis results stored in a database

[0903] Step 5:

[0904] The server integrates information obtained from the video analysis means, location information acquisition means, behavioral pattern analysis means, and emotion analysis means. Using this data as input, it calculates the optimal parking information and fees for the user. Based on the parking information and user information in the database, the fee optimization module dynamically calculates the optimal fee.

[0905] Input: Results of video analysis, location information, behavioral pattern analysis, and emotion analysis

[0906] Data processing: Integration of various information and calculation of optimal pricing

[0907] Output: Optimal parking information and prices

[0908] Step 6:

[0909] The server then notifies the vehicle's display and voice guidance system of the calculated optimal parking information and fees, allowing users to receive the necessary information visually and audibly.

[0910] Input: Best parking information and prices

[0911] Data processing: Creating notification messages

[0912] Output: Notification to vehicle display and voice guidance system

[0913] Step 7:

[0914] The server automatically calculates the optimal route to the selected parking lot and provides real-time navigation, linking with the vehicle's navigation system to provide directions to help users smoothly reach their destination.

[0915] Input: Selected parking information

[0916] Data processing: Calculation of optimal route using route calculation algorithm

[0917] Output: Instructions to the vehicle's navigation system

[0918] 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.

[0919] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0920] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0921] [Third embodiment]

[0922] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0923] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0924] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0925] 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.

[0926] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0927] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0928] 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.

[0929] 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.

[0930] 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 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.

[0931] 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.

[0932] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0933] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0934] The present invention provides a system that includes an image analysis means for analyzing the availability of parking spaces, a location information acquisition means for acquiring user location information and behavioral patterns, a parking information provision means for proposing optimal parking information and fees to the user based on information obtained from the analysis means and location information acquisition means, a notification means for notifying the user of information from the parking information provision means to their terminal, and a navigation means for navigating to the selected parking space based on information from the notification means.

[0935] Program processing details

[0936] Server-side processing

[0937] 1. Video analysis methods

[0938] The server receives video data in real time from security cameras installed in the parking lot.

[0939] The video analytics module analyzes the video data to determine the availability of parking spaces, thereby identifying vacant and occupied spaces and storing this information in a database.

[0940] 2. Location information acquisition means

[0941] The server uses the smartphone's GPS function to obtain the user's current location information, which is then sent to the server in real time.

[0942] 3. Behavioral pattern analysis methods

[0943] The server uses a behavioral pattern analysis module to analyze the user's behavioral patterns based on their past parking history and schedule information, which then predicts their next parking needs and stores future parking patterns in a database.

[0944] 4. Means of providing parking information

[0945] The server calculates the most suitable parking information and fees for the user based on the information obtained from the video analysis means and location information acquisition means.

[0946] The fee optimization module dynamically calculates the optimal fee based on the parking lot's location and usage.

[0947] 5. Means of notification

[0948] The server notifies the user of the optimal parking information and fees via a smartphone app or tablet.

[0949] Notifications are provided in real time, and if the user is driving, information is provided through a voice prompt function.

[0950] 6. Navigation Methods

[0951] The server provides a navigation function based on the notified parking lot information.

[0952] The route to the parking lot selected by the user is calculated and navigation is performed in real time.

[0953] Specific usage scenarios

[0954] When a driver uses a parking lot

[0955] 1. User (Driver)

[0956] Users launch the smartphone app and search for parking spaces. The app automatically obtains their current location and requests information about the nearest parking spaces.

[0957] 2. Server

[0958] The server receives video data from security cameras, and the video analysis module identifies available parking spaces.

[0959] The server analyzes the user's location information and behavioral patterns to select the most suitable parking space.

[0960] The server transmits the selected parking lot information and the dynamically calculated fee to the user's terminal via the notification means.

[0961] 3. Device (smartphone app)

[0962] The user's smartphone app receives notifications from the server and displays information about available parking spaces and fees on the screen. If the user is driving, the voice guidance function is activated.

[0963] Once the user selects a suggested parking spot, the app sends the information to the server and begins navigation.

[0964] 4. Navigation Methods

[0965] The server sends navigation information to the selected parking lot to the device and guides the user through the app.

[0966] When the parking lot owner manages the system

[0967] 1. User (parking lot owner)

[0968] Parking lot owners have access to a cloud-based management dashboard to view parking lot usage and revenue data.

[0969] 2. Server

[0970] The server collects data in real time and displays it on a dashboard, displaying the information in a format that is easy for the owner to manage.

[0971] 3. User (parking lot owner)

[0972] Owners set pricing and promotions through the dashboard, and the settings information is stored on the server.

[0973] The server runs a fee optimization module based on the new fee settings and dynamically adjusts the parking fee.

[0974] This allows users to easily find parking spaces and use them efficiently, while allowing parking lot owners to maximize their parking lot revenue.

[0975] The processing flow will be explained below.

[0976] Step 1:

[0977] The server receives video data in real time from security cameras installed in the parking lot.

[0978] Step 2:

[0979] The server's video analysis module analyzes the availability of parking spaces based on the received video data, identifies vacant and occupied spaces, and stores the results in a database.

[0980] Step 3:

[0981] The user (driver) launches the smartphone app and obtains their current location information. The device (smartphone app) obtains the location information using its GPS function and sends it to the server.

[0982] Step 4:

[0983] The server uses a behavioral pattern analysis module to analyze the user's behavioral patterns based on the user's current location information, past parking history, and schedule information, and predicts the user's next parking needs.

[0984] Step 5:

[0985] The parking lot information providing means of the server calculates the most suitable parking lot information and fees for the user based on the information obtained from the video analysis means and the location information acquisition means.

[0986] Step 6:

[0987] The fee optimization module of the server dynamically calculates the optimal fee based on the location and usage of the parking lot, and reflects the result in the parking lot information providing means.

[0988] Step 7:

[0989] The server's notification means notifies the user's device (smartphone app) in real time of the optimal parking information and fees.

[0990] Step 8:

[0991] The user (driver) checks the parking lot information and fee information provided through the smartphone app and selects a parking lot. The device (smartphone app) then sends the selected parking lot information to the server.

[0992] Step 9:

[0993] The server generates navigation information to the selected parking lot and sends it to the device (smartphone app).

[0994] Step 10:

[0995] The device (smartphone app) guides the user to the parking lot based on the navigation information received from the server. The user (driver) follows the navigation on the smartphone app to head to the parking lot.

[0996] Step 11:

[0997] Parking lot owners have access to a cloud-based management dashboard that provides real-time access to parking lot usage and revenue data.

[0998] Step 12:

[0999] The server receives the owner's pricing and promotion information provided through the management dashboard, and recalculates the price optimization module based on it, resulting in new pricing information being provided to the user.

[1000] Example 1

[1001] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1002] Real-time video analysis and analysis of user behavior patterns are necessary to efficiently grasp parking availability and propose appropriate parking information and fees to users. However, if these functions are insufficient, it becomes difficult to provide appropriate parking information, which causes trouble and stress for users. Furthermore, providing only visual information while driving can be dangerous, so safe information provision via audio guidance is also required.

[1003] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1004] In this invention, the server includes: a video analysis means for analyzing the availability of parking spaces; a location information acquisition means for acquiring user location information and behavioral patterns; a parking information provision means for proposing optimal parking information and fees to the user based on information acquired from the analysis means and the location information acquisition means; a notification means for notifying the user of information from the parking information provision means to the user's terminal; a navigation means for navigating to the selected parking space based on information from the notification means; a server including a video analysis algorithm for determining the availability of parking spaces using a video analysis module; a server including a behavior pattern analysis module for analyzing behavioral patterns based on the user's past parking history and schedule information; a terminal equipped with a voice guidance function for providing parking information via voice guidance while the vehicle is being driven; and a server equipped with a navigation function for providing navigation information in real time. This makes it possible to efficiently analyze available parking spaces in real time and safely and efficiently provide optimal parking information and navigation to the user.

[1005] The "video analysis means for analyzing parking space availability" is a device or module that analyzes video data acquired from cameras installed in the parking lot and identifies vacant and occupied parking spaces in real time.

[1006] "Location information acquisition means for acquiring user location information and behavioral patterns" refers to a device or system that uses a smartphone or other GPS device to collect a user's current location information and past behavioral history.

[1007] The "parking lot information providing means" is a device or system that calculates and presents optimal parking lot information and fees to users based on information obtained from the video analysis means and location information acquisition means.

[1008] The "notification means" is a device or system that notifies the user of calculated parking information and fees in real time via their smartphone app or tablet.

[1009] The "navigation means" is a device or system that provides real-time guidance on routes to a selected parking lot based on the information provided by the notification means.

[1010] "Video analysis algorithms" are image processing technologies and machine learning models used to analyze acquired video data and determine the availability of parking spaces.

[1011] The "behavior pattern analysis module" is a device or software module for predicting a user's parking behavior based on the user's past parking history and schedule information.

[1012] A "voice guidance function" is a device or system that conveys information through voice to safely provide parking information and navigation information while driving a vehicle.

[1013] The "navigation function that provides real-time navigation information" is a function that calculates the route to the parking lot selected by the user in real time and displays and provides voice guidance on the user's smartphone app.

[1014] The present invention is a system that includes an image analysis means for analyzing parking availability, a location information acquisition means for acquiring user location information and behavioral patterns, a parking information provision means for proposing optimal parking information and fees to users based on that information, a notification means for notifying the user of parking information to their terminal, and a navigation means for navigating to the selected parking lot based on that information.

[1015] System configuration

[1016] 1. Video analysis methods

[1017] The server uses video reception technologies such as RTSP (Real-Time Streaming Protocol) to receive video data in real time from security cameras installed in the parking lot.

[1018] The server uses a video analysis module (e.g., OpenCV or TensorFlow) to analyze the availability of parking spaces based on the captured video data, thereby identifying vacant and occupied spaces and storing the results in a database.

[1019] 2. Location information acquisition means

[1020] The device (smartphone) uses its GPS function to obtain the user's current location information. When the user launches the application, the current location information is periodically sent to the server.

[1021] 3. Behavioral pattern analysis methods

[1022] The server collects users' past parking history and schedule information, and uses a behavioral pattern analysis module to predict their next parking needs. The analysis results are stored in a database.

[1023] 4. Means of providing parking information

[1024] The server integrates data obtained from the video analysis and location information acquisition means to calculate the optimal parking information and fees for the user. The fee optimization module dynamically provides the optimal fee, taking into account real-time usage and the location of the parking lot.

[1025] 5. Means of notification

[1026] The server notifies users of the best parking information and fees via a smartphone app or tablet. For example, it sends real-time information about available parking spaces via push notifications or SMS. It also provides information safely to drivers through a voice guidance function.

[1027] 6. Navigation Methods

[1028] The server calculates the route to the parking lot selected by the user and sends the navigation information to the device, which uses a dedicated app to display the route on a map and provide real-time voice guidance.

[1029] Specific usage scenarios

[1030] When a driver uses a parking lot

[1031] The user (driver) starts the smartphone app, which acquires the current location information and searches for the nearest parking lot information. The app then sends a request to the server.

[1032] The server receives video data from security cameras, and the video analysis module identifies available parking spaces. It also analyzes the user's location and behavioral patterns to calculate the optimal parking information and fees.

[1033] The server notifies the user of the selected parking lot information and fees via a smartphone app. If the user is driving, the information is provided via the device's voice guidance function.

[1034] When the user (driver) selects a suggested parking lot, the server calculates navigation information based on that information and sends it to the terminal.

[1035] When the parking lot owner manages the system

[1036] Users (parking lot owners) access a cloud-based management dashboard to view parking lot usage and revenue data.

[1037] The server then feeds the collected data into a dashboard in real time, displaying revenue data and usage in an easy-to-manage format.

[1038] Prompt Sentence Examples

[1039] "I want to use the feature in my app that notifies me of parking availability and best parking options. How can I do this?"

[1040] The system allows users to find parking spaces efficiently and parking lot owners to maximize parking lot utilization and revenue.

[1041] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1042] Step 1:

[1043] Video data reception

[1044] The server receives real-time video data from security cameras installed in the parking lot, which then transmit the video data to the server via the network using RTSP (Real-Time Streaming Protocol).

[1045] Input: Video stream from a security camera.

[1046] Output: Raw video data stored on the server.

[1047] Step 2:

[1048] Video Analysis

[1049] The server passes the received video data to a video analysis module, which uses image processing algorithms (such as OpenCV or TensorFlow) to identify vacant parking spaces in real time. The analysis results are used to generate data indicating the availability of each parking space.

[1050] Input: Raw video data.

[1051] Output: Information about available parking spaces (stored in a database).

[1052] Step 3:

[1053] Location information acquisition

[1054] When a user launches an application, the device (smartphone) uses its GPS function to obtain current location information. The application then periodically sends this location information to a server.

[1055] Input: GPS data from your smartphone.

[1056] Output: Real-time location information transmitted to the server.

[1057] Step 4:

[1058] Behavioral pattern analysis

[1059] The server collects users' past parking history and schedule information, analyzes this data using a behavioral pattern analysis module, and stores the analysis results in a database as information to predict future parking needs.

[1060] Input: User's past parking history, schedule information.

[1061] Output: Predicted user parking needs (stored in database).

[1062] Step 5:

[1063] Parking information provided

[1064] The server integrates the data obtained from the video analysis means and the location information acquisition means, and calculates the optimal parking information and fees using the fee optimization module. The fee is dynamically determined taking into account real-time parking lot usage and location conditions.

[1065] Input: available parking space information, user location information, predicted parking needs.

[1066] Output: Optimal parking information and prices (passed to notification means).

[1067] Step 6:

[1068] Information Notification

[1069] The server then sends calculated parking information and fees to the user's smartphone app or tablet, with real-time updates and voice guidance enabled if the user is driving.

[1070] Input: Best parking information and prices.

[1071] Output: The notification message sent to the user's device.

[1072] Step 7:

[1073] Navigation provided

[1074] The server calculates navigation information to the parking lot selected by the user and sends it to the device via a dedicated app. The smartphone displays the route on a map and provides real-time voice guidance.

[1075] Input: Location information of selected parking lot, user's current location information.

[1076] Output: Navigation information (displayed on the user's device and voice guidance).

[1077] In this way, the system can efficiently analyze parking availability in real time and provide users with optimal parking information and navigation.

[1078] (Application example 1)

[1079] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1080] As autonomous vehicles become more widespread, there is a demand for systems that automatically find parking spaces and park at appropriate fees, without the driver having to search for a parking space themselves. However, current systems lack the means to analyze parking availability in real time, and do not optimize parking fees or suggest parking spaces based on behavioral patterns. Furthermore, there is no integrated system available for autonomous vehicles to navigate to a selected parking space. To address these challenges, a system combining advanced video analysis technology, location information acquisition technology, and behavioral pattern analysis technology is needed.

[1081] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1082] In this invention, the server includes an image analysis means for analyzing the availability of parking spaces, a location information acquisition means for acquiring user location information and behavioral patterns, a parking information provision means for proposing optimal parking information and fees to the user based on information acquired from the analysis means and the location information acquisition means, a notification means for notifying the user's terminal of information from the parking information provision means, and a navigation means for navigating the autonomous vehicle to the selected parking space based on information from the notification means. This allows the user to obtain optimal parking information and fees in real time, and enables the autonomous vehicle to smoothly navigate to the selected parking space.

[1083] The "video analysis means" is a means for analyzing video data acquired from a monitoring device installed in a parking lot and determining the availability and occupancy status of the parking lot in real time.

[1084] "Location information acquisition means" refers to a means of collecting location data in real time using technologies such as GPS in order to acquire user location information and behavioral patterns.

[1085] The "parking lot information providing means" is a means for proposing optimal parking lot information and fees to users based on information obtained from the video analysis means and location information acquisition means.

[1086] The "notification means" is a means for notifying the user's terminal of the information obtained by the parking lot information providing means, and may be email, a smartphone app, voice guidance, or the like.

[1087] The "navigation means" is a means for calculating a route to a parking lot selected by a user based on information obtained from the notification means, and guiding an autonomous vehicle to that parking lot.

[1088] A "surveillance camera" is a device that captures the situation in a parking lot as video data in real time.

[1089] An "autonomous vehicle" refers to a vehicle that can drive and park automatically.

[1090] "Behavioral patterns" are predictive data on a user's next parking needs and behavior, analyzed based on their past parking history, schedule information, etc.

[1091] "Optimal parking information" is information on the most convenient parking lot, selected based on the user's location and behavioral patterns.

[1092] "Fee" refers to the cost for using a parking lot, and is dynamically set based on factors such as parking time and the location of the parking lot.

[1093] The present invention relates to a parking assistance system for autonomous vehicles, and in particular to a system that analyzes parking lot availability in real time and provides users with optimal parking lot information and fees. This system includes an image analysis means for analyzing parking lot availability, a location information acquisition means for acquiring user location information and behavior patterns, a parking lot information provision means for proposing optimal parking lot information and fees based on information obtained from the analysis means and the location information acquisition means, a notification means for notifying the user's terminal of information from the parking lot information provision means, and a navigation means for navigating the autonomous vehicle to a selected parking lot based on information from the notification means.

[1094] The specific configuration of the system will be explained below. The system's video analysis means acquires video data using surveillance cameras installed in parking lots and analyzes the parking lot availability in real time. This identifies vacant and occupied parking spaces and stores this information in a database.

[1095] The location information acquisition means acquires location information using the GPS function of the user's smartphone or vehicle, allowing the user's current location to be grasped in real time. In addition, the behavior pattern analysis module analyzes the user's behavior patterns based on their past parking history and schedule information, and predicts their next parking needs.

[1096] The parking lot information providing means proposes optimal parking lot information and fees to the user based on information obtained from the video analysis means and location information acquisition means. Fees are dynamically set based on the location and usage status of the parking lot. The proposed parking lot information and fees are notified to the user's device in real time via the notification means.

[1097] The navigation means navigates the autonomous vehicle to the selected parking lot based on the notified parking lot information. This navigation is performed using a map service such as Google Maps API.

[1098] As a concrete example, consider a self-driving vehicle arriving in a city center. It can automatically find the best parking spot, calculate the cost, and provide route guidance. The process goes like this:

[1099] 1. When an autonomous vehicle arrives in a city center, the system analyzes available parking spaces in real time based on video data obtained from surveillance cameras.

[1100] 2. At the same time, the vehicle's GPS function is used to obtain current location information.

[1101] 3. Based on the user's past parking history and schedule information, behavioral patterns are analyzed and their next parking needs are predicted.

[1102] 4. Calculate the optimal parking information and fees and notify the user's device.

[1103] 5. After receiving the notification, the user selects the suggested parking lot through the app, and the system uses that information to calculate and navigate the route to the parking lot.

[1104] An example of a prompt is, "When you arrive in the city center, automatically analyze available parking spaces and begin navigation to the best available parking space." This allows users to find parking spaces without stress and park their self-driving vehicles smoothly.

[1105] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1106] Step 1:

[1107] The server acquires video data from surveillance cameras installed in the parking lot, analyzes the video data using a video analysis means, and identifies vacant and occupied parking spaces. The analysis results are stored in a database.

[1108] Input: Video data from a surveillance camera

[1109] Output: Information on vacant and occupied parking spaces

[1110] Specific operation: After receiving real-time video data captured by the surveillance camera, the system uses video analysis technology such as OpenCV to determine the status of the parking space. The analysis results are stored in a database and retained for use in the next step.

[1111] Step 2:

[1112] The server obtains the user's current location via the GPS function of the user's smartphone or vehicle, and this location information is sent to the server in real time.

[1113] Input: GPS data from the user's smartphone or vehicle

[1114] Output: User's current location information

[1115] How it works: Smartphones and autonomous vehicles collect GPS data and send it to a server, which receives this location information and stores it in a database in real time.

[1116] Step 3:

[1117] The server analyzes the user's behavioral patterns based on their past parking history and schedule information, and uses the results of this analysis to predict their next parking needs.

[1118] Input: User's past parking history, schedule information

[1119] Output: Predicted data for next parking needs

[1120] How it works: The server references historical databases and uses machine learning algorithms to analyze user behavior patterns, which then predicts upcoming parking needs and generates data on future parking needs.

[1121] Step 4:

[1122] The server integrates the information obtained from the video analysis and location information acquisition means to provide users with optimal parking information and fees. Fees are dynamically set based on the location and usage of the parking lot.

[1123] Input: Information on available parking spaces, current location, behavioral pattern analysis results

[1124] Output: Optimal parking information and prices

[1125] How it works: The server integrates the collected data and uses an algorithm to select the most suitable parking spot. The fee optimization module then dynamically sets fees based on parking spot usage and location.

[1126] Step 5:

[1127] The server notifies the user's device of the calculated optimal parking information and fees using a notification method. This notification is done in real time, and if the user is driving, the information is provided through a voice guidance function.

[1128] Input: Best parking information, fee information

[1129] Output: Notification to the user's device

[1130] How it works: The server generates a notification message and sends it to the user's smartphone or tablet. If the user is driving, the information is provided as voice guidance.

[1131] Step 6:

[1132] Based on the parking information notified to the user, the route to the selected parking lot is calculated and the autonomous vehicle is navigated. Navigation is provided in real time using the navigation means.

[1133] Input: Selected parking lot information, current location information

[1134] Output: Navigation information to the parking lot

[1135] How it works: The server uses map services such as Google Maps API to calculate the optimal route from the current location to the selected parking lot, then sends the navigation information to the autonomous vehicle to guide it to the parking lot.

[1136] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1137] The present invention is a system that includes an image analysis means for analyzing the availability of parking spaces, a location information acquisition means for acquiring user location information and behavioral patterns, a parking information provision means for proposing optimal parking information and fees to the user based on information acquired from the analysis means and the location information acquisition means, a notification means for notifying the user of information from the parking information provision means to the user's terminal, a navigation means for navigating to the selected parking space based on information from the notification means, and an emotion engine for recognizing the user's emotions.

[1138] Program processing details

[1139] Server-side processing

[1140] 1. Video analysis methods

[1141] The server receives video data in real time from security cameras installed in the parking lot.

[1142] The video analytics module analyzes the video data to determine the availability of parking spaces, thereby identifying vacant and occupied spaces and storing this information in a database.

[1143] 2. Location information acquisition means

[1144] The server uses the smartphone's GPS function to obtain the user's current location information, which is then sent to the server in real time.

[1145] 3. Behavioral pattern analysis methods

[1146] The server uses a behavioral pattern analysis module to analyze the user's behavioral patterns based on their past parking history and schedule information, which then predicts their next parking needs and stores future parking patterns in a database.

[1147] 4. Emotion Engine

[1148] The server uses an emotion engine to analyze the user's facial expressions and tone of voice to recognize the user's emotions in real time. It uses facial recognition and voice analysis technology to identify the user's emotions while operating the app.

[1149] 5. Means of providing parking information

[1150] The server calculates the most suitable parking information and fees for the user based on information obtained from the video analysis means, location information acquisition means, behavioral pattern analysis means and emotion engine.

[1151] The fee optimization module dynamically calculates the optimal fee by taking into account the location and usage of the parking lot, as well as the user's emotions identified by the emotion engine.

[1152] 6. Means of notification

[1153] The server notifies the user of the optimal parking information and fees via a smartphone app or tablet.

[1154] Notifications are provided in real time, and if the user is driving, information is provided through a voice prompt function.

[1155] 7. Navigation Methods

[1156] The server provides a navigation function based on the notified parking lot information.

[1157] The route to the parking lot selected by the user is calculated and navigation is performed in real time.

[1158] Specific usage scenarios

[1159] When a driver uses a parking lot

[1160] 1. User (Driver)

[1161] Users launch the smartphone app and search for parking spaces. The app automatically obtains their current location and requests information about the nearest parking spaces.

[1162] 2. Server

[1163] The server receives video data from security cameras, and the video analysis module identifies available parking spaces.

[1164] The server analyzes the user's location information and behavioral patterns, as well as the user's emotions identified by an emotion engine, to select the most suitable parking space.

[1165] The server transmits the selected parking lot information and the dynamically calculated fee to the user's terminal via the notification means.

[1166] 3. Device (smartphone app)

[1167] The user's smartphone app receives notifications from the server and displays information about available parking spaces and fees on the screen. If the user is driving, the voice guidance function is activated.

[1168] Once the user selects a suggested parking spot, the app sends the information to the server and begins navigation.

[1169] 4. Navigation Methods

[1170] The server sends navigation information to the selected parking lot to the device and guides the user through the app.

[1171] When the parking lot owner manages the system

[1172] 1. User (parking lot owner)

[1173] Parking lot owners have access to a cloud-based management dashboard to view parking lot usage and revenue data.

[1174] 2. Server

[1175] The server collects data in real time and displays it on a dashboard, displaying the information in a format that is easy for the owner to manage.

[1176] 3. User (parking lot owner)

[1177] Owners set pricing and promotions through the dashboard, and the settings information is stored on the server.

[1178] The server runs a fee optimization module based on the new fee settings and dynamically adjusts the parking fee.

[1179] This allows users to efficiently find the best parking space based on their emotions, and parking lot owners to maximize their parking lot revenue.

[1180] The processing flow will be explained below.

[1181] Step 1:

[1182] The server receives video data in real time from security cameras installed in the parking lot.

[1183] Step 2:

[1184] The server's video analysis module analyzes the availability of parking spaces based on the received video data, identifies vacant and occupied spaces, and stores the results in a database.

[1185] Step 3:

[1186] The user (driver) launches the smartphone app and obtains their current location information. The device (smartphone app) obtains the location information using its GPS function and sends it to the server.

[1187] Step 4:

[1188] The server uses a behavioral pattern analysis module to analyze the user's behavioral patterns based on the user's current location information, past parking history, and schedule information, and predicts the user's next parking needs.

[1189] Step 5:

[1190] The server's emotion engine receives the user's facial recognition data and voice data from the smartphone app and analyzes it to recognize the user's emotions in real time, for example, determining whether the user is tired, angry, or relaxed.

[1191] Step 6:

[1192] The server's parking lot information providing means calculates the optimal parking lot information and fees for the user based on information obtained from the video analysis means, location information acquisition means, behavior pattern analysis means, and emotion engine. Taking into account the user's emotional state, it can suggest, for example, a nearby and safe parking lot to a tired user, and a slightly further but cheaper parking lot to a relaxed user.

[1193] Step 7:

[1194] The fee optimization module of the server dynamically calculates the optimal fee based on the location and usage of the parking lot, and reflects the result in the parking lot information providing means.

[1195] Step 8:

[1196] The server's notification method notifies the user's device (smartphone app) in real time of the optimal parking information and fees. If the user is driving, the notification is provided through a voice guidance function.

[1197] Step 9:

[1198] The user (driver) checks the parking lot information and fee information provided through the smartphone app and selects a parking lot. The device (smartphone app) then sends the selected parking lot information to the server.

[1199] Step 10:

[1200] The server generates navigation information to the selected parking lot and sends it to the device (smartphone app).

[1201] Step 11:

[1202] The device (smartphone app) guides the user to the parking lot based on the navigation information received from the server. The user (driver) follows the navigation on the smartphone app to head to the parking lot.

[1203] Step 12:

[1204] Parking lot owners have access to a cloud-based management dashboard that provides real-time access to parking lot usage and revenue data.

[1205] Step 13:

[1206] The server receives the owner's pricing and promotion information provided through the management dashboard, and recalculates the price optimization module based on it, resulting in new pricing information being provided to the user.

[1207] Example 2

[1208] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1209] While existing parking information systems provide information on parking availability and fees, they have limitations in suggesting optimal parking spaces that take into account the user's behavioral patterns and emotions. Furthermore, they lack support for selecting parking spaces that reflects the user's emotional state when operating the app and reduces stress. For these reasons, there was a need for the development of a new system that would improve user convenience while maximizing parking lot owners' profits.

[1210] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1211] In this invention, the server includes a video analysis means for analyzing the availability of parking spaces, a location information acquisition means for acquiring the user's location information and behavioral patterns, and an emotion engine for recognizing the user's emotions, making it possible to propose optimal parking information and fees taking into account the user's current location, behavioral patterns, and emotional state.

[1212] The "video analysis means" is a means for analyzing video data obtained from security cameras and the like, and identifying vacant and occupied spaces within a parking lot.

[1213] "Location information acquisition means" refers to a means of acquiring the user's current location information using a smartphone or GPS device and transmitting that information to a server.

[1214] The "behavioral pattern analysis means" is a means that uses data analysis and modeling technology to predict a user's next parking needs based on their past parking history and schedule information.

[1215] The "emotion engine" is a means of analyzing the user's facial expressions and tone of voice to recognize their emotional state in real time. It uses facial recognition and voice analysis technologies.

[1216] The "parking lot information providing means" is a means that integrates information obtained from the video analysis means, location information acquisition means, behavioral pattern analysis means, and emotion engine, and proposes optimal parking lot information and fees to users.

[1217] The "notification means" is a means by which the server notifies the user's device, such as a smartphone app or tablet, of the optimal parking information and fees obtained from the parking information providing means.

[1218] The "navigation means" is a means for calculating a route to a parking lot selected by a user based on the notified parking lot information and providing real-time navigation.

[1219] The present invention is a system that analyzes parking availability and provides users with optimal parking information and fees. This system includes a video analysis unit, a location information acquisition unit, a behavioral pattern analysis unit, an emotion engine, a parking information provision unit, a notification unit, and a navigation unit.

[1220] Server-side implementation

[1221] 1. Video analysis methods

[1222] The server receives video data from security cameras and uses a video analysis module to identify vacant parking spaces. This analysis is performed using image processing libraries such as OpenCV. The analysis results are stored in a database.

[1223] 2. Location information acquisition means

[1224] The server uses the smartphone's GPS function to obtain the user's current location information in real time. The device encrypts the location information using the HTTPS protocol and sends it to the server.

[1225] 3. Behavioral pattern analysis methods

[1226] The server retrieves the user's past parking history and schedule information from the database, analyzes it using a behavioral pattern analysis module, and predicts the next parking need using a machine learning model (e.g., Scikit-Learn's clustering algorithm).

[1227] 4. Emotion Engine

[1228] The server uses an emotion engine to recognize emotions by analyzing the user's facial expressions and tone of voice, using Microsoft Azure's facial recognition API and voice analysis technology, for example.

[1229] 5. Means of providing parking information

[1230] The server calculates the optimal parking information and fees using a fee optimization module (using, for example, NumPy) based on the information obtained from the video analysis means, location information acquisition means, behavioral pattern analysis means, and emotion engine, and sends the information to the notification means.

[1231] 6. Means of notification

[1232] The server notifies the user's device, such as a smartphone app or tablet, of the calculated parking information and fees in real time. The notification also has a voice guidance function, so it can be used by users who are driving. The voice notification uses the Google Text-to-Speech API.

[1233] 7. Navigation Methods

[1234] The server calculates the route to the selected parking lot using a map API (e.g., Google Maps API) and generates navigation information. The information is sent to the device, which displays the route on a map in real time and provides voice guidance.

[1235] Specific examples of usage scenarios

[1236] When a driver uses a parking lot

[1237] 1. User (Driver)

[1238] Launch the smartphone app and request "Find the nearest available parking space." The app will automatically obtain your current location and send it to the server.

[1239] 2. Server

[1240] The system analyzes security camera footage to identify available parking spaces, then selects the most suitable parking spot based on the acquired location information and the user's behavioral patterns and emotional state, and notifies the user of the calculated information and fees.

[1241] 3. Device (smartphone app)

[1242] Users will receive notifications and be shown information about available parking spaces and fees. If they are driving, voice guidance will be activated, telling them, "The nearest parking lot is 300 meters ahead on the right." Once the user selects a parking spot, navigation will begin.

[1243] 4. Navigation Methods

[1244] The system calculates the route to the selected parking lot and sends navigation information to the device, which then displays the route on a map and provides voice guidance such as "Turn right at the next intersection."

[1245] When the parking lot owner manages the system

[1246] 1. User (parking lot owner)

[1247] Access your cloud-based management dashboard and click "Check usage."

[1248] 2. Server

[1249] The data collected in real time is reflected in the dashboard, displaying usage and revenue data.

[1250] 3. User (parking lot owner)

[1251] Use the dashboard to set up pricing and promotions, then save the new settings to the server.

[1252] 4. Server

[1253] The new pricing is reflected in the pricing optimization module, dynamically adjusting parking fees.

[1254] Prompt Sentence Examples

[1255] "Please tell me the nearest available parking lot from my current location."

[1256] "Please set up special parking rates for this weekend's event."

[1257] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1258] Processing Steps

[1259] Step 1: Obtaining and sending location information

[1260] 1. User: Launches the smartphone app and requests to "find the nearest available parking space."

[1261] 2. Device: Uses the smartphone's GPS function to obtain current location information.

[1262] 3. Device: The acquired location information is encrypted using the HTTPS protocol and sent to the server.

[1263] Input: User requests, smartphone GPS data.

[1264] Output: Your location information is encrypted and sent to the server.

[1265] Step 2: Identifying parking availability through video analysis

[1266] 1. Server: Receives video data in real time from security cameras installed in the parking lot.

[1267] 2. Server: Uses a video analytics module (e.g., OpenCV) to determine the availability of parking spaces, specifically identifying the presence or absence of a vehicle and identifying vacant and occupied spaces.

[1268] 3. Server: Stores the analysis results in a database.

[1269] Input: Real-time video data from security cameras.

[1270] Output: Availability information stored in a database.

[1271] Step 3: Analyze behavioral patterns

[1272] 1. Server: Obtains the user's past parking history and schedule information from the database.

[1273] 2. Server: Analyzes user behavior patterns using a behavioral pattern analysis module (e.g., Scikit-Learn clustering algorithm).

[1274] 3. Server: Save the results of predicting the next parking need in a database.

[1275] Input: Historical parking history and schedule information retrieved from the database.

[1276] Output: Behavioral patterns and parking needs prediction results stored in a database.

[1277] Step 4: Emotion Recognition

[1278] 1. Device: Using the smartphone's camera and microphone, the user's facial expressions and tone of voice are captured in real time.

[1279] 2. Terminal: Sends the acquired data to the server.

[1280] 3. Server: Uses an emotion engine (e.g., Microsoft Azure facial recognition API) to recognize the user's emotions from facial expressions and tone of voice.

[1281] Input: Facial expression data and voice data obtained from the smartphone camera and microphone.

[1282] Output: User emotion data recognized by the server.

[1283] Step 5: Calculate the best parking information

[1284] 1. Server: Integrates information obtained from video analysis, location information acquisition, behavioral pattern analysis, and emotion engine.

[1285] 2. Server: Calculates optimal parking information and fees using a fee optimization module (e.g., NumPy).

[1286] 3. Server: Sends the calculated parking information and fees to the notification means.

[1287] Input: Availability information from video analysis, location information, behavioral pattern analysis results, emotion recognition results.

[1288] Output: Optimal parking information and pricing information.

[1289] Step 6: Parking information notification

[1290] 1. Server: Calculated parking information and fees are sent in real time to user devices such as smartphone apps and tablets.

[1291] 2. Device: Receives notifications and displays information on the screen. If you are driving, voice guidance is enabled.

[1292] Input: Best parking information and prices from the server.

[1293] Output: Notifications to user terminals, screen displays and voice guidance.

[1294] Step 7: Providing navigation

[1295] 1. Server: Calculate the route to the selected parking lot using a map API (e.g., Google Maps API).

[1296] 2. Server: Sends navigation information to the device.

[1297] 3. Terminal: Receives navigation information sent from the server and displays the route on a map in real time. Voice guidance is also provided simultaneously.

[1298] Input: Best parking information, map data.

[1299] Output: Navigation information, map display and voice guidance.

[1300] (Application example 2)

[1301] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1302] Autonomous vehicles are required to quickly and accurately grasp the availability of parking spaces, and to select and guide the optimal parking space by taking into account the driver's location, past behavioral patterns, and emotional state. However, current technology does not have a system that comprehensively considers these factors, which makes parking space selection inconvenient and does not sufficiently reduce stress. Furthermore, after a parking space is selected, navigation must also provide appropriate guidance in real time, but this has not been fully realized.

[1303] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1304] In this invention, the server includes an image analysis means for analyzing the availability of parking spaces, a location information acquisition means for acquiring user location information and behavioral patterns, an emotion analysis means for analyzing user emotions in real time, a parking information provision means for proposing optimal parking information and fees to the user based on information acquired from the analysis means, the location information acquisition means, and the emotion analysis means, a notification means for notifying the user of information from the parking information provision means to the user's terminal, and a navigation means for navigating to the selected parking space. This enables quick and accurate understanding of parking space availability, selection of a parking space taking into account the driver's emotional state, and appropriate navigation in real time.

[1305] The "video analysis means for analyzing parking space availability" is a device that analyzes video data acquired from cameras installed in the parking lot and identifies vacant and occupied parking spaces.

[1306] "Location information acquisition means for acquiring user location information and behavioral patterns" refers to a device that acquires the user's current location information using technology such as GPS, and analyzes behavioral patterns based on past parking history and schedule information.

[1307] The "emotion analysis means" is a device that analyzes the user's facial expressions and voice to analyze their emotional state in real time.

[1308] The "parking lot information providing means" is a device that calculates the most suitable parking lot information and fees for the user based on the information obtained from the video analysis means, location information acquisition means, and emotion analysis means.

[1309] The "notification means" is a device that notifies the user's terminal of the information obtained by the parking lot information providing means in real time.

[1310] The "navigation means" is a device that calculates the optimal route to the selected parking lot and provides guidance to the user in real time.

[1311] The present invention uses hardware and software to realize the following system configuration and processing flow: This system provides optimal parking lot selection and navigation specifically for autonomous vehicles.

[1312] The server drives various modules to achieve the following main functions:

[1313] 1. Video analysis methods

[1314] The server receives video data in real time from cameras installed inside the autonomous vehicles. The video analytics module uses image processing libraries such as OpenCV to analyze the video data and determine the availability of parking spaces. This identifies vacant and occupied parking spaces and stores this information in a database.

[1315] 2. Location information acquisition means

[1316] The server uses the GPS module to obtain the vehicle's current location information, which then transmits the user's location information to the server in real time.

[1317] 3. Behavioral pattern analysis methods

[1318] The server uses a behavioral pattern analysis module to analyze the user's behavioral patterns based on their past parking history and schedule information, which then predicts their next parking needs and stores future parking patterns in a database.

[1319] 4. Emotion analysis method

[1320] The server uses a camera and microphone to analyze the user's facial expressions and tone of voice. It uses an emotion analysis engine (such as IBM Watson Tone Analyzer) to recognize the user's emotions in real time. It then uses facial expression analysis and voice analysis technologies to quantify the user's emotional state.

[1321] 5. Means of providing parking information

[1322] The server calculates the optimal parking information and fees for the user based on information obtained from video analysis, location information acquisition, behavioral pattern analysis, and emotion analysis.The optimal fee is calculated dynamically, taking into account the location and usage conditions of the parking lot, as well as the user's emotions identified by the emotion engine.

[1323] 6. Means of notification

[1324] The server displays the best parking information and prices on the vehicle's display, providing real-time notifications and, if the driver is driving, providing information via the voice guidance system, allowing the driver to get the information they need without having to look away.

[1325] 7. Navigation Methods

[1326] The server automatically calculates the route to the selected parking lot and provides real-time navigation, linking with the vehicle's navigation system to provide optimal route guidance.

[1327] This will enable quick and accurate understanding of parking availability, parking lot selection that takes into account the user's emotional state, and appropriate navigation in real time.

[1328] Example: A vehicle camera detects available parking spaces in real time, and the user's current location is identified by GPS. Based on past parking patterns, frequently used parking areas are prioritized. Furthermore, if the user is feeling stressed, emotion analysis can be used to suggest a suitable parking space for the user.

[1329] Example prompts for generative AI models

[1330] Design a system for autonomous vehicles that analyzes parking availability in real time, suggests the best parking spot and its price based on the user's location, behavioral patterns, and emotions, and then navigates to the parking spot.

[1331] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1332] Step 1:

[1333] The server receives video data in real time from cameras installed inside the vehicles. The video data acquired from the cameras is used as input and analyzed using image processing libraries such as OpenCV. After analyzing the video, vacant and occupied parking spaces are identified and the information is stored in a database. This allows the parking lot availability to be updated in real time.

[1334] Input: Video data from the camera

[1335] Data processing: Video analysis using OpenCV

[1336] Output: Information about identified vacant parking spaces

[1337] Step 2:

[1338] The server obtains the current location information from the GPS module installed in the vehicle. It receives the current location information as input and sends it to the server. The location information is stored in a database and used for subsequent processing.

[1339] Input: Location information from GPS module

[1340] Data processing: Acquiring and storing location information

[1341] Output: Current location information stored in a database

[1342] Step 3:

[1343] The server analyzes behavioral patterns based on past parking history and schedule information. It obtains past history and schedule information from the database and uses this as input. It uses an analysis module such as Python to predict next parking needs and saves the analysis results in the database.

[1344] Input: Past parking history and schedule information

[1345] Data processing: Behavioral pattern analysis

[1346] Output: Analysis results stored in a database

[1347] Step 4:

[1348] The server uses a camera and microphone to collect the user's facial expressions and tone of voice. This data is used as input to analyze emotions in real time using an emotion analysis engine (such as IBM Watson Tone Analyzer). The analysis results are stored in a database.

[1349] Input: facial and voice data from camera and microphone

[1350] Data processing: Analysis using a sentiment analysis engine

[1351] Output: Sentiment analysis results stored in a database

[1352] Step 5:

[1353] The server integrates information obtained from the video analysis means, location information acquisition means, behavioral pattern analysis means, and emotion analysis means. Using this data as input, it calculates the optimal parking information and fees for the user. Based on the parking information and user information in the database, the fee optimization module dynamically calculates the optimal fee.

[1354] Input: Results of video analysis, location information, behavioral pattern analysis, and emotion analysis

[1355] Data processing: Integration of various information and calculation of optimal pricing

[1356] Output: Optimal parking information and prices

[1357] Step 6:

[1358] The server then notifies the vehicle's display and voice guidance system of the calculated optimal parking information and fees, allowing users to receive the necessary information visually and audibly.

[1359] Input: Best parking information and prices

[1360] Data processing: Creating notification messages

[1361] Output: Notification to vehicle display and voice guidance system

[1362] Step 7:

[1363] The server automatically calculates the optimal route to the selected parking lot and provides real-time navigation, linking with the vehicle's navigation system to provide directions to help users smoothly reach their destination.

[1364] Input: Selected parking information

[1365] Data processing: Calculation of optimal route using route calculation algorithm

[1366] Output: Instructions to the vehicle's navigation system

[1367] 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.

[1368] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1369] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1370] [Fourth embodiment]

[1371] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1372] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1373] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[1374] 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.

[1375] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1376] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1377] 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.

[1378] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1379] 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.

[1380] 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 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.

[1381] 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.

[1382] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1383] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1384] The present invention provides a system that includes an image analysis means for analyzing the availability of parking spaces, a location information acquisition means for acquiring user location information and behavioral patterns, a parking information provision means for proposing optimal parking information and fees to the user based on information obtained from the analysis means and location information acquisition means, a notification means for notifying the user of information from the parking information provision means to their terminal, and a navigation means for navigating to the selected parking space based on information from the notification means.

[1385] Program processing details

[1386] Server-side processing

[1387] 1. Video analysis methods

[1388] The server receives video data in real time from security cameras installed in the parking lot.

[1389] The video analytics module analyzes the video data to determine the availability of parking spaces, thereby identifying vacant and occupied spaces and storing this information in a database.

[1390] 2. Location information acquisition means

[1391] The server uses the smartphone's GPS function to obtain the user's current location information, which is then sent to the server in real time.

[1392] 3. Behavioral pattern analysis methods

[1393] The server uses a behavioral pattern analysis module to analyze the user's behavioral patterns based on their past parking history and schedule information, which then predicts their next parking needs and stores future parking patterns in a database.

[1394] 4. Means of providing parking information

[1395] The server calculates the most suitable parking information and fees for the user based on the information obtained from the video analysis means and location information acquisition means.

[1396] The fee optimization module dynamically calculates the optimal fee based on the parking lot's location and usage.

[1397] 5. Means of notification

[1398] The server notifies the user of the optimal parking information and fees via a smartphone app or tablet.

[1399] Notifications are provided in real time, and if the user is driving, information is provided through a voice prompt function.

[1400] 6. Navigation Methods

[1401] The server provides a navigation function based on the notified parking lot information.

[1402] The route to the parking lot selected by the user is calculated and navigation is performed in real time.

[1403] Specific usage scenarios

[1404] When a driver uses a parking lot

[1405] 1. User (Driver)

[1406] Users launch the smartphone app and search for parking spaces. The app automatically obtains their current location and requests information about the nearest parking spaces.

[1407] 2. Server

[1408] The server receives video data from security cameras, and the video analysis module identifies available parking spaces.

[1409] The server analyzes the user's location information and behavioral patterns to select the most suitable parking space.

[1410] The server transmits the selected parking lot information and the dynamically calculated fee to the user's terminal via the notification means.

[1411] 3. Device (smartphone app)

[1412] The user's smartphone app receives notifications from the server and displays information about available parking spaces and fees on the screen. If the user is driving, the voice guidance function is activated.

[1413] Once the user selects a suggested parking spot, the app sends the information to the server and begins navigation.

[1414] 4. Navigation Methods

[1415] The server sends navigation information to the selected parking lot to the device and guides the user through the app.

[1416] When the parking lot owner manages the system

[1417] 1. User (parking lot owner)

[1418] Parking lot owners have access to a cloud-based management dashboard to view parking lot usage and revenue data.

[1419] 2. Server

[1420] The server collects data in real time and displays it on a dashboard, displaying the information in a format that is easy for the owner to manage.

[1421] 3. User (parking lot owner)

[1422] Owners set pricing and promotions through the dashboard, and the settings information is stored on the server.

[1423] The server runs a fee optimization module based on the new fee settings and dynamically adjusts the parking fee.

[1424] This allows users to easily find parking spaces and use them efficiently, while allowing parking lot owners to maximize their parking lot revenue.

[1425] The processing flow will be explained below.

[1426] Step 1:

[1427] The server receives video data in real time from security cameras installed in the parking lot.

[1428] Step 2:

[1429] The server's video analysis module analyzes the availability of parking spaces based on the received video data, identifies vacant and occupied spaces, and stores the results in a database.

[1430] Step 3:

[1431] The user (driver) launches the smartphone app and obtains their current location information. The device (smartphone app) obtains the location information using its GPS function and sends it to the server.

[1432] Step 4:

[1433] The server uses a behavioral pattern analysis module to analyze the user's behavioral patterns based on the user's current location information, past parking history, and schedule information, and predicts the user's next parking needs.

[1434] Step 5:

[1435] The parking lot information providing means of the server calculates the most suitable parking lot information and fees for the user based on the information obtained from the video analysis means and the location information acquisition means.

[1436] Step 6:

[1437] The fee optimization module of the server dynamically calculates the optimal fee based on the location and usage of the parking lot, and reflects the result in the parking lot information providing means.

[1438] Step 7:

[1439] The server's notification means notifies the user's device (smartphone app) in real time of the optimal parking information and fees.

[1440] Step 8:

[1441] The user (driver) checks the parking lot information and fee information provided through the smartphone app and selects a parking lot. The device (smartphone app) then sends the selected parking lot information to the server.

[1442] Step 9:

[1443] The server generates navigation information to the selected parking lot and sends it to the device (smartphone app).

[1444] Step 10:

[1445] The device (smartphone app) guides the user to the parking lot based on the navigation information received from the server. The user (driver) follows the navigation on the smartphone app to head to the parking lot.

[1446] Step 11:

[1447] Parking lot owners have access to a cloud-based management dashboard that provides real-time access to parking lot usage and revenue data.

[1448] Step 12:

[1449] The server receives the owner's pricing and promotion information provided through the management dashboard, and recalculates the price optimization module based on it, resulting in new pricing information being provided to the user.

[1450] Example 1

[1451] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1452] Real-time video analysis and analysis of user behavior patterns are necessary to efficiently grasp parking availability and propose appropriate parking information and fees to users. However, if these functions are insufficient, it becomes difficult to provide appropriate parking information, which causes trouble and stress for users. Furthermore, providing only visual information while driving can be dangerous, so safe information provision via audio guidance is also required.

[1453] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1454] In this invention, the server includes: a video analysis means for analyzing the availability of parking spaces; a location information acquisition means for acquiring user location information and behavioral patterns; a parking information provision means for proposing optimal parking information and fees to the user based on information acquired from the analysis means and the location information acquisition means; a notification means for notifying the user of information from the parking information provision means to the user's terminal; a navigation means for navigating to the selected parking space based on information from the notification means; a server including a video analysis algorithm for determining the availability of parking spaces using a video analysis module; a server including a behavior pattern analysis module for analyzing behavioral patterns based on the user's past parking history and schedule information; a terminal equipped with a voice guidance function for providing parking information via voice guidance while the vehicle is being driven; and a server equipped with a navigation function for providing navigation information in real time. This makes it possible to efficiently analyze available parking spaces in real time and safely and efficiently provide optimal parking information and navigation to the user.

[1455] The "video analysis means for analyzing parking space availability" is a device or module that analyzes video data acquired from cameras installed in the parking lot and identifies vacant and occupied parking spaces in real time.

[1456] "Location information acquisition means for acquiring user location information and behavioral patterns" refers to a device or system that uses a smartphone or other GPS device to collect a user's current location information and past behavioral history.

[1457] The "parking lot information providing means" is a device or system that calculates and presents optimal parking lot information and fees to users based on information obtained from the video analysis means and location information acquisition means.

[1458] The "notification means" is a device or system that notifies the user of calculated parking information and fees in real time via their smartphone app or tablet.

[1459] The "navigation means" is a device or system that provides real-time guidance on routes to a selected parking lot based on the information provided by the notification means.

[1460] "Video analysis algorithms" are image processing technologies and machine learning models used to analyze acquired video data and determine the availability of parking spaces.

[1461] The "behavior pattern analysis module" is a device or software module for predicting a user's parking behavior based on the user's past parking history and schedule information.

[1462] A "voice guidance function" is a device or system that conveys information through voice to safely provide parking information and navigation information while driving a vehicle.

[1463] The "navigation function that provides real-time navigation information" is a function that calculates the route to the parking lot selected by the user in real time and displays and provides voice guidance on the user's smartphone app.

[1464] The present invention is a system that includes an image analysis means for analyzing parking availability, a location information acquisition means for acquiring user location information and behavioral patterns, a parking information provision means for proposing optimal parking information and fees to users based on that information, a notification means for notifying the user of parking information to their terminal, and a navigation means for navigating to the selected parking lot based on that information.

[1465] System configuration

[1466] 1. Video analysis methods

[1467] The server uses video reception technologies such as RTSP (Real-Time Streaming Protocol) to receive video data in real time from security cameras installed in the parking lot.

[1468] The server uses a video analysis module (e.g., OpenCV or TensorFlow) to analyze the availability of parking spaces based on the captured video data, thereby identifying vacant and occupied spaces and storing the results in a database.

[1469] 2. Location information acquisition means

[1470] The device (smartphone) uses its GPS function to obtain the user's current location information. When the user launches the application, the current location information is periodically sent to the server.

[1471] 3. Behavioral pattern analysis methods

[1472] The server collects users' past parking history and schedule information, and uses a behavioral pattern analysis module to predict their next parking needs. The analysis results are stored in a database.

[1473] 4. Means of providing parking information

[1474] The server integrates data obtained from the video analysis and location information acquisition means to calculate the optimal parking information and fees for the user. The fee optimization module dynamically provides the optimal fee, taking into account real-time usage and the location of the parking lot.

[1475] 5. Means of notification

[1476] The server notifies users of the best parking information and fees via a smartphone app or tablet. For example, it sends real-time information about available parking spaces via push notifications or SMS. It also provides information safely to drivers through a voice guidance function.

[1477] 6. Navigation Methods

[1478] The server calculates the route to the parking lot selected by the user and sends the navigation information to the device, which uses a dedicated app to display the route on a map and provide real-time voice guidance.

[1479] Specific usage scenarios

[1480] When a driver uses a parking lot

[1481] The user (driver) starts the smartphone app, which acquires the current location information and searches for the nearest parking lot information. The app then sends a request to the server.

[1482] The server receives video data from security cameras, and the video analysis module identifies available parking spaces. It also analyzes the user's location and behavioral patterns to calculate the optimal parking information and fees.

[1483] The server notifies the user of the selected parking lot information and fees via a smartphone app. If the user is driving, the information is provided via the device's voice guidance function.

[1484] When the user (driver) selects a suggested parking lot, the server calculates navigation information based on that information and sends it to the terminal.

[1485] When the parking lot owner manages the system

[1486] Users (parking lot owners) access a cloud-based management dashboard to view parking lot usage and revenue data.

[1487] The server then feeds the collected data into a dashboard in real time, displaying revenue data and usage in an easy-to-manage format.

[1488] Prompt Sentence Examples

[1489] "I want to use the feature in my app that notifies me of parking availability and best parking options. How can I do this?"

[1490] The system allows users to find parking spaces efficiently and parking lot owners to maximize parking lot utilization and revenue.

[1491] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1492] Step 1:

[1493] Video data reception

[1494] The server receives real-time video data from security cameras installed in the parking lot, which then transmit the video data to the server via the network using RTSP (Real-Time Streaming Protocol).

[1495] Input: Video stream from a security camera.

[1496] Output: Raw video data stored on the server.

[1497] Step 2:

[1498] Video Analysis

[1499] The server passes the received video data to a video analysis module, which uses image processing algorithms (such as OpenCV or TensorFlow) to identify vacant parking spaces in real time. The analysis results are used to generate data indicating the availability of each parking space.

[1500] Input: Raw video data.

[1501] Output: Information about available parking spaces (stored in a database).

[1502] Step 3:

[1503] Location information acquisition

[1504] When a user launches an application, the device (smartphone) uses its GPS function to obtain current location information. The application then periodically sends this location information to a server.

[1505] Input: GPS data from your smartphone.

[1506] Output: Real-time location information transmitted to the server.

[1507] Step 4:

[1508] Behavioral pattern analysis

[1509] The server collects users' past parking history and schedule information, analyzes this data using a behavioral pattern analysis module, and stores the analysis results in a database as information to predict future parking needs.

[1510] Input: User's past parking history, schedule information.

[1511] Output: Predicted user parking needs (stored in database).

[1512] Step 5:

[1513] Parking information provided

[1514] The server integrates the data obtained from the video analysis means and the location information acquisition means, and calculates the optimal parking information and fees using the fee optimization module. The fee is dynamically determined taking into account real-time parking lot usage and location conditions.

[1515] Input: available parking space information, user location information, predicted parking needs.

[1516] Output: Optimal parking information and prices (passed to notification means).

[1517] Step 6:

[1518] Information Notification

[1519] The server then sends calculated parking information and fees to the user's smartphone app or tablet, with real-time updates and voice guidance enabled if the user is driving.

[1520] Input: Best parking information and prices.

[1521] Output: The notification message sent to the user's device.

[1522] Step 7:

[1523] Navigation provided

[1524] The server calculates navigation information to the parking lot selected by the user and sends it to the device via a dedicated app. The smartphone displays the route on a map and provides real-time voice guidance.

[1525] Input: Location information of selected parking lot, user's current location information.

[1526] Output: Navigation information (displayed on the user's device and voice guidance).

[1527] In this way, the system can efficiently analyze parking availability in real time and provide users with optimal parking information and navigation.

[1528] (Application example 1)

[1529] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1530] As autonomous vehicles become more widespread, there is a demand for systems that automatically find parking spaces and park at appropriate fees, without the driver having to search for a parking space themselves. However, current systems lack the means to analyze parking availability in real time, and do not optimize parking fees or suggest parking spaces based on behavioral patterns. Furthermore, there is no integrated system available for autonomous vehicles to navigate to a selected parking space. To address these challenges, a system combining advanced video analysis technology, location information acquisition technology, and behavioral pattern analysis technology is needed.

[1531] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1532] In this invention, the server includes an image analysis means for analyzing the availability of parking spaces, a location information acquisition means for acquiring user location information and behavioral patterns, a parking information provision means for proposing optimal parking information and fees to the user based on information acquired from the analysis means and the location information acquisition means, a notification means for notifying the user's terminal of information from the parking information provision means, and a navigation means for navigating the autonomous vehicle to the selected parking space based on information from the notification means. This allows the user to obtain optimal parking information and fees in real time, and enables the autonomous vehicle to smoothly navigate to the selected parking space.

[1533] The "video analysis means" is a means for analyzing video data acquired from a monitoring device installed in a parking lot and determining the availability and occupancy status of the parking lot in real time.

[1534] "Location information acquisition means" refers to a means of collecting location data in real time using technologies such as GPS in order to acquire user location information and behavioral patterns.

[1535] The "parking lot information providing means" is a means for proposing optimal parking lot information and fees to users based on information obtained from the video analysis means and location information acquisition means.

[1536] The "notification means" is a means for notifying the user's terminal of the information obtained by the parking lot information providing means, and may be email, a smartphone app, voice guidance, or the like.

[1537] The "navigation means" is a means for calculating a route to a parking lot selected by a user based on information obtained from the notification means, and guiding an autonomous vehicle to that parking lot.

[1538] A "surveillance camera" is a device that captures the situation in a parking lot as video data in real time.

[1539] An "autonomous vehicle" refers to a vehicle that can drive and park automatically.

[1540] "Behavioral patterns" are predictive data on a user's next parking needs and behavior, analyzed based on their past parking history, schedule information, etc.

[1541] "Optimal parking information" is information on the most convenient parking lot, selected based on the user's location and behavioral patterns.

[1542] "Fee" refers to the cost for using a parking lot, and is dynamically set based on factors such as parking time and the location of the parking lot.

[1543] The present invention relates to a parking assistance system for autonomous vehicles, and in particular to a system that analyzes parking lot availability in real time and provides users with optimal parking lot information and fees. This system includes an image analysis means for analyzing parking lot availability, a location information acquisition means for acquiring user location information and behavior patterns, a parking lot information provision means for proposing optimal parking lot information and fees based on information obtained from the analysis means and the location information acquisition means, a notification means for notifying the user's terminal of information from the parking lot information provision means, and a navigation means for navigating the autonomous vehicle to a selected parking lot based on information from the notification means.

[1544] The specific configuration of the system will be explained below. The system's video analysis means acquires video data using surveillance cameras installed in parking lots and analyzes the parking lot availability in real time. This identifies vacant and occupied parking spaces and stores this information in a database.

[1545] The location information acquisition means acquires location information using the GPS function of the user's smartphone or vehicle, allowing the user's current location to be grasped in real time. In addition, the behavior pattern analysis module analyzes the user's behavior patterns based on their past parking history and schedule information, and predicts their next parking needs.

[1546] The parking lot information providing means proposes optimal parking lot information and fees to the user based on information obtained from the video analysis means and location information acquisition means. Fees are dynamically set based on the location and usage status of the parking lot. The proposed parking lot information and fees are notified to the user's device in real time via the notification means.

[1547] The navigation means navigates the autonomous vehicle to the selected parking lot based on the notified parking lot information. This navigation is performed using a map service such as Google Maps API.

[1548] As a concrete example, consider a self-driving vehicle arriving in a city center. It can automatically find the best parking spot, calculate the cost, and provide route guidance. The process goes like this:

[1549] 1. When an autonomous vehicle arrives in a city center, the system analyzes available parking spaces in real time based on video data obtained from surveillance cameras.

[1550] 2. At the same time, the vehicle's GPS function is used to obtain current location information.

[1551] 3. Based on the user's past parking history and schedule information, behavioral patterns are analyzed and their next parking needs are predicted.

[1552] 4. Calculate the optimal parking information and fees and notify the user's device.

[1553] 5. After receiving the notification, the user selects the suggested parking lot through the app, and the system uses that information to calculate and navigate the route to the parking lot.

[1554] An example of a prompt is, "When you arrive in the city center, automatically analyze available parking spaces and begin navigation to the best available parking space." This allows users to find parking spaces without stress and park their self-driving vehicles smoothly.

[1555] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1556] Step 1:

[1557] The server acquires video data from surveillance cameras installed in the parking lot, analyzes the video data using a video analysis means, and identifies vacant and occupied parking spaces. The analysis results are stored in a database.

[1558] Input: Video data from a surveillance camera

[1559] Output: Information on vacant and occupied parking spaces

[1560] Specific operation: After receiving real-time video data captured by the surveillance camera, the system uses video analysis technology such as OpenCV to determine the status of the parking space. The analysis results are stored in a database and retained for use in the next step.

[1561] Step 2:

[1562] The server obtains the user's current location via the GPS function of the user's smartphone or vehicle, and this location information is sent to the server in real time.

[1563] Input: GPS data from the user's smartphone or vehicle

[1564] Output: User's current location information

[1565] How it works: Smartphones and autonomous vehicles collect GPS data and send it to a server, which receives this location information and stores it in a database in real time.

[1566] Step 3:

[1567] The server analyzes the user's behavioral patterns based on their past parking history and schedule information, and uses the results of this analysis to predict their next parking needs.

[1568] Input: User's past parking history, schedule information

[1569] Output: Predicted data for next parking needs

[1570] How it works: The server references historical databases and uses machine learning algorithms to analyze user behavior patterns, which then predicts upcoming parking needs and generates data on future parking needs.

[1571] Step 4:

[1572] The server integrates the information obtained from the video analysis and location information acquisition means to provide users with optimal parking information and fees. Fees are dynamically set based on the location and usage of the parking lot.

[1573] Input: Information on available parking spaces, current location, behavioral pattern analysis results

[1574] Output: Optimal parking information and prices

[1575] How it works: The server integrates the collected data and uses an algorithm to select the most suitable parking spot. The fee optimization module then dynamically sets fees based on parking spot usage and location.

[1576] Step 5:

[1577] The server notifies the user's device of the calculated optimal parking information and fees using a notification method. This notification is done in real time, and if the user is driving, the information is provided through a voice guidance function.

[1578] Input: Best parking information, fee information

[1579] Output: Notification to the user's device

[1580] How it works: The server generates a notification message and sends it to the user's smartphone or tablet. If the user is driving, the information is provided as voice guidance.

[1581] Step 6:

[1582] Based on the parking information notified to the user, the route to the selected parking lot is calculated and the autonomous vehicle is navigated. Navigation is provided in real time using the navigation means.

[1583] Input: Selected parking lot information, current location information

[1584] Output: Navigation information to the parking lot

[1585] How it works: The server uses map services such as Google Maps API to calculate the optimal route from the current location to the selected parking lot, then sends the navigation information to the autonomous vehicle to guide it to the parking lot.

[1586] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1587] The present invention is a system that includes an image analysis means for analyzing the availability of parking spaces, a location information acquisition means for acquiring user location information and behavioral patterns, a parking information provision means for proposing optimal parking information and fees to the user based on information acquired from the analysis means and the location information acquisition means, a notification means for notifying the user of information from the parking information provision means to the user's terminal, a navigation means for navigating to the selected parking space based on information from the notification means, and an emotion engine for recognizing the user's emotions.

[1588] Program processing details

[1589] Server-side processing

[1590] 1. Video analysis methods

[1591] The server receives video data in real time from security cameras installed in the parking lot.

[1592] The video analytics module analyzes the video data to determine the availability of parking spaces, thereby identifying vacant and occupied spaces and storing this information in a database.

[1593] 2. Location information acquisition means

[1594] The server uses the smartphone's GPS function to obtain the user's current location information, which is then sent to the server in real time.

[1595] 3. Behavioral pattern analysis methods

[1596] The server uses a behavioral pattern analysis module to analyze the user's behavioral patterns based on their past parking history and schedule information, which then predicts their next parking needs and stores future parking patterns in a database.

[1597] 4. Emotion Engine

[1598] The server uses an emotion engine to analyze the user's facial expressions and tone of voice to recognize the user's emotions in real time. It uses facial recognition and voice analysis technology to identify the user's emotions while operating the app.

[1599] 5. Means of providing parking information

[1600] The server calculates the most suitable parking information and fees for the user based on information obtained from the video analysis means, location information acquisition means, behavioral pattern analysis means and emotion engine.

[1601] The fee optimization module dynamically calculates the optimal fee by taking into account the location and usage of the parking lot, as well as the user's emotions identified by the emotion engine.

[1602] 6. Means of notification

[1603] The server notifies the user of the optimal parking information and fees via a smartphone app or tablet.

[1604] Notifications are provided in real time, and if the user is driving, information is provided through a voice prompt function.

[1605] 7. Navigation Methods

[1606] The server provides a navigation function based on the notified parking lot information.

[1607] The route to the parking lot selected by the user is calculated and navigation is performed in real time.

[1608] Specific usage scenarios

[1609] When a driver uses a parking lot

[1610] 1. User (Driver)

[1611] Users launch the smartphone app and search for parking spaces. The app automatically obtains their current location and requests information about the nearest parking spaces.

[1612] 2. Server

[1613] The server receives video data from security cameras, and the video analysis module identifies available parking spaces.

[1614] The server analyzes the user's location information and behavioral patterns, as well as the user's emotions identified by an emotion engine, to select the most suitable parking space.

[1615] The server transmits the selected parking lot information and the dynamically calculated fee to the user's terminal via the notification means.

[1616] 3. Device (smartphone app)

[1617] The user's smartphone app receives notifications from the server and displays information about available parking spaces and fees on the screen. If the user is driving, the voice guidance function is activated.

[1618] Once the user selects a suggested parking spot, the app sends the information to the server and begins navigation.

[1619] 4. Navigation Methods

[1620] The server sends navigation information to the selected parking lot to the device and guides the user through the app.

[1621] When the parking lot owner manages the system

[1622] 1. User (parking lot owner)

[1623] Parking lot owners have access to a cloud-based management dashboard to view parking lot usage and revenue data.

[1624] 2. Server

[1625] The server collects data in real time and displays it on a dashboard, displaying the information in a format that is easy for the owner to manage.

[1626] 3. User (parking lot owner)

[1627] Owners set pricing and promotions through the dashboard, and the settings information is stored on the server.

[1628] The server runs a fee optimization module based on the new fee settings and dynamically adjusts the parking fee.

[1629] This allows users to efficiently find the best parking space based on their emotions, and parking lot owners to maximize their parking lot revenue.

[1630] The processing flow will be explained below.

[1631] Step 1:

[1632] The server receives video data in real time from security cameras installed in the parking lot.

[1633] Step 2:

[1634] The server's video analysis module analyzes the availability of parking spaces based on the received video data, identifies vacant and occupied spaces, and stores the results in a database.

[1635] Step 3:

[1636] The user (driver) launches the smartphone app and obtains their current location information. The device (smartphone app) obtains the location information using its GPS function and sends it to the server.

[1637] Step 4:

[1638] The server uses a behavioral pattern analysis module to analyze the user's behavioral patterns based on the user's current location information, past parking history, and schedule information, and predicts the user's next parking needs.

[1639] Step 5:

[1640] The server's emotion engine receives the user's facial recognition data and voice data from the smartphone app and analyzes it to recognize the user's emotions in real time, for example, determining whether the user is tired, angry, or relaxed.

[1641] Step 6:

[1642] The server's parking lot information providing means calculates the optimal parking lot information and fees for the user based on information obtained from the video analysis means, location information acquisition means, behavior pattern analysis means, and emotion engine. Taking into account the user's emotional state, it can suggest, for example, a nearby and safe parking lot to a tired user, and a slightly further but cheaper parking lot to a relaxed user.

[1643] Step 7:

[1644] The fee optimization module of the server dynamically calculates the optimal fee based on the location and usage of the parking lot, and reflects the result in the parking lot information providing means.

[1645] Step 8:

[1646] The server's notification method notifies the user's device (smartphone app) in real time of the optimal parking information and fees. If the user is driving, the notification is provided through a voice guidance function.

[1647] Step 9:

[1648] The user (driver) checks the parking lot information and fee information provided through the smartphone app and selects a parking lot. The device (smartphone app) then sends the selected parking lot information to the server.

[1649] Step 10:

[1650] The server generates navigation information to the selected parking lot and sends it to the device (smartphone app).

[1651] Step 11:

[1652] The device (smartphone app) guides the user to the parking lot based on the navigation information received from the server. The user (driver) follows the navigation on the smartphone app to head to the parking lot.

[1653] Step 12:

[1654] Parking lot owners have access to a cloud-based management dashboard that provides real-time access to parking lot usage and revenue data.

[1655] Step 13:

[1656] The server receives the owner's pricing and promotion information provided through the management dashboard, and recalculates the price optimization module based on it, resulting in new pricing information being provided to the user.

[1657] Example 2

[1658] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1659] While existing parking information systems provide information on parking availability and fees, they have limitations in suggesting optimal parking spaces that take into account the user's behavioral patterns and emotions. Furthermore, they lack support for selecting parking spaces that reflects the user's emotional state when operating the app and reduces stress. For these reasons, there was a need for the development of a new system that would improve user convenience while maximizing parking lot owners' profits.

[1660] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1661] In this invention, the server includes a video analysis means for analyzing the availability of parking spaces, a location information acquisition means for acquiring the user's location information and behavioral patterns, and an emotion engine for recognizing the user's emotions, making it possible to propose optimal parking information and fees taking into account the user's current location, behavioral patterns, and emotional state.

[1662] The "video analysis means" is a means for analyzing video data obtained from security cameras and the like, and identifying vacant and occupied spaces within a parking lot.

[1663] "Location information acquisition means" refers to a means of acquiring the user's current location information using a smartphone or GPS device and transmitting that information to a server.

[1664] The "behavioral pattern analysis means" is a means that uses data analysis and modeling technology to predict a user's next parking needs based on their past parking history and schedule information.

[1665] The "emotion engine" is a means of analyzing the user's facial expressions and tone of voice to recognize their emotional state in real time. It uses facial recognition and voice analysis technologies.

[1666] The "parking lot information providing means" is a means that integrates information obtained from the video analysis means, location information acquisition means, behavioral pattern analysis means, and emotion engine, and proposes optimal parking lot information and fees to users.

[1667] The "notification means" is a means by which the server notifies the user's device, such as a smartphone app or tablet, of the optimal parking information and fees obtained from the parking information providing means.

[1668] The "navigation means" is a means for calculating a route to a parking lot selected by a user based on the notified parking lot information and providing real-time navigation.

[1669] The present invention is a system that analyzes parking availability and provides users with optimal parking information and fees. This system includes a video analysis unit, a location information acquisition unit, a behavioral pattern analysis unit, an emotion engine, a parking information provision unit, a notification unit, and a navigation unit.

[1670] Server-side implementation

[1671] 1. Video analysis methods

[1672] The server receives video data from security cameras and uses a video analysis module to identify vacant parking spaces. This analysis is performed using image processing libraries such as OpenCV. The analysis results are stored in a database.

[1673] 2. Location information acquisition means

[1674] The server uses the smartphone's GPS function to obtain the user's current location information in real time. The device encrypts the location information using the HTTPS protocol and sends it to the server.

[1675] 3. Behavioral pattern analysis methods

[1676] The server retrieves the user's past parking history and schedule information from the database, analyzes it using a behavioral pattern analysis module, and predicts the next parking need using a machine learning model (e.g., Scikit-Learn's clustering algorithm).

[1677] 4. Emotion Engine

[1678] The server uses an emotion engine to recognize emotions by analyzing the user's facial expressions and tone of voice, using Microsoft Azure's facial recognition API and voice analysis technology, for example.

[1679] 5. Means of providing parking information

[1680] The server calculates the optimal parking information and fees using a fee optimization module (using, for example, NumPy) based on the information obtained from the video analysis means, location information acquisition means, behavioral pattern analysis means, and emotion engine, and sends the information to the notification means.

[1681] 6. Means of notification

[1682] The server notifies the user's device, such as a smartphone app or tablet, of the calculated parking information and fees in real time. The notification also has a voice guidance function, so it can be used by users who are driving. The voice notification uses the Google Text-to-Speech API.

[1683] 7. Navigation Methods

[1684] The server calculates the route to the selected parking lot using a map API (e.g., Google Maps API) and generates navigation information. The information is sent to the device, which displays the route on a map in real time and provides voice guidance.

[1685] Specific examples of usage scenarios

[1686] When a driver uses a parking lot

[1687] 1. User (Driver)

[1688] Launch the smartphone app and request "Find the nearest available parking space." The app will automatically obtain your current location and send it to the server.

[1689] 2. Server

[1690] The system analyzes security camera footage to identify available parking spaces, then selects the most suitable parking spot based on the acquired location information and the user's behavioral patterns and emotional state, and notifies the user of the calculated information and fees.

[1691] 3. Device (smartphone app)

[1692] Users will receive notifications and be shown information about available parking spaces and fees. If they are driving, voice guidance will be activated, telling them, "The nearest parking lot is 300 meters ahead on the right." Once the user selects a parking spot, navigation will begin.

[1693] 4. Navigation Methods

[1694] The system calculates the route to the selected parking lot and sends navigation information to the device, which then displays the route on a map and provides voice guidance such as "Turn right at the next intersection."

[1695] When the parking lot owner manages the system

[1696] 1. User (parking lot owner)

[1697] Access your cloud-based management dashboard and click "Check usage."

[1698] 2. Server

[1699] The data collected in real time is reflected in the dashboard, displaying usage and revenue data.

[1700] 3. User (parking lot owner)

[1701] Use the dashboard to set up pricing and promotions, then save the new settings to the server.

[1702] 4. Server

[1703] The new pricing is reflected in the pricing optimization module, dynamically adjusting parking fees.

[1704] Prompt Sentence Examples

[1705] "Please tell me the nearest available parking lot from my current location."

[1706] "Please set up special parking rates for this weekend's event."

[1707] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1708] Processing Steps

[1709] Step 1: Obtaining and sending location information

[1710] 1. User: Launches the smartphone app and requests to "find the nearest available parking space."

[1711] 2. Device: Uses the smartphone's GPS function to obtain current location information.

[1712] 3. Device: The acquired location information is encrypted using the HTTPS protocol and sent to the server.

[1713] Input: User requests, smartphone GPS data.

[1714] Output: Your location information is encrypted and sent to the server.

[1715] Step 2: Identifying parking availability through video analysis

[1716] 1. Server: Receives video data in real time from security cameras installed in the parking lot.

[1717] 2. Server: Uses a video analytics module (e.g., OpenCV) to determine the availability of parking spaces, specifically identifying the presence or absence of a vehicle and identifying vacant and occupied spaces.

[1718] 3. Server: Stores the analysis results in a database.

[1719] Input: Real-time video data from security cameras.

[1720] Output: Availability information stored in a database.

[1721] Step 3: Analyze behavioral patterns

[1722] 1. Server: Obtains the user's past parking history and schedule information from the database.

[1723] 2. Server: Analyzes user behavior patterns using a behavioral pattern analysis module (e.g., Scikit-Learn clustering algorithm).

[1724] 3. Server: Save the results of predicting the next parking need in a database.

[1725] Input: Historical parking history and schedule information retrieved from the database.

[1726] Output: Behavioral patterns and parking needs prediction results stored in a database.

[1727] Step 4: Emotion Recognition

[1728] 1. Device: Using the smartphone's camera and microphone, the user's facial expressions and tone of voice are captured in real time.

[1729] 2. Terminal: Sends the acquired data to the server.

[1730] 3. Server: Uses an emotion engine (e.g., Microsoft Azure facial recognition API) to recognize the user's emotions from facial expressions and tone of voice.

[1731] Input: Facial expression data and voice data obtained from the smartphone camera and microphone.

[1732] Output: User emotion data recognized by the server.

[1733] Step 5: Calculate the best parking information

[1734] 1. Server: Integrates information obtained from video analysis, location information acquisition, behavioral pattern analysis, and emotion engine.

[1735] 2. Server: Calculates optimal parking information and fees using a fee optimization module (e.g., NumPy).

[1736] 3. Server: Sends the calculated parking information and fees to the notification means.

[1737] Input: Availability information from video analysis, location information, behavioral pattern analysis results, emotion recognition results.

[1738] Output: Optimal parking information and pricing information.

[1739] Step 6: Parking information notification

[1740] 1. Server: Calculated parking information and fees are sent in real time to user devices such as smartphone apps and tablets.

[1741] 2. Device: Receives notifications and displays information on the screen. If you are driving, voice guidance is enabled.

[1742] Input: Best parking information and prices from the server.

[1743] Output: Notifications to user terminals, screen displays and voice guidance.

[1744] Step 7: Providing navigation

[1745] 1. Server: Calculate the route to the selected parking lot using a map API (e.g., Google Maps API).

[1746] 2. Server: Sends navigation information to the device.

[1747] 3. Terminal: Receives navigation information sent from the server and displays the route on a map in real time. Voice guidance is also provided simultaneously.

[1748] Input: Best parking information, map data.

[1749] Output: Navigation information, map display and voice guidance.

[1750] (Application example 2)

[1751] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1752] Autonomous vehicles are required to quickly and accurately grasp the availability of parking spaces, and to select and guide the optimal parking space by taking into account the driver's location, past behavioral patterns, and emotional state. However, current technology does not have a system that comprehensively considers these factors, which makes parking space selection inconvenient and does not sufficiently reduce stress. Furthermore, after a parking space is selected, navigation must also provide appropriate guidance in real time, but this has not been fully realized.

[1753] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1754] In this invention, the server includes an image analysis means for analyzing the availability of parking spaces, a location information acquisition means for acquiring user location information and behavioral patterns, an emotion analysis means for analyzing user emotions in real time, a parking information provision means for proposing optimal parking information and fees to the user based on information acquired from the analysis means, the location information acquisition means, and the emotion analysis means, a notification means for notifying the user of information from the parking information provision means to the user's terminal, and a navigation means for navigating to the selected parking space. This enables quick and accurate understanding of parking space availability, selection of a parking space taking into account the driver's emotional state, and appropriate navigation in real time.

[1755] The "video analysis means for analyzing parking space availability" is a device that analyzes video data acquired from cameras installed in the parking lot and identifies vacant and occupied parking spaces.

[1756] "Location information acquisition means for acquiring user location information and behavioral patterns" refers to a device that acquires the user's current location information using technology such as GPS, and analyzes behavioral patterns based on past parking history and schedule information.

[1757] The "emotion analysis means" is a device that analyzes the user's facial expressions and voice to analyze their emotional state in real time.

[1758] The "parking lot information providing means" is a device that calculates the most suitable parking lot information and fees for the user based on the information obtained from the video analysis means, location information acquisition means, and emotion analysis means.

[1759] The "notification means" is a device that notifies the user's terminal of the information obtained by the parking lot information providing means in real time.

[1760] The "navigation means" is a device that calculates the optimal route to the selected parking lot and provides guidance to the user in real time.

[1761] The present invention uses hardware and software to realize the following system configuration and processing flow: This system provides optimal parking lot selection and navigation specifically for autonomous vehicles.

[1762] The server drives various modules to achieve the following main functions:

[1763] 1. Video analysis methods

[1764] The server receives video data in real time from cameras installed inside the autonomous vehicles. The video analytics module uses image processing libraries such as OpenCV to analyze the video data and determine the availability of parking spaces. This identifies vacant and occupied parking spaces and stores this information in a database.

[1765] 2. Location information acquisition means

[1766] The server uses the GPS module to obtain the vehicle's current location information, which then transmits the user's location information to the server in real time.

[1767] 3. Behavioral pattern analysis methods

[1768] The server uses a behavioral pattern analysis module to analyze the user's behavioral patterns based on their past parking history and schedule information, which then predicts their next parking needs and stores future parking patterns in a database.

[1769] 4. Emotion analysis method

[1770] The server uses a camera and microphone to analyze the user's facial expressions and tone of voice. It uses an emotion analysis engine (such as IBM Watson Tone Analyzer) to recognize the user's emotions in real time. It then uses facial expression analysis and voice analysis technologies to quantify the user's emotional state.

[1771] 5. Means of providing parking information

[1772] The server calculates the optimal parking information and fees for the user based on information obtained from video analysis, location information acquisition, behavioral pattern analysis, and emotion analysis.The optimal fee is calculated dynamically, taking into account the location and usage conditions of the parking lot, as well as the user's emotions identified by the emotion engine.

[1773] 6. Means of notification

[1774] The server displays the best parking information and prices on the vehicle's display, providing real-time notifications and, if the driver is driving, providing information via the voice guidance system, allowing the driver to get the information they need without having to look away.

[1775] 7. Navigation Methods

[1776] The server automatically calculates the route to the selected parking lot and provides real-time navigation, linking with the vehicle's navigation system to provide optimal route guidance.

[1777] This will enable quick and accurate understanding of parking availability, parking lot selection that takes into account the user's emotional state, and appropriate navigation in real time.

[1778] Example: A vehicle camera detects available parking spaces in real time, and the user's current location is identified by GPS. Based on past parking patterns, frequently used parking areas are prioritized. Furthermore, if the user is feeling stressed, emotion analysis can be used to suggest a suitable parking space for the user.

[1779] Example prompts for generative AI models

[1780] Design a system for autonomous vehicles that analyzes parking availability in real time, suggests the best parking spot and its price based on the user's location, behavioral patterns, and emotions, and then navigates to the parking spot.

[1781] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1782] Step 1:

[1783] The server receives video data in real time from cameras installed inside the vehicles. The video data acquired from the cameras is used as input and analyzed using image processing libraries such as OpenCV. After analyzing the video, vacant and occupied parking spaces are identified and the information is stored in a database. This allows the parking lot availability to be updated in real time.

[1784] Input: Video data from the camera

[1785] Data processing: Video analysis using OpenCV

[1786] Output: Information about identified vacant parking spaces

[1787] Step 2:

[1788] The server obtains the current location information from the GPS module installed in the vehicle. It receives the current location information as input and sends it to the server. The location information is stored in a database and used for subsequent processing.

[1789] Input: Location information from GPS module

[1790] Data processing: Acquiring and storing location information

[1791] Output: Current location information stored in a database

[1792] Step 3:

[1793] The server analyzes behavioral patterns based on past parking history and schedule information. It obtains past history and schedule information from the database and uses this as input. It uses an analysis module such as Python to predict next parking needs and saves the analysis results in the database.

[1794] Input: Past parking history and schedule information

[1795] Data processing: Behavioral pattern analysis

[1796] Output: Analysis results stored in a database

[1797] Step 4:

[1798] The server uses a camera and microphone to collect the user's facial expressions and tone of voice. This data is used as input to analyze emotions in real time using an emotion analysis engine (such as IBM Watson Tone Analyzer). The analysis results are stored in a database.

[1799] Input: facial and voice data from camera and microphone

[1800] Data processing: Analysis using a sentiment analysis engine

[1801] Output: Sentiment analysis results stored in a database

[1802] Step 5:

[1803] The server integrates information obtained from the video analysis means, location information acquisition means, behavioral pattern analysis means, and emotion analysis means. Using this data as input, it calculates the optimal parking information and fees for the user. Based on the parking information and user information in the database, the fee optimization module dynamically calculates the optimal fee.

[1804] Input: Results of video analysis, location information, behavioral pattern analysis, and emotion analysis

[1805] Data processing: Integration of various information and calculation of optimal pricing

[1806] Output: Optimal parking information and prices

[1807] Step 6:

[1808] The server then notifies the vehicle's display and voice guidance system of the calculated optimal parking information and fees, allowing users to receive the necessary information visually and audibly.

[1809] Input: Best parking information and prices

[1810] Data processing: Creating notification messages

[1811] Output: Notification to vehicle display and voice guidance system

[1812] Step 7:

[1813] The server automatically calculates the optimal route to the selected parking lot and provides real-time navigation, linking with the vehicle's navigation system to provide directions to help users smoothly reach their destination.

[1814] Input: Selected parking information

[1815] Data processing: Calculation of optimal route using route calculation algorithm

[1816] Output: Instructions to the vehicle's navigation system

[1817] 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.

[1818] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1819] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1820] 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.

[1821] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1822] 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.

[1823] 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).

[1824] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1825] 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."

[1826] 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.

[1827] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1828] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1829] 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.

[1830] 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.

[1831] 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.

[1832] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1833] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1834] 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.

[1835] 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.

[1836] 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.

[1837] 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.

[1838] The following is further disclosed regarding the above embodiment.

[1839] (Claim 1)

[1840] A video analysis means for analyzing parking lot availability;

[1841] location information acquisition means for acquiring location information and behavior patterns of a user;

[1842] a parking lot information providing means for proposing optimal parking lot information and fees to a user based on the information obtained from the analyzing means and the location information acquiring means;

[1843] a notification means for notifying a user's terminal of information from the parking lot information providing means;

[1844] a navigation means for navigating to the selected parking lot based on the information from the notification means;

[1845] A system including:

[1846] (Claim 2)

[1847] 2. The system according to claim 1, wherein the video analysis means analyzes the availability of parking spaces in real time using video data acquired from a security camera.

[1848] (Claim 3)

[1849] 2. The system according to claim 1, wherein the parking lot information providing means analyzes the user's behavioral patterns based on the user's past parking history and schedule information, and predicts the user's next parking needs.

[1850] "Example 1"

[1851] (Claim 1)

[1852] A video analysis means for analyzing parking lot availability;

[1853] location information acquisition means for acquiring location information and behavior patterns of a user;

[1854] a parking lot information providing means for proposing optimal parking lot information and fees to a user based on the information obtained from the analyzing means and the location information acquiring means;

[1855] a notification means for notifying a user's terminal of information from the parking lot information providing means;

[1856] a navigation means for navigating to the selected parking lot based on the information from the notification means;

[1857] a server including a video analytics algorithm that uses a video analytics module to determine parking space availability;

[1858] a server including a behavior pattern analysis module that analyzes behavior patterns based on a user's past parking history and schedule information;

[1859] A terminal equipped with a voice guidance function that provides parking lot information by voice guidance while driving a vehicle;

[1860] a server having a navigation function for providing real-time navigation information;

[1861] A system including:

[1862] (Claim 2)

[1863] 2. The system according to claim 1, further comprising a video analysis means for analyzing the availability of parking spaces in real time using video data acquired from a security camera.

[1864] (Claim 3)

[1865] 2. The system according to claim 1, further comprising a behavior pattern analysis module that analyzes a user's behavior pattern based on the user's past parking history and schedule information, and predicts the user's next parking needs.

[1866] "Application Example 1"

[1867] (Claim 1)

[1868] A video analysis means for analyzing parking lot availability;

[1869] location information acquisition means for acquiring location information and behavior patterns of a user;

[1870] a parking lot information providing means for proposing optimal parking lot information and fees to a user based on the information obtained from the analyzing means and the location information acquiring means;

[1871] a notification means for notifying a user's terminal of information from the parking lot information providing means;

[1872] a navigation means for navigating the autonomous vehicle to the selected parking lot based on information from the notification means;

[1873] A system including:

[1874] (Claim 2)

[1875] 2. The system according to claim 1, wherein the video analysis means analyzes the availability of parking spaces in real time using video data acquired from a surveillance camera.

[1876] (Claim 3)

[1877] 2. The system according to claim 1, wherein the parking lot information providing means analyzes the user's behavioral patterns based on the user's past parking history and schedule information, and predicts the user's next parking needs.

[1878] "Example 2: Combining Emotion Engines"

[1879] (Claim 1)

[1880] A video analysis means for analyzing parking lot availability;

[1881] location information acquisition means for acquiring location information and behavior patterns of a user;

[1882] a parking lot information providing means for proposing optimal parking lot information and fees to a user based on the information obtained from the analyzing means and the location information acquiring means;

[1883] a notification means for notifying a user's terminal of information from the parking lot information providing means;

[1884] a navigation means for navigating to the selected parking lot based on the information from the notification means;

[1885] An emotion engine that recognizes the user's emotions;

[1886] A system including:

[1887] (Claim 2)

[1888] 2. The system according to claim 1, wherein the video analysis means analyzes the availability of parking spaces in real time using video data acquired from a security camera.

[1889] (Claim 3)

[1890] 2. The system according to claim 1, wherein the parking lot information providing means analyzes the user's behavioral patterns based on the user's past parking history and schedule information, and predicts the user's next parking needs.

[1891] (Claim 4)

[1892] 2. The system according to claim 1, wherein the emotion engine recognizes emotions by analyzing the user's facial expressions and tone of voice.

[1893] "Application example 2 when combining emotion engines"

[1894] (Claim 1)

[1895] A video analysis means for analyzing parking lot availability;

[1896] location information acquisition means for acquiring location information and behavior patterns of a user;

[1897] a parking lot information providing means for proposing optimal parking lot information and fees to a user based on the information obtained from the analyzing means and the location information acquiring means;

[1898] a notification means for notif...

Claims

1. A video analysis means for analyzing parking lot availability; location information acquisition means for acquiring location information and behavior patterns of a user; a parking lot information providing means for proposing optimal parking lot information and fees to a user based on the information obtained from the analyzing means and the location information acquiring means; a notification means for notifying a user's terminal of information from the parking lot information providing means; a navigation means for navigating to the selected parking lot based on the information from the notification means; A system including:

2. 2. The system according to claim 1, wherein the video analysis means analyzes the availability of parking spaces in real time using video data acquired from a security camera.

3. 2. The system according to claim 1, wherein said parking lot information providing means analyzes a user's behavioral pattern based on the user's past parking history and schedule information, and predicts the user's next parking needs.

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